Solu Manual For MSA Johnson
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Preface
This solution manual was prepared as an aid for instrctors who wil benefit by
having solutions available. In addition to providing detailed answers to most of the
problems in the book, this manual can help the instrctor determne which of the
problems are most appropriate for the class.
The vast majority of the problems have been solved with the help of available
the problems have been solved with
software (SAS, S~Plus, Minitab). A few of
computer
hand calculators. The reader should keep in mind that round-off errors can occurparcularly in those problems involving long chains of arthmetic calculations.
We would like to take this opportnity to acknowledge the contrbution of many
students, whose homework formd the basis for many of the solutions. In paricular, we
would like to thank Jorge Achcar, Sebastiao Amorim, W. K. Cheang, S. S. Cho, S. G.
Chow, Charles Fleming, Stu Janis, Richard Jones, Tim Kramer, Dennis Murphy, Rich
Raubertas, David Steinberg, T. J. Tien, Steve Verril, Paul Whitney and Mike Wincek.
Dianne Hall compiled most of the material needed to make this current solutions manual
consistent with the sixth edition of the book.
The solutions are numbered in the same manner as the exercises in the book.
Thus, for example, 9.6 refers to the 6th exercise of chapter 9.
We hope this manual is a useful aid for adopters of our Applied Multivariate
Statistical Analysis, 6th edition, text. The authors have taken a litte more active role in
the preparation of the current solutions manual. However, it is inevitable that an error or
two has slipped through so please bring remaining errors to our attention. Also,
comments and suggestions are always welcome.
Richard A. Johnson
Dean W. Wichern
.ì
Chapter 1
1.1
Xl =" 4.29
X2 = 15.29
51i = 4.20
522 = 3.56
S12 = 3.70
1.2 a)
Scatter Plot and Marginal Dot Plots
.
.
.
.
.
.
.
.
.
.
.
.
17.5
.
15.0
I'
.
.
.
.
.
12~5
.
.
.
)C
10.0
.
7.5
.
.
.
.
.
.
.
5.0
0
4
2
6
10
8
12
xl
b) SlZ is negative
c)
Xi =5.20 x2 = 12.48 sii = 3.09 S22 = 5.27
SI2 = -15.94 'i2 = -.98
Large Xl occurs with small Xz and vice versa.
d)
x = 12.48
(5.20 )
Sn --
-15.94)
-15.94
( 3.09
5.27
R =(
1 -.98)
-.98 1
.
2
1.3
SnJ6 : -~::J
x =
-
-. 40~
UJ
R =
. 3~OJ
.(1
(synet:;
.577c )
L (synetric) 2 .
1.4 a) There isa positive correlation between Xl and Xi. Since sample size is
small, hard to be definitive about nature of marginal distributions.
However, marginal distribution of Xi appears to be skewed to the right. .
The marginal distribution of Xi seems reasonably symmetrc.
.....'....._.,..'....,...,..'.":
SCëtter.PJot andMarginaldøøt:~llôt!;
. .
. . .
.
25
20
.
I'
)C
15
.
.
50
.
.
.
.
..
.
.
10
.
. .
.
.
.
.
.
.
.
.
.
100
;..
150
xl
200
250
300
b)
Xi = 155.60 x2 = 14.70 sii = 82.03 S22 = 4.85
SI2 = 273.26 'i2 = .69
Large profits (X2) tend to be associated with large sales (Xi); small profits
with small sales.
3
1.5 a) There is negative correlation between X2 and X3 and negative correlation
between Xl and X3. The marginal distribution of Xi appears to be skewed to
the right. The marginal distribution of X2 seems reasonably symmetric.
The marginal distribution of X3 also appears to be skewed to the right.
Sêåttêr'Plotäl'(i'Marginal.DotPiØ_:i..~sxli.
.
.
. .
.
.
. .
1600
)C
.
.
.
1200
M
.
.
.
.
.
. .
.
.
.
.800
.
400
.
.
.
.
0
25
20
15
10
. . . .
x2
. '-'
. .Scatiêr;Plötànd:Marginal.alÎ.'llÎi:lîtfjtì.I...
. .
. . .
1600
)C
.
.
. .
.
.
1200
M
.
..
.
.
800
.
400
.
.
0
50
100
150
xl
200
250
.
300
.
.
.
.
.
.
. . .
4
1.5 b)
273.26
Sn = 273.26
(- 32018.36
82.03
x = 14.70
710.91
(155.60J
1
-.85
1.6
- 948.45
461.90
-.85)
-.42
.69
R = ( ~69
4.85
-32018.36)
-948.45
-.42
1
a) Hi stograms
Xs
Xi
NUMBER OF
HIDDLE OF
HIDIILE OF
INTERVAL
co
OBSERVATioNS
*****
5
INTERVAL
5.
6.
7.
8.
9.
6.
********
S
******
X2
NUHBER OF
OBSERVATIONS
1
*
HIDDLE OF
INTERVAL
30.
40.
50.
60.
70.
80.
90.
J
2
3
10
12
a
100.
110.
2
1
s
9.
10.
11.
12.
13.
14.
15.
16.
17.
un*
6
4
4
**********
************
********
0
2.
4.
6.
1*
OBSERVA T I'ONS
1.
.... .
:3 .
4.
s.
NUMBER OF.
OBSERVA T IONS
1 J ***$*********
15 ***************
a ********
5
1 ui**
*
J ****
***
4
7. *******
5 n***
2
2 **
u
1*
2
1 **
*
20.
22.
24.
26.
X4
NUltEiER OF
OI4SERVATIOllS
7 *******
B ********
10.
12.
14.
16.
18.
NUHBER OF
S u***
INTERVAL
*
1
I' I DOLE .OF
19 *******************
9 *********
.3 U*
HIDDLE OF
*
1
Xl
:s *****
4.
5.
6.
7.
*
0
a.
J.
*
1
n
*
X3
2.
****
1
0
INTE"RVAL
HIDIILE OF
INTERVAL
4
0
19.
20.
21.
**
***
u**
Uu.*
.S
LS.
n*
*****
*****
******
****
S
a.
***********
11
5
6
U*
J
7.
u*****
7
10.
oJ .
NUMB£R. OF
08SERVATIONS
2
**
o
o
X7
HIDDLE OF
INTERVAL
2.
J.
4.
s.
NUMBER OF
OBSERVATIONS
7 *******
9 *********
1*
25 *************************
5
1.6
2.440
7.5
b)
293.360
73 .857
-
x
-2 . 714
4.548
=
2. 191
-.369
3.816
1.486
S =
-.452
- . 571
-2.1 79
.,.67
-1 .354
30.058
2.755
:609
.658
6.602
2.260
1 . 154
1 . 062
-.7-91
.172
11. 093
3.052
1 .019
30.241
.580
n
1 0 . 048
9.405
3.095
.138
.467
(syrtric)
The pair x3' x4 exhibits a small to moderate positive correlation and so does the
pair x3' xs' Most of the entries are small.
1.7
ill
b)
3
x2
.
4
4
..
.
2
.
Xl
2
2 4
Scatter.p1'Ot
(vari ab 1 e space)
~ ~tem space.)
1
-6
1.8 Using (1-12) d(P,Q) = 1(-1-1 )2+(_1_0)2; = /5 = 2.236
Using (1-20) d(P.Q)' /~H-1 )'+2(l)(-1-1 )(-1-0) '2t(-~0);' =j~~ = 1.38S
Using (1-20) the locus of points a c~nstant squared distance 1 from Q = (1,0)
is given by the expression t(xi-n2+ ~ (x1-1 )x2 + 2t x~ = 1. To sketch the
locus of points defined by this equation, we first obtain the coordinates of
some points sati sfyi ng the equation:
(-1,1.5), (0,-1.5), (0,3), (1,-2.6), (1,2.6), (2,-3), (2,1.5), (3,-1.5)
The resulting ellipse is:
X1
1.9
a) sl1 = 20.48
s 12 = 9.09
s 22 = 6. 19
X2
.
5
.
.
.
0
.
-"5
.
.
.
5
xi
10
7
1.10 a) This equation is of the fonn (1-19) with aii = 1, a12 = ~. and aZ2 = 4.
Therefore this is a
distance for correlated variables if it is non-negative
easily if we write
for all values of xl' xz' But this follows
2. 2. 1 1 15 2.
xl + 4xZ + x1x2 = (xl + r'2) + T x2 ,?o.
b) In order for this expression to be a distance it has to be non-negative for
2. :¿
all values xl' xz' Since, for (xl ,x2) = (0,1) we have xl-2xZ = -Z, we
conclude that this is not a validdistan~e function.
1.11
d(P,Q) = 14(X,-Yi)4 + Z(-l )(x1-Yl )(x2-YZ) + (x2-Y2):¿'
= 14(Y1-xi):¿ + 2(-i)(yi-x,)(yZ-x2) + (xz-Yz):¿' = d(Q,P)
Next, 4(x,-yi)2. - 2(xi-y,)(x2-y2) + (x2-YZ): =
=,(x1-Yfx2+Y2):1 + 3(Xi-Yi):1,?0 so d(P,Q) ~O.
The s€cond term is zero in this last ex.pr.essi'on only if xl = Y1 and
then the first is
zero only if x.2 = YZ.
8
1.12 a)
If P = (-3,4) then d(Q,P) =max (1-31,141) = 4
b) The locus of points whosesquar~d distance from (n,O) is , is
X2
.1
1
..
1
-1
7
x,
-1
c) The generalization to p-dimensions is given by d(Q,P) = max(lx,I,lx21,...,lxpl)'
1.13 Place the faci'ity at C-3.
9
1.14 a)
360.+ )(4
.
320.+
.
.
280.+
.
.
....
. .
240.+
200.+
. ...
.
. .
.
..
.
.. .
.*
I:
I:
160.+
+______+_____+-------------+------~.. )(2
130. 1:5:5. 180. 20:5.' 230. 2:5:5.
Strong positive correlation. No obvious "unusual" observations.
b) Mul tipl e-scl eros; s group.
42 . 07
179.64
x =
12.31
236.62
13.16
116.91
Sn
61 .78
-20.10
61 . 1 3
-27 . 65
812.72
-218.35
865.32
3 as . 94
221 '. 93
90.48
286.60
82.53
=
1146.38
(synetric)
337.80
10
.200
1
1
R
-. H)6
.167
-.139
.438
.896
.173
1
.375
.892
.133
=
1
( synetrit: )
1
Non multiple-sclerosis group.
37 . 99
147.21
i = 1 .56
1 95.57
1.62
5.28
1.84
1.78
273.61 95.08
11 0.13
sn =
1 01 . 67
3.2u
1 03 .28
2.15
2.22
.49
2.35
2.32
183 . 04 .
(syietric)
1
.548
1
R
=
(symmetric)
.239
.132
.454
.727
.127
.134
1
.123
.244
1
.114
1
11
1.15
a) Scatterplot of x2 and x3.
., ..".. ... . . . ., .. .0 .
. . . . . . + . . . . l . . . . + . . . . + . . . . . . . . . +. . . . . . . . . + . . .. .... + . . . . . . . . . + . .. . . . . . . . . .
.
.l -
.
.
I
.
.
1
.
3. it
.
.
.
J
-
.
.
1.2
.
t
1
1
.,
.
I
1
--
.
~
.
1
.
1
t
.
"'. .
1
-
E
E
.
1
1
I
--
~
.
.
.
t
I
:
.
I
-.- '_ 1
X:i
.
~
I
I
+.
1
.
2
.
1
1
III
2. cl
.
.
1
I
1
.
.
1
I
. .
.z.o
.1
3
.
.
1
I
.
+
t
1
.
.
.
.
I
1
.
\1
.
1
t
I
i
.
I
1
\1
.
.
1
1
.
1
.
i
J .2
I
t
1
.
l
1
.
.
1
.
J
1I
2
1
.
1
.80
..
I
. . .. .. . . . .
. 75f)
. .
1
.
.. . .. .
1.25
1.88
t.~~
.
.. .
1.75
.. '. ...
t. ~ e
.. . . ..
2.25
... ...-
2.7'5 3.25
Z "~A
ACTIVITY X%
b)
3.54
1.81
x =
2.14
~.21
2.58
1.27
.. .
3.P,1) 3.S11
.
3.75 G.25
".--. . .
1I.llfl
12
4.61
1.15
Sn
..92
.58
.27
.61
.11
.12
.57
.09
1.~6
.39
.34
.11
.21
=
.85
;.
(synetric)
1
.551
1
.362
.187
1
R
=
.386
.455
.346
1
(syretric)
.537
.15
-.02
.11
.02
-.01
.85
. 077 '
.535
.496
.704
-.035
1
-. 01 0
.156
.071
1
The largest correlation is between appetite and amount of food eaten.
Both activity and appetite have moderate positive correlations with
symptoms. A1 so, appetite and activity have a moderate positive
correl a tion.
13
1.16
There are signficant positive correlations among al variable. The lowest correlation is
. 0.4420 between Dominant humeru and Ulna, and the highest corr.eation is 0.89365 bewteen
Dominant hemero and Hemeru.
0.8438
0.8183
1.00000 0.85181 0.69146 0.66826 0.74369 0.67789
0.85181 1.00000 0.61192 0.74909 0.74218 0.80980
1. 7927
1. 7348
, R = 0.66826 0.74909 0.89365 1.00000 0.ti2555 0.61882
0.7044
0.6938
0.74369 0.74218 0.55222 0.62555 1.00000 0.72889
0.67789 0.80980 0.44020 0.61882 0.72889 1.00000
x-
0.69146 0.61192 1.00000 -0.89365 0.55222 0.4420
0.0124815 0.0099633 0.0214560 0.0192822 0.0087559 0.0076395
0.0099633 0.0109612 0.0177938 0.0202555 0.0081886 0.0085522
0.02145tiO
Sn -
0.0192822
0.0087559
0.0076395
0.0177938
0.0202555
0.0081886
0.0085522
0.0771429
0.0641052
0.0161635
0.0123332
0.0641052
0.0667051
0.0170261
0.0161219
0.0161635
0.0170261
0.0111057
0.0077483
0.0123332
0.0161219
0.0077483
0.0101752
1.17
There are large positive correlations among all variables. Paricularly large
correlations occur between running events that are "similar", for example,
the 1 OOm and 200m dashes, and the 1500m and 3000m runs.
11.36
.152
.338 .875
.027
.082
.230
4.254
23.12
.338
.847 2.152
.065
.199
.544
10.193
51.99
.875
2.152 6.621
.178
.500
1.400
.027
.065 .178
.007
.021
. .060
.082
.199 .500
.021
.073
.212
.230
.544 1.400
.060
.212
.652
28.368
1.197
3.474
10.508
265.265
x = 2.02
4.19
9.08
153.62
So=
4.254 10.193 28.368 1.197
1.000 .941.871 .809 .782 .728 .669
.941 1.000 .909 .820 .801 .732 .680
.871 .909 1.000 .806 .720 .674 .677
R = .809 .820 .806 1.000 .905 .867 .854
.782 .801 .720. .905 1.000 .973 .791
.728 .732 .674 .867 .973 1.000 .799
.669 .680 .677 .854 .791 .799 1.000
3.474 10.508
14
1.18
There are positive correlations among all variables. Notice the correlations
decrease as the distances between pairs of running events increase (see the first
column of the correlation matrx R). The correlation matrix for running events
measured in meters per second is very similar to the correlation matrix for the
running event times given in Exercise 1.17.
8.81
.091 .096 .097 .065 .082 .092 .081
8.66
.096 .115 .114 .075 .096 .105 .093
7.71
.097 .114 .138 .081 .095 .108 .102
x = 6.60
Sn = .065 .075 .081 .074 .086 .100 .094
5.99
.082 .096 .095 .086 .124 .144 .118
5.54
4.62
.092 .105 .108 .100 .144 .177 .147
.081 .093 .102 .094 .118 .147 .167
1.000 .938 .866 .797 .776 .729 .660
.938 1.000 .906 .816 .806 .741 .675
.866 .906 1.000 .804 .731 .694 .672
R = .797 .816 .804 1.000 .906 .875 .852
.776 .806 .731 .906 1.000 .972 .824
.729 .741 .694 .875 .972 1.000 .854
.660 .675 .672 .852 .824 .854 1.000
15
1.19
(a)
o _R A 0 IUS
RADIUS
LHUI.ERUS
tlUME~US
ILULNA
ULNA
c-
..
I
o'
'0
..
o'
:
'.
.'
.'
"
"
..
....
z-
C
...
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C
.,.
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:;
00
00.
:
o.
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-
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0'
i::
,
,
-
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....I:
II
~
"
o'
"
.0
-
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..
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..
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QI
t-
"
.~
-..I:
..C
co
..
..
'"
....C
co
co
..-
UI
..
....
....I:
~
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t-
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CI
-
.QI
.0
-
QI
CI
".
i
::
;:
o
c:
en
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=
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c:
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c:
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CD
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16
1.19
(b)
~.
.c
. P.
~.
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i:_...to
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17
1.20
Xl
(a)
.
. ..
..
.
..
'.
. ..
..
(b) ,,' A L_-l_ X
. ..
...
.. .
. .
.
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L _ _ (,"" i. \ l'ø.l l
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.ö .
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.
(a1 The plot looks like a cigar shape, but bent. Some observations. in the lower left hand
part could be outliers. From the highlighted plot in (b) (actually non-bankrupt group
not highlighted), there is one outlier in the nonbankruptgroup, which is apparently
located in the bankrupt group, besides the strung out pattern to the right.
(ll) The dotted line in the plot would be an orientation for the classificà.tion.
18
1.21
o
(a)
o
(b)
Outlier
Outlier
~~ô
0'"
.
. ...
.
X1
... .
..
. ... .
...... .
.
.
..
.
.. .
X1
.
.
.
.
. .. . .
...... .
. ..
. .. ... ....
.. ..
G~~
.
.
..-....l.~...
.
.
...
.
.
.
..
.
.
X3
.
tfe'
Ó,e
Outlier Q
(a) There are two outliers in the upper right and lower right corners of the plot.
(b) Only the points in the gasoline group are highlighted. The observation in the upper
right is the outlier. As indiCated in the plot, there is an orientation to classify into two
groups.
19
1.22
possible outliers are indicated.
G
Outlier
~ø
~~
~ø.
t I ~~\e
X1
.
.
.
.. .
.. .
.. . .
. . . . .
fi
.
/ \, X1
Xz/ ·
.
..
. ..
.
.
.
.
..
x,
.
..
Xz
.."
..
.
. . ...
.
.. . . .
. .... .
.
. .
. ...,.
.
x,
..
.. .
.
.
.
fi
..
.
~~e
..
Xz
.
~,et
ot)~
...
)l"
;I
./
.
./
./
.. .~
..
... ./ /
././
..
· ),. il
./ ~e~~\.e
.
..
..
..s..
Outliers
~
VI
Q.
.Q
-0
VI
CI
...
s.
U
..u
VI
C
s.
0
IØ
U
s.
c:
fa
s.
V)
oi
=
V)
s.
II
C'
Cd
.::
oi
::
V)
s.
ci
V)
Co
=
V)
i.
s.
~ci
::
iu
VI
II
ai
ci
~VI::
..a:u
4;
. ..
..
z:
Cd
CJ
I)
.,.
c:
.
~
i-aci
.Q
c:
ci
~
s.
s.
ci
VI
en
::
a.
~
C"
~
::
iU
~ci
~
~s.
~VIa.
--i..
ci
to
::
iCo
.-
-u
Cd
ci
Q.
VI
CI
VI
ci
u
..
..I¡
Cd
a
c:
s.
ci
.c
u
N
oi
c:
-=
s.
~ciVI
en ::
..c:s. iu
ci
~VI i-U::
iVI
-~ .. -u::
cc:
s.
ci
..c;I
-a:
ra
::
VI
s.
c(
.G
M
N.
'"
+J
VI
ra
ci
.c
::
en
i-
Cd
ci
VI
..~a:
i-n:
+J
l-0
..u
::
"'
e
a.
20
21
Cl uster 1
1.24
20
10
13
4
C1 uster 2
3
9
14
19
18
C1 uster 3
22
.s
1
22
Clust~r 4
16
8
11
Cl uster 5
21
5
Cluster 6
17
12
2
C1 uster 7
We have cluster~d these faces
in. the same manner as those in
Example 1.12. Note, however,
other groupings are~qually .
plausible. for instance, utilities
9 and 18 l1ight be swit.ched from
7.
'5
Cluster 2 toC1 uster 3 and so
forth.
23
1.25
We illustrate one cluster of "stars.l. The
shown) can be gr~uped in 3 or 4 additional
r.emai ni ng
stars
cl usters.
....
10
4
-.
'.
-.
."-,,.
/ ....1
¡.. ..l ".......;-
....'." .¡..~.
'/
....~." I:
. ...0: .. .":
..
.i ....
-,'-1
.....: l. f
~
20
",. -.
13
'-a.-
(not
24
1.26 Bull data
R
(a) XBAR
Breed SalePr YrHgt FtFrBody PrctFFB Frame BkFat SaleHt SaleWt
4. 3816
1742.4342
50.5224
995.9474
70.8816
6.3158
0.1967
1.000 -0.224 0.525 0.409 0.472 0.434 -0.~15 0.487 0.116
-0.224 1.000 0.423 0.102 -0.113 0.479 0.277 0.390 0.317
o . 525 0 .423 1 .000 O. 624 0 . 523 0 . 940 -0.344 0 . 860 0 . 368
0.409 0.102 0..624 1.000 0.691 0.605 -0.168 0.699 0.5££
0.472 -0.113 0.523 0.691 1.000 0.482 -0.488 0.521 0.198
0.434 0.479 0.940 0.605 0.482 1.000 -0.260 0.801 0.368
-0.615 0.277 -0.344 -0.168 -0.488 -0.260 1.QOO ~0.282 0.208
£4. 1263
0.487 0.390 0.860 0.699 0.521 0.801 -0.282 1.00 0.~66
1555.2895
0.116 0.317 0.368 0.555 0.198 0.368 0.208 0.566 1.000
Sn
SalePr
-429.02
Breed
YrHgt FtFrBody PrctFFB
Frame
BkFat
2.79 116.28
1.23 -0.17
4;73
-429 ..02 383026.64 450.47 5813.09 -226.46 272.78 15.24
450. 47
2.96
2.79
98.81
2.92
1.49 -0.05
5813.09 98.81 8481. 26 206 . 75 51. 27 -1.38
116.28
-226.46
2.92 206. 75 10.55
4.73
1.44 -0.14
272.78
1.49
51.27
1.44
1.23
0.85 -0.02
15.24 -0.05
-0. 17
-1. 38
-0.14 -0.02 0.01
480 . 56
2.94 128.23
3.00
3.37
1.47 -0.05
46.32 25308.44 81.72 6592.41 82.82 43.74 2.38
9.55
SaleHt
3.00
480.56
2.94
128.23
3.37
1.47
'SaleWt
46.32
25308 . 44
81. 72
6592.41
82.82
43.74
2.38
145.35
16628.94
-0.05
3.97
145 . 35
90 1100 t30
5.0 6.0 7.0 8.0
.
.
. . . .
.,
CD
CD
.
Breed
.
.
. . . .
.
Breed
..
N
'"
.
cci .
a.. .
.
.
.
.
..
. . . . . .
. .
.
.
~
.
. . . . .
.
.
8
--¡.:
Frame
:i .
~ .
.
.
. . . . . . .
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
. .
. .
.
.
.
2 4 6 8
§!
. . . .
.
.
~
.
~
.
.
.
.
.
FtFrB
I
,
.
.
.
.
.
.
BkFat
.
'"
d
'"
d
.
.
d
O. t 0.2 0.3 0.4 0.5
.
. ... .
I..,.,;....
'.
..~'.l . .
.
. .., .1, .
. . .
. - . -:-
.I ..-.. .
. ....
l. \..i'..
..,..
-.-'..
o
CD
..
CD
CD
on
SaleHt
l: -:,: .
. ,.
:;
~
g
2 4 6 8
50 52 54 1i 58 60
25
1.27
(a) Correlation r = .173
Scatterplot of Size Y5 viSitors
2500
.
2000
.
1500
iI
üi
.
.1000
.
.
Gæct '5lio\£~
"' .
500
..
. .
0
0
1
. .
.
. .
2
3
4
5
6
7
8
9
Visitors
(b) Great Smoky is unusual park. Correlation with this park removed is r = .391.
This single point has reasonably large effect on correlation reducing the positive
correlation by more than half when added to the national park data set.
(c) The correlation coefficient is a dimensionless measure of association. The
correlation in (b) would not change if size were measured in square miles
instead of acres.
26
Chapter 2
2.1
a)
;
...
---.
I,
i
;
:
i
,
,
:
... ------
II
i
i
.
I
.
i
, .
~
,
I
!
.
/l
oJ
:
:
,
r
I
,
-A : t- ~ ..1'-
, ø ':, . - ,i :=-: -j.1 3 d~j =
,:':
,,,~.;
. .,;'
.
,.. .:~..,:/
_~,__
"
,1
'
,1
..
.
.
.
/"
1-.
: '7
~~
/.
-~'"'
K
(-~
'
"'7' .! . _ ... i I .
~ =, -ii;;;;;.I~ -.A
: 'JO " : i i ; , , ,
l-' : ; '.,;
../ :
'-'
,
ILl
/
i I;
:I Ii I i
-tI ! ii i
7.
./
I
;
~
.
,
,
,
,
I
!
! i/'i : g
¡
,
.
:
;
;
¡
i
~. .
:
,
.
i
:
I
i
.
, 'i;
i ,,/_.
.
!
"
I
I
,
I
,
I ,
i
;
I
i
I I
I
,
,
I
,
:
I
,
i !!
I i : I
.
,
,
,
i
!
: :
1
I
I
I
,
./
== i
.
i I
J
:
.
,t
I
b)
i)
il)
Lx =
RX
cas(e)
=
..
x.y
=
Iß
=
5.9l'i
1
=
=
19.621
LxLy
.051
- -
e
; 1 i)
;, 870
= arc cos ( .051 )
proJection of
L
on
x
;s
lt~i
is1is
x =
i
3š
x
= 7~35'35
(1 1 31'
c)
':
: i
, :
! I
I .
. i
, I
;-.
i
i : l :
=i
i
I 1
I i
02-2:
. I
!- .
::t
i
i I
,
i'
i,
:i:-'.
i
.1 ..
:i
'i
i
I
.1
i.
i
:3
.
1
~
I
~
;
"T
~-:~~-';'-i-'
.
~._~-:.i" 1 .'
:.
. :. -"-- --_..-- ---
I
27
15)
2.2
a)
b)
SA =
20
-6
r-6
1a
( -~
-q
SA = - ~
-9
c)
d)
=
AIBI
-1
(-1 :
e)
No.
a)
AI
b)
C'
c
i
so
3.
. (~
(A I) = A' = A
(C'f"l~
:l
.(:
1a
-ìa
lõ,i
31
-1 =
J)
10
c)
8
' 10
=
4
(1:
AB
=
n
(i
has
-Tõ
7)'
(AB) , =
d)
il). (t''-'
(C' J' 'l- 1~
iÕ 4
-iÕJ
i2
B'A'
(12, -7)
-: )
1). A
2.3
=
C'B
U ':11 )
11
(~
~)
-
=
(~
(AB) i
':)
11
(i ~j )th entry
k
a,.
= i aitb1j
1 =Ja"b1,
1 +Ja'2b2'
1 J+...+
1 a,,,b,,,
J R.=1
Consequently,
(AB) i
has
entry.
('1 ~J',)th
k
c,, =
Jl
Next ßI
I
ajR,b1i ,
1=1
has
.th row (b, i ,b2i ~'" IbkiJ
1
and
A'
lias. jth
28
column (aji,aj2"",ajk)1 so SIAl has ~i~j)th entry
k
bliaji+b2ibj2+...+bk1~jk = t~l ajtb1i = cji
51 nce i and j were arbi trary choices ~ (AB) i = B i A I .
2.4
a)
I = II and AA-l = I = A-1A.
and 1= (A-1A)' = A1(A-l)l.
Thus I i = I = (AA - ~ ) I = (A-l)' A,I
Consequently, (A-l)1 is the inverse
of Al or (AI r' = (A-l)'.
(f1A)B - B-1S' I so AS has inverse (AS)-1 ·
I
bl (S-lA-l)AS _ B-1
B-1 A- i. It was suff1 ci ent to check for a 1 eft inverse but we may
also verify AB(B-1A-l) =.A(~Bi~)A-i = AA-l = I ,
¡s
2.5
IT
QQI
=
-12
13
2,6
12l r
_121 r
IT IT
13 = 1 69
5 12
i3 IT
5
i3 a
169
~1
A' is symetric.
a)
5i nce
b)
Since the quadratic form
A = AI,
= QIQ ,
1 :J .l:
9xi - 4x1 X2 + 6X2
x' Ax . (xi ,x2J ( 9
- - .. -2 -:)(::1~ (2Xi.x2)2 + 5(x;+xi) ~ 0 for tX,lx2) -l (O~O)
we conclude that A is positive definite.
2.7 a) Eigenvalues: Ål = 10, Å2 = 5 .
Nonnalized eigenvectors: ':1 = (2/15~ -1/15)= (,894~ -,447)
~2 = (1/15, 2/15) = (.447, .894)
29
b)
A' V-2
-2 ) . 1 fIlS r2/1S.
-1//5 + 5 (1/1S1 (1/IS,
9-1/~
2/~
2) . (012
c)
-1
A =
2//5
0041
1
9(6)-( -2)( -2)
(: 9 ,04
.18
d) Eigenval ues: ll = ,2, l2 = ,1
Normal;z~ eigenvectors: ;1 = (1/¡;~ 2/15J
;z =: (2/15~, -1I/5J
2.8 Ei genva1 ues: l1 = 2 ~ l2 = -3
Norma 1; zed e; genvectors: ;~ = (2/15 ~ l/~ J
=~ = (1/15. -2/15 J
2) = 2 (2//5) (2/15, 1/15J _ 3( 1/1S)(1//s' -2/151 '
A · (:
2.9
-2 1/15 -2/~
a) A-1
= 1(-2)-2(2)
1 - -1 (-2 -2) -2
=i1131
11
3 6
b) Eigenvalues: l1 = 1/2~ l2 = -1/3
Nonna1iz.ed eigenvectors: ;1 = (2/ß, l/I5J
;z = (i/ß~ -2/I5J
cJ A-l =(t
11 = 1 (2/15) (2/15, . 1//5J _ir 1/15) (1//5, -2/ß1
-1 2 1/15 3L-21 5
30
2.10
B-1- 4(4,D02001
_ 1 r 4.002001
-44,0011
)-(4,OOl)~ ~4,OOl
.
~.0011
= 333,333
-4 , 001
( 4,OÒZOCl
-: 00011
1 ( 4.002
-1
A = 4(4,002)~(4,OOl)~ -4,001
-: 00011
= -1,000,000
-4 , 001
. ( 4.002
Thus A-1 ~ (_3)B-1
with p=2,
aii- and
2.11 With p=l~ laii\ =
aii
a
a
a22
= a11a2Z - 0(0) = aiia22
Proceeding by induction~we assume the result holds for any
(p-i)x(p-l) diagonal matrix Aii' Then writing
aii
=
A
(pxp)
a
a
a
.
.
.
Aii
a
we expand IAI according to Definition 2A.24 to find
IAI = aii I
Aii
I + 0 + ,.. + o. S~nce IAnl =, a2Za33 ... ~pp
by the induction hypothesis~ IAI = al'(a2Za33.... app) =
al1a22a33 ,.. app'
31
2.12 By (2-20), A = PApl with ppi = pip = 1. From Result 2A.l1(e)
IAI = ¡pi IAI Ipil = ¡AI. Since A is a diagonal matrix wlth
p p
diagonal elements Ài,À2~...,À , we can apply Exercise 2.11 to
get I A I = I A I = n À , .
'1
1=
1
2.14 Let À be ,an eigenvalue of A, Thus a = tA-U I. If Q ,is
orthogona 1, QQ i = I and I Q II Q i I = 1 by Exerci se 2.13. . Us; ng
Result 2A.11(e) we can then write
a = I Q I I A-U I I Q i I = I QAQ i -ÀI I
and it follows that À is also an eigenvalue of QAQ' if Q is
orthogona 1 .
2.16
show; ng A i A ; s symetric.
(A i A) i = A i (A i ) I = A i A
Yl
Y = Y 2 = Ax.
p _.. .. ..
Then a s Y12+y22+ ,.. + y2 = yay = x'A1Ax
yp
and AlA is non-negative definite by definition.
2.18
Write c2 = xlAx with A = r 4 -n1. Theeigenvalue..nonnalized
- - tl2 3
eigenvector pairs for A are:
Ài = 2 ~
Å2 = 5,
'=1 = (.577 ~ ,816)
':2 = (.81 6, -, 577)
'For c2 = 1, the hal f 1 engths of the major and minor axes of the
elllpse of constant distance are
~1 12 ~ ~
~ = -i = ,707 and ~ =.. = .447
respectively, These axes 1 ie in the directions of the vectors ~1
and =2 r~spectively,
32
For c2 = 4~ th,e hal f lengths of the major and mlnor axes are
c
2
'
ñ:, .f
c _ 2 _
- = - = 1.414 and -- - -- - .894 .
ñ:2 ' IS
As c2 increases the lengths of, the major and mi~or axes ; ncrease.
2.20 Using matrx A in Exercise 2.3, we determne
Ài = , ,382, :1 = (,8507, - .5257) i
À2 = 3.6'8~ :2 = (.5257., .8507)1
We know
,325)
A '/2 = Ifl :1:1 + 1r2 :2:2
,325
__(' .376
1. 701
- .1453 J
A-1/2 = -i e el + -- e el _ ( ,7608
If, -1 -1 Ir -2 _2 ~ -,1453
We check
Al/ A-1/2 =(:
~) . A-l/2 Al/2
.6155
33
2,21 (a)
A' A = r 1 _2 2 J r ~ -~ J = r 9 1 J
l1 22 l2 2 l19
0= IA'A-A I I = (9-A)2- 1 = (lu- A)(8-A) , so Ai = 10 and A2 = 8.
Next,
U;J ¡::J
¡ i ~ J ¡:~ J
-
10 ¡:~ J
-
8 ¡:~J
gives
gives
ei - . 1/.;
- (W2J
¡ 1/.; J
e2 = -1/.;
(b)
AA'= ¡~-n U -; n = ¡n ~J
o = /AA' - AI 12-A
I - .1 0 80- À40
4 0 8-A
= (2 - A)(8 - A)2 - 42(8 - A) = (8 - A)(A -lO)A so Ai = 10, A2 = 8, and
A3 = O.
(~ ~ ~ J ¡ ~ J - 10 (~J
.gves
¡~
gives
so ei= ~(~J
4e3 - 8ei
8e2 - lOe2
0
8
0 ~J
4e3
4ei
Also, e3 = 1-2/V5,O, 1/V5 J'
-
8 (~J
¡ :: J
-
Gei
U
so e,= (!J
34
\C)
u -~ J - Vi ( l, J ( J" J, 1 + VB (! J (to, - J, I
2,22 (a)
AA' = r 4 8 8 J
l 3 6 -9
r : ~ J = r 144 -12 J
l8 -9 L -12 126
o = IAA' - À I I = (144 - À)(126 - À) - (12)2 = (150 - À)(120 - À) , so
Ài = 150 and À2 =' 120. Next,
r 144 -12) r ei J = .150 r ei J
L -12 126 L e2 le2
. r 2/.; )
gives ei = L -1/.; .
and À2 = 120 gives e2 = f1/v512/.;)'.
(b)
AI A = r: ~ J
l8 -9
r438 8J
- r ~~ i~~ i~ J
l 6-9
25 - À 505
l 5 10 145
0= IA'A - ÀI 1= 50 100 - À 10 = (150 - A)(A - 120)A
5 10 145 - À
so Ai = 150, A2 = 120, and Ag = 0, Next,
¡ 25 50 5 J
50 100 10
5 10 145
gives
r ei J' r ei J
l :: = 150 l::
-120ei + 60e2 0 1 ( J
-25ei + 5eg
VùU
O or ei
= 'W0521
lD 145
( ~5 i~~
i~ J
eg e2
( :~ J = 120 (:~ J
35
gives -l~~~ ~ -2:~: ~ or., = ~ ( j J
Also, ea = (2/J5, -l/J5, 0)'.
(c)
3 68 -9
(4
8J
= Ý150 ( _~ J (J. vk j, J + Ý120 ( ~ J (to ~ - to J
2.24
a)
;-1 = ~
'9
( 1
c)
For ~-l
+:
À1 = 4,
a
1
a
b)
n
À1 = 1/4,
À2 = 9 ~
À3 = 1,
':1 = (1 ,O,~) i
À2 = 1 /.9, ':2 = (0 ~ 1 ,0) ,
À3 = 1,
el
-3
= (OlO~l)1
=l=('~O,OJ'
=2 = (0,1,0)'
=3 = (0,0,1)'
36
2.25
Vl/2 "(:
a)
a
3 4/15 1/6 1
(:
0
0Jt 1 -1/5 4flJ (5
2
a -1/5,
a
3 4/15 1/6
= (~:
a)
-.2 .26~
- 2
1
il
.1'67
' 1 67 i
" ~:i'67
V 1/2 .e v 1/2 =
b)
2.26
2
OJ ( 1 -1/5 4fl5J
o 'if.= ,-1/5 1 1/6=
a
1
° OJ i5
-1
1/6 0
2
° = -2/5
2
1 a
a
3 4/5
1/2
4/3) (5 a
1/3 a
2
3 a
0
:J
-2
4
n =f
1
1/2 i /2
P13 = °13/°11 °22, = 4/13 ¡q = 4l15 = ,2£7
b) Write Xl = 1 'Xl + O'X2 + O-X3 = ~~~. with ~~ = (1 ~O~O)
1 1 i , i 1 1
2 x2 + 2 x3 = ~2 ~ W1 th ~2 = (0 i 2' 2" J
Then Var(Xi) =al1 = 25. By (2-43),
~
1X 1X ,+ 1 2 1 .19
Var(2" 2 +2" 3) =':2 + ~2 =4 a22 + 4 a23 + '4 °33 = 1 + 2+ 4
15
= T = 3.75
By (2-45) ~ (see al so hi nt to Exerc,ise 2.28),
1 1 i 1 1
Cov(X, ~ 2Xi + 2 Xi) = ~l r ~2 = "'0'12 +"2 °13 = -1 + 2 = 1
~o
37
1Xl +1'2 X2) =
Corr(X1 ~ '2
2.27
, 1
COy(X" "2X, + '2X2) 1
.103
~r(Xi) har(~ Xl + ~ X2) =Sl3 :=
a)
iii
- 2iiZ ~
aii
b)
-lll
+ 3iZ ~
aii + 9a22 - 6a12
c)
iii + \12 + \13'
d)
ii, +~2\12 -. \13,
+ 4a22 - 4012
aii + a22 + a3i + 2a12 + 2a13 +2a23
aii' +~a22 + a33 + 402 - 2a,.3 - 4023
e) 3i1 - 4iiZ' 9a11 + 16022 since a12 = a .
38
2,31 (a)
E¡X(l)J = ¡,(l) = ¡ :i (b) A¡,(l) = ¡ 1 -'1 1 ¡ ~ J = 1
(c)
COV(X(l) ) = Eii = ¡ ~ ~ J
(d)
COV(AX(l) ) = AEiiA' = ¡i -1 i ¡ ~ n ¡ -iJ = 4
(e)
E(X(2)J = ¡,,2) = ¡ n tf) B¡,(2) (~ -iJ ¡ n = ¡ n
(g)
COV(X(2) ) = E22 = ¡ -; -: J
(h)
COV(BX(2)) = BE22B' = ¡ ~ -~ J (-; -: J (-~ ~ J - (~: -~ J
0)
COV(X(l), X(2)) = ¡ ~ ~ J
(j)
COV(AX(1),BX(2))=AE12B'=(1 -1) ¡~ ~J ¡ _~ n=(O 21
39
2,32 ~a)
EIX(l)j = ILll) = ¡ ~ J (b) AIL(l) = ¡ ~ -~ J ¡ ~ J = ¡ -~ J
(c)
Co(X(l) ) = En = l-i -~ J
td)
COV(AX(l)) = AEnA' = ¡ ~ -¡ J ¡ -i -~ J L ~~ ~ J - ¡ i ~ J
(e)
E(Xl2)j = IL(;) = ( -~ J (f) BIL(2) = ¡ ~ ; -~ J ( -~ i = ¡ -; J
(g)
COV(X(2) ) = ~22 = 1 4
( -1
6 10 -~1 i
(h)
COV(BX(2) ) = BE22B' ,
= U i -~ J (j ~ -~ J U -n 0)
CoV(X(1),X(2)) = ¡ l ::J ~ J
(j)
COV(AX(l) i BX(2)) = AE12B'
¡ 12 9 J
9 24
40
- U j J H =l n (¡ j J - l ~ ~ J
2,33 (a)
E(X(l)j = Li(l) = ( _~ J (b) Ati(l) = L î -~ ~ J ( _~ J - ¡ ~ J
(c)
Cov(X(l¡ ) = Eii = - ~ - ~
( 4 i 6-i~J
(d)
COV(AX(l) ) = Ai:iiA' ,
¡234)
= (î -~ ~) (-¡ -~!J (-~ n -
4 63
(e)
E(X(2)J = ti(2) = ¡ ~ ) (f) Bti(2) = ¡ ~ -î J ¡ ~ J = I ; )
(g)
Co( X(2) ) = E" = ¡ ¿ n
(h)
CoV(BX,2) ) = BE"B' = U - î ) L ¿ ~ J D - ~ J - I 1~ ~ J
41
(i)
COv(X(1),X(2))= -1 0
1 -1
( _1
0 J
ü)
COV(AX(l), BX~2)) = A:E12B1
= ¡ 2 -1 0 J (=!O J
1 1 3 i1 -1
0
¡ ~ - ~ J = ¡ -4,~ 4,~ J
42
2.34
bib = 4 + 1 + 16 + a = 21,-did
- = 15 and bid = -2-3-8+0 = -13
(ÉI~)Z = 169 ~ 21 (15) = 315
2.35
bid
- -
biBb
-
= -4 + 3 = -1
= (-4, 3)
=
L: -:J
(-:14
23)
( -~ J · 125
(-~ J
2/6 ) il )
d I B-1 d
=
(1~1) 2/6
11/6
=
2/6 1
( 5/6
--'
so 1 = (bld)Z s 125 (11/6)" = 229.17
2.36 4x~ + 4x~ + 6xix, = x'Ax wher A = (: ~).
(4 - ).)2 - 32 = 0 gives ).1 = 7,).2 = 1. Hence the maximum is 7 and the minimum is 1.
2.37
From (2~51),
max
x'x=l
- -
X i Ax =
max
~ 'A!
~13
= À1
~fQ
where À1 is the largest eigenvalue of A. For A given in
2.7 ~ Ài = 10 and
-1 x I x Fl
Exercise 2.6, we have from Exercise
el . (.894, -,447), Therefore max xlAx = 10 andth1s
maximum is attained for : = ~1.
2.38
Using computer, ).1 = 18, ).2 = 9, ).3 = 9, Hence the maximum is 18 and the minimum is 9,
43
2.41 (8) E(AX) = AE(X) = APX = m
o OJ
(b)
Cav(AX) = ACov(X)A' = ALXA' = (~
18 0
o 36
(c) All pairs of linear combinations have zero covarances.
2.42 (8) E(AX)
=
AE(X)=
Apx =(i
o OJ
(b)
Cov(AX) = ACov(X)A' = ALxA' = ( ~
12 0
o 24
(c) All pairs of linear combinations have zero covariances.
44
Chapter 3
3.1
a) ~ = (:)
b) ~, = ~, - i,! = (4 tOt -4) i
':2 = ~z - x2! = (-1 t '. 0) I
c)
et
L = m.
L = 12
..1
:2
Let e be the angl e between .:, and :2' then èos ~e) ~
-4//32
x 2 = -.5
:, 22 ~2
Therefore n s" = L2 or $" = 32/3; n S = i2 or S22 = 2/3;
n 5'2 = ~i':2 or slZ = -4/3. Also, riZ = cos (e) = -.S. Conse-
quently
S = and
n -4/3
2/3R =-.5
1
(32/3 -413) "( 1" -.5)
3.2
a) g = (;J
b) :1 = II - xl! = (-', 2, -11'
~2 = l2 - xz! = (3, -3, 0)'
c)
L =/6; L =11
':1 ~2
Let e be the angle between ':1 and ~2' then eOs (e) =
-9/16 x 18 = - .866 .
Therefore n 31, = L!l or s" = 6/3 = 2; n 522 ~ l~ or szi =
= 18/3 = "6; n ši.2 = :~ -:2
or :5'2 = -9/3 = -3. Also, r1Z =
~"s (e) = _ .8:6'6. Consequently So =( Z -3) and R= ( 1 - .8661
-3 '6 . -.86'6 1 J
45
3.3
xl !
II = (1, 4, 4)';
= (3,3. 3);
Thus
li
=
4
=
a)
l'.(~
1 .--
+
3
3
4
3.5
_2J
3
1
5
5
- l1&
X
:) ;
3
(: i :J
_.) - ')'
~2J
S .l6
0
=e
-:)E ~;o1. -4
( 32
1
-:J
and l sIc: l2
-2
6
b)
ii ! + (ll - xl l)
1
2S=(X-xl CX-xl
.. ..
so
II - ii ! = (-2, 1, 1 J'
i l' · (;
1: =
, (34
-2
4
;J
1
:J ~
". ,. .. ..
2 S = (X - 1 x')' ( X-I ?) =
so S =.. ( 3 -9/2 J
-1
3
~
-3
-1
0
2
(-31
-3
-:J
e -9)
= -9 18
and Isl = 2.7/4
-9/2 9
- N 3 -1 2 N
3.6 a) X'- 1 x' = r -~ ~ -~ J. Thus d'i = (-3, 0, -3),
!2 = to, 1, -1) and ~/3 = (-3, 1,2) .
Since,Ši = .92 = 23' the matrx of deviations is not offull ra.
46
15J
-3
b)
2 S =
(X -..
1 X')'
X-I-xl) = ( ~ ~
"i ( øw
15
So
S = -3/2 1
-1/2
( 15/2
9 -3/2.
2
-1
-1
l4
1'5/2)
-1/2
7
. .
Isl = 0 (Verify). The 3 deviation vectors lie in a 2-dimensional
subspace. - The 3-dimensional volume ,enclosed by ~he deviation
vectors 1 s zero.
c) Total sample varia-nce = 9 + 1 + 7 = 17 .
-
3.7
All e11 ipses are 'centered at
i) For S = (: : J '
-x .
-4/9J
S";1
~ (-:~:
519
Eigenvalue-normalized eigenv~ctor pairs for 5-1 are:
À1 = 1. ;1 = (.707, -.707)
À2 = 1/9, !~ = (.707, .7n7)
Half lengths of
axes
.. - ..-
of ellipse (x-x)'S-l(X-X) S 1
are l/Ir = 1 and l/~ = 3 respectively. The major axis
of ell ipse 1 ies in the direction of ~2; the minor axis
1 ies in the direction of :1.
if)
For
s=
,
s
=
-4
5
( 5 -4) -1 .
4/9)
4/9
(5/9
Eigenvalue-normal ized eigenvectors for
5/9
5-1
Ài = 1. :~ = (.707. .707)
i
À2 = 1/9, ~2 = (.7~7, -.7Ð7)
are:
47
Half l~ngths ~faxes of ell ipse (x....
- x)'S-l ...
(x - x) ~ 1 are,
of the
again. l/lr = 1 and 1/1. = 3. The major axes
ellipse li.es in the direction of ':2; the minor axis lieS'
in the directi~n of =1. Note that ~2 here is =1 in
"part (i) above and =1 here is =2 in part (i) above.
o 3 0 l/3
iii) For S = (3 0),. S-l = (1/3 OJ
Eigenvalue-normalized eigenvector pairs for 5-1 are:
).1 = 1 13; ~i = (1, 0)
).2 = 1/3, !~ = (0. lJ
axes
Half lengths of
(x....
- x)' 5-1_..
(x - x) s 1
of ellipse
are
equal and given by l/ir = l/lr = 13. Major and minor
"
axes of ellipse can be taken to lie in the directions of
the
coordinate axes. Here, the salid ellipse.is, fn fact, a solid
sphere.
Notice for aii three cases 1s1 = 9.
3.8
a)
Total
sample variance in
both cases is
3.
0
b)
For
S. G
1
0
Isl = 1
~J.
-1/2
For S =(-1~2
-1/2
1
- l/2
-1/2J
-1/2 ,
1
Isl = 0
48
3.9 (8) Vve calculate æ = (16,18,34 l and
-4 -1 -5
2
2
4
Xc= -2 -2 -4
4
0
0
4
1
1
and we notice coh( Xc)+ coh( Xc) = cOli( Xc)
so a = fl, 1, -1 J' gives Xca = O.
(b)
S = 1~ 2.~ 5~~ so S = -(13)2(2.5) _ 9(18.5) -55(5.5) = 0
13 5.5 18.5 "
( J I I 10(2.5)(18.5) + 39(15.5) + 39(15.5)
As above in a)
Sa = ( 3 ~ ;53 -= 5~~ J - ¡o~ J
13 + 5.5 - 18.5
( c) Check.
3.10 (a) VVe calculate æ = (5,2,3 J' and
-2 -1 -3
1
Xc= -1
2
0
2
0
-2
1
3
-1
and we notice coh( Xc)+ ~012( Xc) = cOli( Xc)
0
1
so a = iI, 1, -1 J' giv.es Xca = O.
~b)
S =. 0 2.5 2.5
soI-S-(2.5)3
_ 0 -+(2.5)3
I - 5(2.5)2
0 + 0 = i)
( 2.52.5
2.5 .0 2.55 J
Using the
save coeffient vector a as in Part a) Sa = O.
49
(c:) Setting Xa = 0,
3ai + a2 = 0
7ai + 3ag = 0 so
5ai + 3a2 + 4ag = 0
ai 5ai
g
-"jag
3(3ai) + 4ag = 0
so we must have ai = as = 0 but then, by the first equation in the fil"t
set, a2 = O. The columns of the data matrix are linearly independent.
1 4213 J
3.11
Con~equently
S =
14213
i14808
15538
o)
09:70) ;
01/2 = (121 ~6881
R =
124 .6515
( 09:70
and
0-1/2 =
(" 0:82
00:0 J
The relationships R = 0-1/2 S 0-1/2 and S = 0'12 R 01/2
can now be verifi ed by direct matrix multiplication.
50
3.14. a) From fi rst pri nciples we hav.e
f ~l · (2 3) (~J' 21
-
Similarly Ë' ~2 = 19 and Ë' ~3 = 8 so
sample mean =
2l+19+8 = 16
3
sampl~ vari ance =
(21_16)1+(19-16)2+(8-16)2 = 49
2
I
Also :' ~1 · (-1 2) (~J = -7;
C -2
X=
_
1
and :' ~3 = 3
so
sampl e mean = -1
sampl~ variance = 28
Finally sample covariance = (21-16)(-7+1)+(19-16)(1+1)+(8-16)(3+1) =
-2.8.
b) ~-I= (5
.
2)
Using (3-36)
and
S · ( ~: -12 J
51
sample mean of b' X =~' ~. (2 3) (:1 = 16
sample mean of :' ~ = (-1 2) (:1.-1
sample variance of b' X · ~' S~ · (2 3) e: -121(: 1 = 49
sample variance of C' X = :' S:.' (-1 21 C: -121 (";1 · .28
'"
sample covariance of ..
b' X..
and..
c' X
:b'Sc=(23) " . =-28
- -, l6
-2-2J1 (-11
2
Resul ts same as those ; n part (a).
3.15
-2.5
E · (;1.
S = -2.5
1.5 -1.5
(13
1
sampl e mean of -b.-X= 12
sample mean of c.
X = -1
- samp1 e variance of b' X = l2
sampl e vari ance of c' X = 43
sample covariance of b' X and c' X = -3
1.SJ
-1.5
3
52
3.16
S 1 nee
tv =E(~ -~V)(~ -~V)'
I , I I)
= E(~ - ~V - ~V~ +~VJ:V
, 'E(V' )" ,
,,,,
;: E(~ ) - E(~)!:V - ~V _ +~V!:V
:: E(~ ) - !:VJ:V -: !:V~V + ~V!:V
= E(~') - !:V!;V. '
we have E(VV') = * + !;V!;~ .
3.18 (a) Let y = Xi+X2+X3+X4 be the total energy consumption. Then
y=(1 1 1 l)x=1.873
,
s~ =(1 1 1 I)S(1 1 1 1) =3.913
(b) Let y = Xl -X2 be the excess of petroleum consumption over natural gas
consumption. Then
y=(i -1 0 0)x=.258
,
s~ =(1 -1 0 O)S(1 -1 0 0) =.154
S3
Chapter 4
4.1 (a) We are given p = 2 i
2 -.8 x V2i J
¡i=(;J E=¡ -.8 x J2
50
I E I = .72 and
E-1 = ~:
i
(i
1
2
2V2
2
)
(27l) .72 2 .72.9 .72
( 4:
i V2)
:7
I(:i) = V: exp -- ( -(Xi - 1) + -(Xl - i)(X2 - 3) + -(X2 - 3)2)
1
(
)2
2V2(
2
2
.72.9 .72
-(b)
- Xl - 1 + - Xl - 1)(x2 - 3) + -(X2 - 3)
4.2 ta) We are given p = 2 ,
I' = (n E =(
2
1
V2
and
2
L-l =
v'
~)
so I E I = 3/2
-4
. V2"J
2
-T
:i = (27l)'¡3/2
3Xi -23Xl(X2
+ 3 X2 - 2) )
I(
) i (exp1-2"(2
2V2- 2)4()2
(b)
2
2
2V2
4
2
3 3 3
-Xi - -Xi~X2 - 2) + -(X2 - 2 )
~c) c2 = x~(.5) = 1.39. Ellpse centered at (0,2)' with the major ax liav-
in, haif-length .¡ c = \12.366\11.39 = 1.81. The major ax lies
in the direction e = I.SSg, .460)'. The minor axis lies in the direction
e =i-Aß-O , .B81' and has half-length ý' c = \I;ô34v'1.S9 = .94.
54
Constant density contour that contains
50% of the probability
oc?
I.
~
C'
x ~0
..I.
..o
-3
-2
-1
o
1
2
3
x1
4.3 We apply Result 4.5 that relates zero covariance to statisti~a1 in-
dependence
a) No, 012 1 0
b) Yes, 023 = 0
c) Yes, 013 = 023 = 0
d) Yes, by Result 4.3, (X1+XZ)/Z and X3 are jointly normal and
their covariance is210
= 0. (0 ,
1 +1a.
3 2 ¿3
e) No, by Result 4.3 with A = _~ 1
to see that the covari anc.e i~ 10 and not o.
_ ~ ), form A * A i
ss
4.4
a) 3Xi - 2X2 + X3 is N03,9)
b) Require Cov (X2,X2-aiXi-a3X3) = : - a, - 2a3 = O. Thus any
~ i = tai ,a3J of the fonn ~ i
requirement. As an example,
4.5
= (3-2a3,.a3J wi 11 meet the
-a'
= (1,1).
a) Xi/x2 is N(l'(XZ-2),~)
b) X2/xi ,x3 is N(-2xi-5, 1)
c) x3lxi ,x2 is N(¥x1+X2+3) ,!)
4.6 (a) Xl and X2 are independent since they have a bivariate normal distribution
with covariance 0"12 = O.
(b) Xl and X3 are dependent since they have nonzero covariancea13 = - i.
~c) X2 and X3 are independent sin-ce they have a bivariate normal distribution
with covariance 0"23 = O.
(d) Xl, X3 and X2 are independent since they have a trivariate normal distribution where al2 = û and a32 = o.
te) Xl and Xl + 2X2 - 3X3 are dependent since they have nonzero covariance
au + 20"12 - 3a13 = 4 + 2(0) - 3( -1) = 7
4.7 (a) XilX3 is N(l + "&(X3 - 2) , 3.5)
.(b) Xilx2,X3 is N(l + .5(xa - 2) ,3.5) . Since)(2 is independent of Xi, conditioning further on X2 does not change the answer from Part a).
S6
4.16 (a) By Result 4.8, with Cl = C3 = 1/4, C2 = C4 - -1/4 and tLj = /- for
. j = 1, ...,4 we have Ej=1 CjtLj = a and ( E1=1 c; ) E = iE. Consequently,
VI is N(O, lL). Similarly, setting b1 = b2 = 1/4 and b3 = b4 = -1/4, we
find that V2 is N(a, iL).
(b) A.gain by Result 4.8, we know that Viand V 2 are jointly multivariate
normal with covariance
4
(1
1
-1
1
1
-1
-1
-1
)
( L bjcj ) L = -( -) + -( - ) + -( -) + -( - ) E = 0
j=1 4 4 4 4 4 4 4 4
That is,
( ~: J is distributed N,p (0, (l; l~ J )
so the joint density of the 2p variables is
I( v¡, v,) = (21l)pf lE I exp ( - ~(v;, v; J (l; l~ r (:: J )
1 . (1 i -1 i -l ) )
= (27l)pl lE I exp - s( VI E Vl + V2 E V2
4.17 By Result 4.8, with Cl = C2 = C3 = C4 = Cs = 1/5 and /-j - tL for j = 1, ...,5 we
find that V 1 has mean EJ=1 Cj tLj = tL and covariance matrix ( E;=1 cJ ) .L =
lL.
Similarly, setting bi = b3 = bs = 1/5 and b2 = b4 = -1/5 we fid that V2 has
mean ì:;=i bj/-j = l/- and covariance matrix ( ¿:J=1 b; ) L = fE.
Again by Result 4.8, we know that Vi and V2 have covariance
4
(1
1
-1
1
1
1
-1
1
1
1)
1
(~b'c.)L=
-( -)5+-5.(-)5.Jl -(
- )+-(
.;; J 1 "5
5 -)5+-(
5"5
'5 5-) ~=-E
25
57
4.18 By Result 4.11 we know that the maximum 1 He1 i hood estimat.es of II
and
and t are x = (4,6) i
1 L - -)'
n
_n j=l
(x.-x)(x.-x
= t tmH~J)(m-mHm-(~J)((:1-(m'
-J - -J.(GJ-m)(~H~J) '.((~J-m)mimn .
= t tc~J Gi aj.~¥o -i).m(i j) .(~JfP 1)1
b) From (4-23), ~ - N~(~,io t). Then ~-~ - N~(~,io t) and
finally I2 (~-~) - Nô(~,t)
c) From (4-23), 195 has a Wishart distribution with 19 d.f.
4.20 8(195)B' is a 2x2 matrix distributed as W19('1 BtBt) with 19 d.f.
where
1 1 1
1 1 1 1 1'1
1 1 i
a) BtB i has
(1,1) entry =011 + ~22 + tf33 - 012 - G13 + Z'23
l'
(1 ,2) entry = -r14 of :t.24 +tf34 -'ZOlS +:tZ5 +-r35 +?'l ô - za26 - f13'ô
(2,2) entry = 0ô6 + :t55 + tf44 - °46 - °S6 + zc45
°131 .
b)
stB'
°31
=l °11
G33J
S8
4.21 (a) X is distributed N4(J.1 n-l~ )
(b) Xl - J- is distributed N4\OI L ) so ( Xl - J. )'L-1( Xl - J. ) is distributed
as chi-square with p degrees of freedom.
(c) Using Part a)i
( X - J. )'( n-1L )-l( X - J. ) = n( X - Jl )'~-l( X - J. )
is distributed as chi-square with p degrees of freedom.
(d) Approximately distributed as chi-square with p degrees of freedom. Since
i L can be replaced by S.
the sample size is 1
arge
59
4.22' a) We see that n = 75 is a sufficiently lar"ge sample (compared
with p)and apply R,esult 4.13 to get Iñ(~-!:) is approximately
Hp(~,t) and that ~ is approximately Np(~'~ t).
c i -1(- )
By (4-28) we ~onclude that ýn(X-~) S ~-~ is approximately
b)
X2
p.
4.23 (a) The Q-Q plot shown below is not particularly straight, but the sample
size n = 10 is small. Diffcult to determine if data are normally distributed
from the plot.
Q-Q Plot for Dow Jones Data
30
.
.
.
.
20
.
-C
10
.
)C
.
0
.
.
-10
.
-20
-2
-1
0
1
q(i)
(b) TQ = .95 and n = 10. Since TQ = .95 ~ .9351 (see Table 4.2), cannot reject
hypothesis of normality at the 10% leveL.
2
60
4.24 (a) Q-Q plots for sales and profits are given below. Plots not particularly
straight, although Q-Q plot for profits appears to be "straighter" than
plot for sales. Difficult to assess normality from plots with such a small
sample size (n = 10).
Q-Q Plot for Sales
300
250
a.'I.
200
~
.
150
.
100
50
-2
-1
o
2
1
q(i)
.. Q"4 P1Ót for l)rofits
.
.
lS
.
10
-2
~1
o
1
2
q(i)
(b) The critical point for n = i 0 when a = . i 0 is .935 i. For sales, TQ = .940 and for
profits, TQ = .968. Since the values for both of these correlations are greater
than .9351, we cannot reject normality in either case.
61
4.25 The chi-square plot for the world's largest companies data is shown below. The
plot is reasonably straight and it would be difficult to reject multivariate normality
given the small sample size of n = i O. Information leading to the construction of
this plot is also displayed.
5
4
1i
is 3
g
'"
l!
u 2
'!
o
1
o
o
2
1
3ChiSqQuantii.
4 5
6
303.6 -35576 J
x = 14.7
S = 303.6
710.9
(155.6J
(-35576
7476.5
Ordered SqDist
.3142
1.2894
1.4073
1.6418
2.0195
3.0411
3.1891
4.3520
4.8365
4.9091
26.2 -1053.8
-l053.8 237054
Chi-square Ouantiles
.3518
.7978
1.2125
1.6416
2.1095
2.6430
3.2831
4.1083
5.3170
7.8147
7
8
62
x=( 12.48
5.20J s=(
10.6222 -17.7102
J s-I 1.2569
=(2.1898 .7539
1.2569
4.26 (a)
' -17.7102
30.8544'
J
Thus dJ = 1.8753, 2.0203, 2.9009, .7353, .3105, .0176, 3.7329, .8165,
1.3753, 4.2153
50% contour.
(b) Since xi(.5) = 1.39, 5 observations (50%) are within the
(c) The chi-square plot is shown below.
CÍ1i~squåre pløt for
.
.
..
.
. 2
(d) Given the results in pars (b) and (c) and the small number of observations
(n = 10), it is diffcult to reject bivarate normality.
4.27
q-~ plot is shown below.
63
*
*
*
100.
* 2
2 2
so.
*"..
*3*
**2*
:;3 3
2*
2*
60.'
40. *
*
*
* *
:\
20.
\
-2. S
i
I
-1.S
-0.5
0.5
i.5
\'a(i)
%.5
Since r-q = .970 -i .973 (See Table 4..2 for n = 40 and .a = ..05) t
we would rejet the hypothesis of normality at the 5% leveL.
64
4.29
(a).
x = (~~4~:~~:~)' s = (11.363531 3~:~~:~~~).
Generalized distances are as follows;
2.3771
0.8162
1 . 6283
0.4135
o . 47£ 1
1. 1849
1.3566
o .6228
5.6494
o . 8988
4. 7647
3.0089
6.1489
1 .0360
2.2489
3.4438
0.1901
o .4607
1 .8985
2 .7783
8.4731
1.1472
0.6370
o . 6592
O. 1388
7 . 0857
o . 7032
0.3159
2.7741
0.8856
o .4607
o . 6592
10.6392
0.4135
1.0360
0.1388
0.1225
o . 7874
O. 1380
O. 1225
1 .4584
1. 80 14
(b). The number of observations whose generalized distances are less than X2\O.ti) = 1.39 is
26. So the proportion is 26/42=0.6190.
(c). CHI-SQUARE PLOT FOR (X1 X2)
8
w
a:
8
~
~
4
c
2
0
0
2
4
6
8
10
~saUARE
4.30 (a) ~ = 0.5 but ~ = 1 (i.e. no transformation) not ruled out by data. For
~ = 1, TQ = .981 ~.9351 the critical point for testing normality with
n = 10 and a = .10. We cannot reject the hypothesis of normality at
the 10% level (and, consequently, not at the 5% level).
(b) ~ = 1 (i.e. no transformation). For ~ = 1, TQ = .971 ~.9351 the critical
point for testing normality with n = 10 and a = .1 O. We cannot reject the
hypothesis of normality at the 10% level (and, consequently, not at
the 5% level).
(c) The likelihood function 1~Â" --) is fairly flat in the region of Â, = 1, -- = 1
so these values are not ruled out by the data. These results are consistent with
those in parts (a) and (b).
n-n niot~ follow
65
4.31
The non-multiple-scle"rosis group:
X2
X3
X4
Xs
0.96133Xi3.S
0.95585(X3 + 0.005)°.4
0.91574X¡3.4
0.94446-
Xl
X2
X3
0.91137
0.97209
0.79523-
X4
0.978-69
Xs
0.84135-
Xi
0.94482X-o.s
1
rQ
(Xs + 0.'(05)°.32
Transformation
*: significant at 5 % level (the critical point = 0.9826 for n=69).
The multiple-sclerosis group:
rQ
-
-
-
(X5 + 0.005)°.21
(X3 + 0.005)°.26
Transformation
*: significant at 5 % level (the critical point = 0.9640 for n=29).
Transformations of X3 and X4 do not improve the approximaii-on to normality V~l"y much
because there are too many zeros.
4.32
Xl
X2
X3
X4
rQ
0.98464 -
0.94526-
0.9970
0.98098-
Transformation
(Xl + 0.005)-0.59
x.¡0.49
*: significant at 5 % level
-
XO.2S
4
X6
Xs
0.99057
-
0.92779(Xs + 0.ûå5)0.Sl
(the critical point = O.USïO for n=98).
4.33
Marginal Normality:
rQ
Xl
X2
0.95986*
0.95039-
X3
0.96341
X4
0.98079
*: significant at 5 % level (the ci"itical point = 'Ü.9652 for n=30).
Bivariate Normality: the X2 plots are
(X31 X4) appear reasonably straight.
given in the next page. Those for (Xh X2), (Xh X3),
66
CHI-SQUARE PLOT FOR (X1,X3)
CHI-SQUARE PLOT FOR (X1,X2)
8
8
6
w
"
~
.¿
~
~
8
i:
c
"
is
is
2
2
0
0
2
0
"
8
6
CHI-SQUARE PLOT FOR (X2,X3)
CHI-SQUARE PLOT FOR tX1 ,X4)
8
8
6
w
a:
w
~
c
~.¿
"
:f
6
"
is
CJ
2
2
0
0
0
2
"
8
8
"
2
0
12
10
8
10
12
CHI-SQUARE PLOT FOR (X3,X4)
CHI-SQUARE PLOT FOR (X2,X4)
8
8
8
w
6
a:
c
~
i:
~
8
e-SOARE
e-SOARE
w
10
e-SORE
e-SOUARE
i:
c~
8
6
"
2
0
10
"
"
:f
(.
:f
CJ
2
2
0
0
0
5
10
e-SOUARE
15
0
2
"
e-SORE
6
a
67-
4.34
Mar,ginal Normality:
Xl
rQ. 0.95162-
X2
X3
X4
0.97209
0.98421
0.99011
Xs
0.98124
X6
0.99404
*: significant at 5 % level (the critical point == 0.9591 for n==25).
Bivariate Normalitv: Omitted.
4.35 Marginal normality:
& (MachDir) X;i ,(CrossDir)
Xl (Density)
.991 .924*
rQ I .897*
* significant at the 5% level; critical point = .974 for n = 41
From the chi-square plot (see below), it is obvious that observation #25 is a
multivariate outlier. If this observation is removed, the chi-square plot is
considerably more "straight line like" and it is difficult to reject a hypothesis of
multivariate normality. Moreover, rQ increases to .979 for density, it is virtually
unchanged (.992) for machine direction and cross direction (.926).
Chi-square Plot
3S
:l
25
:!
15
Chi-square Plot without observation 25
10
6
10
12
2
4
6
B
10
12
68
4.36 Marginal normality:
100m 200m 400m 800m
rQ I .983 .976* .969* .952*
1500m 3000m Marathon
.909* .866* .859*
* significant at the 5% level; critical point = .978 for n = 54
Notice how the values of rQ decrease with increasing distance. As the distance
increases, the distribution of times becomes increasingly skewed to the right.
The chi-square plot is not consistent with multivariate normality. There are
several multivariate outliers.
4.37 Marginal normality:
100m 200m 400m 800m
rQ I .989 .985 .984 .968*
1500m 3000m Marathon
.947* .929* .921*
* significant at the 5% level; critical point = .978 for n = S4
As measured by rQ, times measured in meters/second for the various distances
are more nearly marginally normal than times measured in seconds or minutes
(see Exercise 4.36). Notice the values of rQ decrease with increasing distance. In
this case, as the distance increases the distribution of times becomes increasingly
skewed to the left.
The chi-square plot is not consistent with multivariate normality. There are
several multivariate outliers.
69
4.38. Marginal and multivariate normality of bull data
Normaliy
of Bull Data
A chi-square plot of the ordered distances
o
C\
r:l/
.¡ ..
'C
CI
~0
-ë ..
o
lt
.
..'
~'"
.... . .
2 4 6 8 10 12 14 16 18
qchisq
..
I/
r = 0.9916 normal
00
..C'
C\
_I/
01
"8 0
;; 0I/
~
::
u.
II
0010
=-
:i
ai 0
..
-2
.1
0
2
1
-2
Quantiles of Standard Norml
0
II
not nonnal
r = 0.9631
0
-1
Quantlles of
1
2
Standard Nonnal
I/
c:
r = 0.9847 nonnal
r = 0.9376 not nonnal
..
c:
ai
I/
u. iu.
~
a.
~~
oX 0
ai
0i-
C\
.0
..
d
lt
co
-2
0
co
II
0
1
Quantiles of Standard Nonnal
-1
2
. ...
.2
-1
0
1
2
Quantiles of Standard Nonnal
00
..01
r = 0.9956 normal
lt
r = 0.9934 normal
00
_ is:
..
Gl
_I/
:i
CI
co
Æ ;g
¡¡ 00
en
I/
..
C\
I/
0
I/
00
..C'
-2
-1
0
1
Quantiles of Standard Nonnal
2
-2
-1
0
1
Ouantiles of Standard Norml
2
70
XBAR
S
FtFrBody
100.1305
8594.3439
2 . 9600
209.5044
-0 .0534
-1. 3982
2.9831
129.9401
82.8108 6680. 3088
YrHgt
5-0.5224
995.9474
70.881-6
0.1967
54. 1263
1555.2895
1
2
3
4
5
6
7
8
2 . 9980
100 . 1305
Ordered
dsq qchisq
1 . 3396 0.7470
1. 7751 1.1286
1 . 7762 1.3793
2.2021 1 .5808
2.3870 1.7551
2.5512 1 . 9118
2.5743 2.0560
2.5906 2.1911
2. 7604 2.3189
3.0189 2.4411
3 . 0495 2.5587
9
10
11
12 3 . 2679
13 3.2766
14 3.3115
15 3.3470
16 3 . 3669
17 3.3721
18 3.4141
19 3 . 5279
2 .6725
2.7832
2.8912
2.9971
3.1011
3 . 2036
3 . 3048
3.4049
20
3.5453
3 . 5041
21
3 . 6097
3 .6027
22
23
24
25
3.6485
3.6681
3 . 7007
3 . 7236
3. 7983
3. 8957
3.7395
3.9929
3.4142 -0.0506
SaleHt
SaleWt
2.9831
82.8108
129.9401 6680 . 3088
3.4142
83 .9254
-0.0506
2.4130
4.0180
147.2896
83.9254 2.4130
147.2896 16850.6618
PrctFFB
BkFat
2 . 9600 -0.0534
209.5044 -1.3982
10.6917 -0.1430
-0.1430
Ordered
dsq qchisq
26 3.8618 4 .0902
27 3 . 8667 4.1875
28 3 .9078 4.2851
29 4.0413 4 .3830
30 4.1213 4.4812
31 4. 1445 4.5801
32 4 . 2244 4.6795
33 4.2522 4 . 7797
34 4.2828 4 . 8806
35 4.4599 4.9826
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
4. 7603
4. 8587
5. 1129
5 . 1876
5.2891
5 . 0855
5. 1896
5 . 2949
5 .4017
5 .5099
5 . 3004
5.6197
5.3518
5 . 7313
5 .4024
5 .8449
5 .9605
5.5938
6.0783
5.6333 6.1986
5 .7754 6.3215
6.2524 6.4472
5 .6060
6 . 3264
6.57'60
6.6491
6.7081
o .0080
Ordered
dsq qchisq
51
52
53
54
55
56
57
58
59
6 . 6693
6 . 8439
6.6748 6 .9836
6 .6751
6.8168
7 . 1276
7 . 2763
6 . 9863
7 .430 1
7. 1405
7 .5896
7 . 1763
7 . 7554
7.4577 7 .9281
7.5816 8.1085
7 .6287 8.2975
60
61
8 . 0873 8 . 4963
62
8 .6430 8 .7062
63
8 . 7748 8 .9286
64
8.7940 9. 1657
65
9.3973 9.4197
66
9 . 3989 9.6937
67
9 .6524 9.9917
68 10.6254 10.3191
69 10.6958 10.6829
70 10.8037 11. 0936
71 10.9273 11.5665
72 11.3006 12.1263
73 11.321$ 12.8160
74 12.4744 13.7225
75 17.6149 15.0677
76 21.5751 17.8649
From Table 4.2, with a = 0.05 and n = 76, the critical point f.or the Q - Q plot correlation coeffcient test for normality is 0.9839. We reject the hypothesis of multivariate
normality at a = 0.05, because some marginals are not normaL.
71
4.39 (a) Marginal normality:
independence
support benevolence
conformity leadership
.997 .984*
.993 .997
rQ I .991
* significant at the 5% level; critical point = .990 for n = 130
(b) The chi-square plot is shown below. Plot is straight with the exception of
observation #60. Certainly if this observation is deleted would be hard
to argue against multivariate normality.
Chi-square plot for indep, supp, benev, conform, leader
15
.
. ...
10
. ~&
..
..
"..
du)A2
5
..
o
o
2
4
6
8 10 12 14 16 18
q(u-.5)/130)
(c) Using the rQ statistic, normality is rejected at the 5% level for leadership. If
leadership is transformed by taking the square root (i.e. 1 = 0.5), rQ = .998 and
we cannot reject normality at the
5% leveL.
72
4.40 (a) Scatterplot is shown below. Great Smoky park is an outlier.
o
G..u;t .;..01/.0':
500
o -...
.'
.
cO
"Visitors
5 6
8
7
9
(b) The power transformation -l = 0.5 (i.e. square root) makes the size
observations more nearly normaL. rQ = .904 before transformation and
rQ = .975 after transformation. The 5% critical point with n = 15 for the
hypothesis of normality is .9389. The Q-Q plot for the transformd
observations is given below.
10
-1
1
(c) The power transformation ~ = 0 (i.e. logarithm) makes the visitor
observations more nearly normaL. rQ = .837 before transformation and
rQ == .960 after transformation. The 5% critical point with n = 1"5 for the
hypothesis of normality is .9389. The Q-Q plot for the transformed
observations is .given next.
73
(d) A chi-square plot for the transformed observations is shown below. Given
the small sample size (n = i 5), the plot is reasonably straight and it would be
hard to reject bivarate normality.
.. _.,..,.'...... -,
....,...
.........'..,....
".. .. ",-,-,-,
.......:.,.
transformed nat
Chi-square plot for
..
o
Ð
1
..
2 3 4 5
Chi~square quantiles
6
7
74
4.41 (a) Scatterplot is shown below. There do not appear to be any outliers with the
possible exception of observation #21.
(b) The power transformation ~ = 0 (i.e. logarithm) makes the duration
observations more nearly normaL. TQ = .958 before transformation and
TQ = .989 after transformation. The 5% critical point with n = 25 for the
hypothesis of normality is .9591. The Q-Q plot for the transformed
observations is given below.
Dutå60n
Q-QPlatfotNatural Log
3.0
2.5
(I
5'
o~ 2.0
1.5
1.0
-2
-1
o
q(i)
1
2
7S
(c) The power transformation t = -0.5 (i.e. reciprocal of square root) makes the
man/machine time observations more nearly normaL. rQ = .939 before
transformation and rQ = .991 after transformation. The 5% critical point with
n = 25 for the hypothesis of normality is .9591. The Q-Q plot for the
transformed observations is given next.
ManlMachinl! Time
,QeQ',plot for Reê:iprocal of Square Root of
..
..
. .
. .
..
.2
-1
o
2
1
q(i)
(d) A chi-square plot for the transformed observations is shown below. The plot is
straight and it would be difficult to reject bivariate normality.
Ci., ",:,-,..,::'_',::-...','__d"":,'d,.'.' _ ',' "',, , ' ,,:':::;_:,":':'"::-_'..,.,..,'.:.;c_',:::,.,:;,',"',""" ._,_, _ "".:;..:',_'"':::',, -,-," 'J;'g'~
Chi-square plot for transformed,snow,rernovat data ...0 0'0
10
8
6
o
......
o
.....
. .. .
..
2
. . .
3 4 5
Chi-squa.~ quanti
6
7
8
7-6
Chapter 5
-i:13).
5.1 .) ~ "
(i60) ; s" (-i-:/3
f2 = 1 SO/ll = 13.64
b) T2 is 3ri,2 (see (5-5))
c) HO :~. ~ (7,11)
a =- .05 so F2,2(.05~ - 19.00
Since T2 _ 13.64 (;' 3FZ,2(.05) = 3(19) =57; do not reject H1l at
the a - .05 1 eve1
n
(n-1)!.J.g1 (:j-~O)(:j-~O)'!
5.3
a)
TZ ;.
n ,- - (n-1) = 3(~4) - 3 = 13:64
!j=l
r (x.-i)(xJ.-i)'!
.
-J - - -
b)
li - (I Jïi (~j-~)(~j-~) 'I 'r =
244 =.0325
(44)2
- I j~i (~r~~H~j-~o)' i/
Wil ks i 1 ambda '" A2/n = A1/Z = '.0325 - .1803
5.5 HO:~' = (.'5'5,;60); TZ = 1.17
a -.05; FZ ,40( .05) ;. 3.23
Since TZ '" 1.17 (; 2~~) F2,40( .05) =- 2.05(3.23) = 6.62,
we do not reject HO at the a" .05 level. The r,esult is ~onsistent
with the 9Si confidence ellipse
-
for ~ pi~tured in Figure 5.1 since
\11 = (.'55,.60) is inside the ellipse.
77
-1(- )
el
X-\1 .:
- CI-S -0
5.8
f227.273 -181.8181
t18L818 212.121 J
.603 .60
((.Sti4 J -( .5'5 J )
-1.909
= (2.636
J
tZ = n(~'(~-~O))Z =
a'
- SA
-
42(~.£,'3L. -1.9a'"J . )
(.014)
.003 2
= 1.31 = TZ
r2.636 -1 9091 .(.0144 .01 i71f2.6361
1. ':J .0117 .0146jL-i.909j
5.9 a) Large sample 95% T simultaeous confdence intervals:
Weight: (69.56, 121.48) Girt: (83.49, 103.29)
Body
leng: (16.55, 19.41)
lengt: (152.17, 176.59) Head
Neck: (49.61, 61.77) Head width: (29.04, 33.22)
b) 95% confidence region determined by all Pi,P4 such that
(95.52 - ,up93.39
~93.39
12.59/61
= .2064
- .006927--,u4
.019248
- P4
L,002799
.006927J(9S.52
-Pi)
Beginng at the center x' = (95.52,93.39), the axes of
the 95%
confidence ellpsoid are:
major axis
.,
minor axis
:t .J3695.52.Ji 2.5 9(' 939)
.343
:t .J45.92.J12.59(- .343)
.939
(See confidence ellpsoid in par d.)
c) Bonferroni 95% simultaneous confidence intervals (m = 6):
160 (.025 / 6) = 2.728 (Alternative multiplier is z(.025/6) = 2.638)
Weight: . (75.56, 115.48) Gii1h: (86.27, 100.51)
Body lengt: (155.00, 173.76) Head length: (16.~, 19.0g)
Neck: (51.01, 60.37) Head width: (29.52, 32.74)
d) Because ofthe high positive correlation between weight (Xi) and girt~X4),
the 95% confidence ellpse is smaller, more informative, than the 95%
Bonferroni rectangle.
78
5.9
,Continued)
Large sample 95% confidence regions.
0
....
large sample simultaneous
Bonferroni
..0
LO
0
..0
"'
-- --- -~ -------- - - - - ---- - ----
LO
)(
C'
0
C'
I
:
,
i
I
:
i
:
:,
I.
- - - - - - - - - - - - - - - - -.
LO
CD
I
. . . . .. .. . . . . .'. .. . . . .. . ... . . . . . . . . . -' . . . . . . . . . . . . "l . . . . . . ~
0
CD
60
70
80
100
90
110
120
130
x1
e) Bonferroni 95% simultaneous confidence interval for difference between
mean head width and mean head lengt (,u6 - tls ) follows.
(m = 7 to allow for new statement and statements about individual means):
t60 (.025/7) = 2.783 (Alternative multiplier is z(.025/7) = 2.690)
n 61
x6 -xs :tt60 . J
- (0036) S66 - 2sS6 + sss = (31.13 -17.98) +_ 2.78~~2i.26 -2(13.88) + 9.95
or
12.49:: tl6 -,us:: 13.81
79
5.10 a) 95% T simultanous confidence intervals:
Lngt: (13D.65, 155.93) Lngt4: (160.33, 185.95)
Lngt3: (127.00, 191.58) Lngt5: (155.37, 198.91)
b) 95% T- simultaneous intervals for change in lengt (ALngt):
~Lngth2-3: (-21.24, 53.24)
~Lngt-4: (-22.70, 50.42)
~Lngth4-5: (-20.69, 28.69)
c) 95% confidenceregon determined by all tl2-3,tl4-S such that
. 16-tl2_3,4-tl4_s
( i.Oll024 .009386J(16.- ~72.96/7=10.42
,u2-3)
.009386 .025135 4 - ,u4-S
where ,u2-3 is the mean increase in length from year 2 to 3, and tl4-S is
the mean increase in length from year 4 to 5.
the 95% confidence
Beginnng at the center x' = (16,4), the axes of
ellpsoid are:
maior axis
.~~.895)
:tv157.8 72.96.
- .447
mior axis
:t .J33.53.J72.96 .
( .895
447)
(See confidence ellpsoid in par e.)
d) Bonferroni 95% simultaneous confdence intervals (m = 7):
Lngt: (137.37, 149.21)
Lngt4: (167.14, 179.14)
Lngth3: (144.18, 174.40)
Lngth5: (166.95, 187.33)
..6Lngth2-3: (-1.43, 33.43)
i1Lngth4-5: (-7.55, 15.55)
i1Lngt3-4: (-3.25, 30.97)
'80
-5.10 (Continued)
e) The Bonferroni 95% confidence rectangle is much smaller and more
informative than the 95% confidence ellpse.
95% confidence regions.
o
"l
simultaneous T"2
o
C"
Bonferroni
...... ..........
....................................................
0C\
.,
vI
,.0
::
0
0,.
I
0C\
I
I
I
I
I
I
I
I
I
I
I
.
I
;
I
:
I
;
I
;
: I
i
:
I
I
I
--~--------- ----------------~
.. . , . .~. . ... .. , ... , , . , . . . J. . . . . .. . .. , . . . , , . . . . , . . , , , . , .. . . .
-20
o
20
J.2-3
40
81
5.11 a) E' =- (5.1856, 16.0700)
S = (176.0042 . 287.2412J;
S-1 =(
287.2412 527.8493
.0508
~ .0276 J
-.0276
.0169
Eigenvalues and eigenvectors of S:
,
,t = 688.759
~
--
A
.42 = 15.094
£1 = (.49,.87)
,
i. = (.87,-.49)
§i 16
Fp,n_p(.10) =: 7 F2.7(.10) = T (3.26) = 7.45
Confidence Region
45
40
35
~
L.
V)
'O
N
)(
!'
15 I 20 25 30 35 40 45
-10 -
,
I -10 J
x1 ( C r )
b) 90% T intervals for the full data set:
Cr: (-6.88, 17.25) Sr: (-4.83, 36.97)
(.30, 1
OJ' is a plausible value for i.
.r-
82
5.11 (Continued)
c) Q-Q pJotsfor the margial distributions of
both varables
.
oi
30
020
o. ......
10
.
-l. -UL .0.5 0.0 os 1.0 1.5
nomscor
normty for ths varable at a = 0.01
Since r = 0.627 we rejec the hypothesis of
80
.
7I
eo
50
u; 40
30
20
10
0
.
-1.
..
.
..
..
-1.0 .0.5 0.0 0.5 1.0 1.5
nomsrSr
Since r= 0.818 we rejec the hypothesis of
this varable at a = 0.01
normty for
d) With data point (40.53, 73.68) removed,
ii = (.7675, 8.8688);
.3786
S =b r .0303
1.0303 J
69.8598.
S-1·(2.7518
- .0406
-.0406 J
. 0149
-T p1n-p 'I
1. F (.10)= 7(62t F" 6(.10) '" 164 (3.4'6) ~. 8.07
90% r intervals: Cr: (.15, 1.9)
Sc: (.47, 17.27)
83
5.12 Initial estimates are
2 1.5
'ß - 6, ~ - 2.0 0.0 .
( 4 i - - (0.5 0.0 0.5 i
The first revised estimates are
'ß = 6.0000 , E = 2.500 0.0.
2.2500 1.9375
( 4.0833 i -( 0.6042 0.1667 0.8125 i
5.13 The X2distribution with 3 degrees of freeom.
Bonferroni interval = tn_i(a/2)/tn_i(a/2m).
5.14 Length of one-at-a time t-interval / Length of
n 2
15 0.8546
25 0.8632
-50 0.8691
100 0.8718
00 0.8745
m
4
10
0.7489
0.7644
D.7749
0.7799
0.6449
0.6678
0.6836
0.6911
0.6983
"0.7847
5.15
(0).
E(Xij) = (l)Pi + (0)(1 - Pi) = Pi.
Var(Xij) = (1 - pi)2pi +(0 - p¡)2(1 - Pi) = Pi(1 - Pi)
(b). COV(Xij, Xkj)
= E(XijXik) - E(Xij)E(Xkj) = 0 - PiPIi =-PiPk.
5.16
(6). Using Pj:: vx3.(0.05)VPj(1 - pj)ln, the 95 % confidence intervals for Pi, P2, 11, P4, Ps
are
(0.221, 0.370),(0.258, 0.412), (0.098, 0.217), (0.029, 0.112),\0.084, .a.198) respectively.
(b). Using Pi - ßi :l Vx3.(0.05)V(pi(1 - ßi) + ßi(1 - ßi) - 2ßiPi) In, the 95 % confdence
interval for Pi - P2 is (-0.118, 0.0394), There is no significant difference in two proportions.
5.17
ßi = 0.585, ßi = 0.310, P3 = 0.105. Using Pj:l vx'5(O.-D5)VPj(1 - Pi)fn, the 95 %.confidence
intervals for Pi, P2, 11 are "(0.488, 0.682), (0.219, 0.401), ('0.044, 0.lô6), respectively.
84
5.18
\lo). Hotellng's T2 = 223.31. The critical point for the statistic (0: = 0.05) is 8.33. We reject
Ho : fl = (500,50,30)'. That is, The group of students represented by scores are significantly
different from average college students.
(b). The lengths of three axes are 23.730,2.473, 1.183. And directions of corresponding ax..
are
-0.010 .
0.995 ,
0.103 .,
0.999 )
( -0.037
0.006 )
( -0.104
0.038 )
( 0.994
.(c). Data look fairly normaL.
..
700
70
/
60
I'
.I-
.
500
I"
ir
60
I'
I
;C
~
.Lf
xM
i
J
ii
o.
..."1
-1
0
-
.
--
15
-2
2
1
-.
.
-
-
.
.-
25
20
30
-2
-
.
.-
30
J
50
40
400
35
~
-1
0
.2
2
1
2
1
NORMAL SCORE
NORMA SCORE
NORMA SCORE
0
-1
.
.
700
600
-, ..
. .a. .
50' .
400
. ..~....
...-I. ...,.0....
. ... ...t
.... : .
.. .......... ..
60
..- .'1.
..e : .._
;c
... .a.
... I.. ....
x
500
400
I...
.. i. .:.-.
..
.
.. . ..
. .
.
:. 0.
60
.
N
x
I . 01
.. .
. .
...-..
.
.o .-.:
:.
..
i
.
1
0
I.oi o.0 ..0
. .! .
50
40
. . . .
. . .. 0
0
0
70
700
.
.. .
30
.o
30 40 50 60 70
15 20 2S 30 35
X2
X3
15
20
2S
30
35
X3
5.19 a) The summary statistics are:
361"621 .031
n = 30,
-x __ (18£0. 50J
~354 .13
and
s = (124055.17
361621 .03
348"6330.9'0 J
85
wher~ S has e i g~nva 1 ues and e; g~nv ect~rs
Å1 = 3407292
e~ = (.105740, .994394)
_1
Å2 = 82748
!2 = (.994394,-.1 0574~)
, n, '
Then, since 1 p(;:~) .Fp n_p(a) = 3~ 2~i) F2 2St .tl5) = .2306,
. -
is given by the set of \1
a 95% confidence region for ~
-
(1860.'50-\11' 8354.13-~2)
..
361621 .03 348633tl. 90 83~4 .13-~2
(124055.17 3'61~21.03J' ~1(1860.5tl-~lJ
. ~ .2306
half lengths of the axes of this ellipse are 1.2300 Ir = 886.4 and
The
l. 2306 .~ = 138 ~ 1. Th~refore the ell ipse has the form
-------_. --_...__.. . ...... --_..- .._._.. ".
-----------_.- ------_._-
-~
Ì"
/,.
'1
,
;
,
,.
:
--
:
f
j
;
'v..
,
:
;
~
;
,
:
,
I
!
;
...
I
!
i
.
i
.
i
,
i
,
,
,
,
I
:
,
-
,
,
i
:
,
,
,
¡
,~~.So .
:: --
-
l
I
,
i
"J
JI
:
!
,
!
i
J
.1
I
1
.
;
:
!
i
;
i
;
:
,
!
l
:
11
,
!
:
I
i
!
.
i
i
:
ß~5'4.13
.1
,
,
;
:
'.
i
;
,
:
i
:
:
~ I
:
i
:
';fJ"w
:
,
:
;
;
,
,
,
i
:
,
,
;
:
.
¡
'-
I
10000
:
;
/'
i
:
,
I
!
i
;
2,000
-
,
:
i
~E
.
,
:¿1,
I
'. . IOQ"
i
2,.aoø. '
,
. 3öOO '
I
'. l.øo.ft
Xl
:
86
b) Since ~O = (2000, 10000)' does not fall within the 9Siconfidence
ellipse, we would reJect the hypothesis HO:~ = ~O at the 5% level.
Thus, the data analyz~d are not consistent with these values.
c) The Q-Q plots for both stiffness and bending strength (see below)
show that the marginal normal ity is not seri ously viol ated. Ai so
;
coefficients for the test of normal ity are .989 and
the correlation
.990 respectively so that we fail to reject even at the ii signifi-
cance level. Finally, the scatter diagram (see below) does not indicate departure from bivariate normality. So, the bivariate normal
distribution is a plausible probability model for these data.
Q-Q Plot-Bend i n9 Strength
X2
12000. .
*
...._-----
* * *
10000.
**
**
. ._-* ***
---- -.- --
**
8000 .
..2--..' ..
*****
- _.. .,-------***"
*
._-"-
..._--- -_._-
. -._--_._--
* * *
6000.
- ...... *
4000. :i
-2.0
------_.._---
._--.._.._....... _.
l
,
-1.'0
t:rr.e 1 at; on .989
0.0
1.~
2.0
3.0
I.
!
87
Q-Q Plot-Stiffness
Xi
2800.
*
* *
2400 .
* *
***
*
::ooo.
..
***2
*2
**
**
****
1600 .
* *
*
."__0"" ._____. .._--_ -_._---"
1200.
*
*
800. .._----------- ._-- ..---"
..
-2.0
_ ____, ._-_ _=J.!.9..._
. _.._ . ..Correlation .. -.990 .
_......-_.. ._.__....__.- ~.-,
I
0.0
.
1.0
.....-._.. ---~------I--:.
2.0
._ _ _ _.._~ .9.___
'88
Sea tter 01 agram
-_.. . -...._....~.. ..-
- -------- - ...*.
2400 ;. .
--- ----_..-
**
*
*
*.
*
*
*
2000.
* *
* *
* ****
1600.
**
*
* *
*
*
.....-_.._-- -_..__...
*
**
. '.._-.- . ........_... .. . ...
-- ---.- -._----
1200. . *
*
..._-_..__....- _.. ......._. .. ,...__. ---------
800.
I
4000 .
6000.
._-.. -- ---- . _._........--
I
80ÖO.
. - - ..
10000.
_._-~.-:-
.__.,.. .1---0- -~r.
12000.
X2
.. . i 4000 .
89
5.20 (6). Yes, they are plausible since the hypothesized vector eo (denoted as . in the
plot) is inside the "95% confidence region. .
96li S1mullJeouB Cooldence Region for Wean Veclor
iiI
ii.
ii.
i .~
ii i
ii.
"
i.o
i ..
¥
i"
i' .
i, i
Ui
i' .
...
110
II .
'ii ,., ... 1'. ,.. "7 ...
iiu.
(11).
189 .822
UPPER
197 . 423
274.782
284.774
LOWER
Bonferroni C. i.:
Simultaneous C. i.:
189.422
197 .823
274. 25S
285.299
Simultaneous confidence intervals are larger than Bonferroni's confidence intervals. Simulfrom outside.
(c). Q~Q plots suggests non-normality of (Xii X2). Could try tra.nsforming XI.
taneous confidence intervals wil touch the simultaneous confidence region
Q-Q PLOT FOR X1
Q-Q PLOT FOR X2
310
-.
210
-.
300
.
200
..
.
)(
190
.
xN
.
---
.2
280
270
.
29
xN
..
.-
0
NORMAL SCORE
2
.. . .
'.
.
260
250
-1
. ..: .- . ...
280
270
..
260
. ..
180
r
./
..
300
J
290
.
..
.
..
310
. ..
250
-2
-1
0
NOMA SC-QRE
2
ISO 20
X1
.
90
5.21
HOTELLING T SQUARE - 9 .~218
P-VALUE 0.3616
T2 INTERVAL
xl
x2
x3
x4
x5
x6
N
2S
25
25
25
25
25
MEAN
0.84380
0.81832
1.79268
1.73484
0.70440
0.69384
STDEV
0.11402
0.10685
0.28347
0.26360
0.10756
0.10295
.742
.723
1.540
1. 499
.608
.602
.946
.914
2.046
Bonferroni
the
T2 intevals use the constant 4.465.
t
(
.778
.757
.642
.635
.786
.00417
)
TO
1. 629
1. 583
1. 970
.800
The
intervals use
BONFERRONI
TO
-
2.88 and
.909
.880
1. 95"6
1. 887
.766
.753
5.22
91
la). After eliminating outliers, the approximation to normality is improved.
a-a PLOT FOR X1
a-a PL-DT FOR X2
a-a PLOT FOR X3
30
18
2S
15
20
X
C/
W
. . .'
.
5
..
::
0
~
.'
10
a:
..
,.
_..
15
l-
-I
-2
0
..
10
111
.'.
..
--
x..
...
5
2
. .. .
-2
NOMA SCRE
..
14
12
10
,.'--
8
6
0
....
4
0
.1
2
-2
NOMA SCORE
::
~
.,.
0
-1
2
NORMA SCORE
l-
15
~
..
'.
10
. .'..
.
5
.
.l
8
...
10
5
..X
is
20
25
a:
W
..l::
a
l::
a::
..
ø. .
..
8
8
6
4
4
5
20
15
25
..
30
10
S
1S
a-a PLOT FOR X1
a-a PLOT FOR X2
a-a PLOT FOR X3
12
10
8
6
.. o.
..
..
..
.-
..-
~
. .
..
14
12
10
8
..
6
. .. .
4
2
.
2
0
-1
.2
14
..
.. ..
.
4
.
2
4
II
...
8 10
X
XI
x
..
. ...
6
0
2
,.
ILL
.. .
:. .
.
. ..
'"
.
x
14
12
10
.
6
II
4
4
4
8
8 10
X1
14
-I
0
2
NOMA SCRE
111
12
10
...""
.. ...
.
-2
iI
8
14
.1
14
'"
. ..
'"
NOM4 SCORE
l-
12
10
8
-_..
...
18
16
14
12
10
8
4
NORMA SCRE
II
10
I. .
.
X2
-2
~
xM
o.
X1
4
~
, ...
o
.
16
14
12
10
Xi
14
X
18
30
18
CJ
18
18
14
12
10
I. .
.
..
2
4
6 8 10
X2
14
92
l. Outliers remov.edi~
LOWER
UPPER
Bonferroni c. i.:
9.63
5.24
8.82
12.87
9.67
12.34
Simul taneous C. i.:
9.25
4.72
8.41
13.24
10.19
12.76
Simultaneous confidence intervals are larger than ßonferroni's confidence intervals.
(b) Full data set:
Bonferroni C. I.:
Simultaneous C. I.:
Lower Upper
9.79
15.33
5.78
10.55
8.65
12.44
9.16
5.23
8.21
15.96
11.09
12.87
93
5.23 a) The data appear to be multivanate normal as shown by the "straightness" of
the Q-Q plòts and chi-square plot below.
.
140 -
.
.c
't
CD
x
t'
:2
130 -
.
. .
.
.
..
.
.
.
.
140
.
. .
-
. .
.
. .
.
. .
or
0)
:i
Ul
lU
130
ID
.
.
.
.
120 -
.
-1
i
-2
-1
. I
i
0
1
.
120
2
NScMB
-2
-1
0
2
NScBH
~= .994
i- = .97'6
.
... .
55 110 -
-i
s:
. .
.
. .
C)
Ul
lU
m
100 -
.
90 -
.
.
.
. .
.
. .
-
.c
Ul
lU
50 -
Z
.
45 -
.
-T
.1
I
I
,
-2
-1
0
1
2
.
.
-2
'.
I
-1
0
!
r;= .995
10 -
.
5 _.
. ...
.. .
...
..
.. ...
. .
..
..
I
i
"U
5
.
.
.
.
. .
¡
10
.fe,4l.( -.5)/30)
i
2
NScNH
i. = .992
o -
.
.
.
NScBL
d¿)
.
C)
:i
.
.
.
.
94
5.23 (Continued)
b) Bonferroni 95% simultaneous confidence intervals (m = p = 4):
t29 (.05/8) = 2.663
MaxBrt:
BasHgth:
BasLngth:
NasHgt:
(128.87, 133.87)
(131.42, 135.78)
(96.32, 102.02)
(49.17, 51.89)
95% T simultaneous confidence intervals:
4(29) F (.05) = 3.496
26 4.26
MaxBrt:
BasHgt:
BasLngt:
NasHgth:
(128.08, 134.66)
(130.73, 136.47)
(95.43, 102.91)
(48.75, 52.31)
The Bonferroni intervals are slightly shorter than the T intervals.
9S
5.24 Individual X charts for the Madison, Wisconsin, Police Department data
LegalOT
ExtraOT
xbar
s
3557.8 ô06.5
LeL
UCL
5377.4
1478.4 1182.8 -2.070.0 5026.9
Holdover
2676.9 1207.7
COA 13563.6 1303.2
800.0 474.0
MeetOT
1738. 1
-946 . 2
6300 . 0
9654.0 17473.2
2222 . 1
-622. 1
use
use
L-CL = 0
LCL = 0
use LCL=O
The XBAR chart for x3 = holdover hours
0
ai
::
ii
;:
ii::
"C
":;
'e
.5
0
.0
CD
0
0
0
("
0
.0
0
..
.
.
.
.
.
.
.
...a................y........................;..........;..........................__......................;................................
.
. . .
----------------------------------------
.
2
4
6
8
10
12
14
16
Observation Number
The XBAR chart for x4 = COA hours
ai
::
ii
;:
ii::
"C
":;
'e
.5
0
0
0
,...
.0
0
a("
..
0
0
0
Q)
. . .
.
.
.
............................................................--;........................................................................................
.
. .
.
.
.
.
2
4
6
8
10
Observation Number
Both holdover and COA hours are stable and in control.
12
14
16
96
5.25 Quality ellpse and T2 chart for the holdover and COA overtime hours.
quality control 95% ellpse is
All points ar.e in control. The
1.37x 10-6(X3 - 2677)2 + 1.18 x 10-6(X4 - 13564)2
+1.80 X 1O-6(x3 - 2677)(X4 - 13564) =5.99.
The quality control 95% ellipse for
holdover hours and COA hours
00
0
r..
0
00
..
co
.
.
..
00
0
..It
0
in
0'I0
:i
0
..
J:
c(
0
u0 0
t'0
..
0
0
0
C\
..
00
0
T-
.
. .+
.
.
.
.
.
T-
-1000
0
1000
3000
5000
Holdover Hours
The 95% Tsq chart for holdover hours and COA hours
a:
r-
UCL = 5.991
ci .................. '''..n.....
............ ........ ..... ...... .._...... ..... ..............._.._...........__.
i:
in
t! 'It'
C\
o
97
5.26 T2 chart using the data on Xl = legal appearances overtime hours, X2 - extraordinary
event overtime hours, and X3 = holdover overtime hours. All points are in control.
The 99% Tsq chart based on x1, x2 and x3
o
..
................................................................................................................................................
.
CD
C'
~
co
v
N
o
5.27 The 95% prediction ellpse for X3 = holdover hours and X4 = COA hours is
1.37x 10-6(x3 - 2677)2 + 1.18 x 1O-6(x4 - 13564)2
+1.80x 1O-6(x3 - 2677)(X4 - 13564) = 8.51.
The 95% control ellpse for future holdover hours
and COA hours
0
..
0co0
0j
!!
:z
.
...
00
. .
. .+
.
0
..v
c(
0
()
0
..
0N0
o
o
o
o
..
-1000 0 1000
3000
Holdover Hours
5000
98
5.28 (a)
x=
-.506
.0626 .0616
.0474 .0083 .0197 .0031
-.207"
-.062
.0616 .0924
.0268 -.0008 .0228 .0155
.0474 .0268
.1446 .0078 .0211 -.0049
.0083 -.0008
.0078 .1086 .0221 .0066
.698
.0197 .0228
.0211 .0221 .3428 .0146
-.065
.0031 .0155
-.0049 .0066 .0146 .0366
-.032
s=
The fl char follows.
limit.
(b) Multivariate observations 20, 33,36,39 and 40 exceed the upper control
The individual variables that contribute significantly to the out of control data
points are indicated in the table below.
Point Variable P-Value
Grea ter Than UCL
20
33
Xl
X2
X3
X4
X5
X6
X4
X6
O. 0000
0.00.01
0.0000
0.0105
0.0210
0.0032
.0.0088
O. 0000
o . 0000
\) .0000
36
Xl
39
X2
X3
X4
X2
X4
X5
X6
40
XL
0.0000
X2
X3
X4
O. 0088
\). OO.QO
0.0343
0.0198
0.0001
0.0054
o . 000'0
0.0114
0.0-013
99
2 472' 2 29(6) .
5.29 T = 12. . Since T = 12.472 c: -- F6,24 (.05) = 7.25(2.51) = 18.2 , we do not
reject H 0 : ¡. = 0 at the 5% leveL.
5.30 (a) Large sample 95% Bonferroni intervals for the indicated means follow.
Multiplier is t49 (.05/2(6)):: z(.0042) = 2.635
Petroleum: .766:t 2.635(.9251,J) = .766:t .345 -7 (.421, 1.111)
Natural Gas: .508:t 2.635(.753/.J) = .508:t .282 -7 (.226, .790)
Coal: .438:t2.635(.4141.J) = .438:t.155 -7 (.283, .593)
Nuclear: .161:t 2.635(.207/.J) = .161 :t.076 -7 (.085, .237)
Total: 1.873:t 2.635(1.978/.J) = 1.873 :t.738 -7 (1.135, 2.611)
Petroleum - Natural Gas: .258:t2.635(.392/.J) = .258:t.146 -- (.112, .404)
(b) Large sample 95% simultaneous r intervals for the indicated means follow.
Multiplier is ~%;(.05) = .J9.49 = 3.081
Petroleum: .766:t3.081(.9251.J) = .766:t.404 -- (.362, 1.170)
Natural Gas: .508:t3.081(.753/.J) = .508:t.330 -- (.178, .838)
Coal: .438:t3.081(.414/.J) = .438:t.182 -- (.256, .620)
Nuclear: .161:t3.081(.207/.J) =.161:t.089 -- (.072, .250)
Total: 1.873:t 3.081(1.978/.J) = 1.873:t .863 -- (1.010, 2.736)
Petroleum - Natural Gas: .258:t 3.081(.392/.J) = .258:t .171-- (.087, .429)
Since the multiplier, 3.081, for the 95% simultaneous r intervals is larger than
given
interval is the same, the r intervals wil be wider than the Bonferroni intervals.
the multiplier, 2.635, for the Bonferroni intervals and everything else for a
100
5.31 (a) The power transformation ~ = 0 (i.e. logarthm) makes the duration
observations more nearly normaL. The power transformation t = -0.5
(i.e. reciprocal of square root) makes the man/machine time observations
more nearly normaL. (See Exercise 4.41.) For the transformed observations,
say Yi = In Xi' Y2 = 1/'¡ where Xl is duration and X2 is man/machine time,
- = p.171J
Y l .240
s = r .1513 -.0058J
l- .0058 .0018
, ,
S-i - r 7.524 23.905J
l23.905 624.527
The eigenvalues for S are Â. = .15153, Â. = .00160 with corresponding
eigenvectors ei = (.99925 - .03866), e2 = (.03866 .99925l Beginning at
center y, the axes of the 95% confidence ellpsoid are
maior axis:
:! IT
v Â.2(24)
F2 23 (.05) ei = :t.208el
..
r: 2(24)
:tvÂ.
F223(.OS)e2 =:t.021e2
mInor axis:
The ratio of
25(23) .
the lengths of
25(23) .
the major and minor axes, .416/.042 = 9.9, indicates
the confidence ellpse is elongated in the ei direction.
(b) t24 (.05/2(2)) = 2.391, so the 95% confidence intervals for the two component
the transformed observations) are:
means (of
Yi :tt24(.0125)¡; = 2.171:t2.391.J.1513 = 2.171:t.930 ~ (1.241, 3.101)
Y2 :tt24
(.0125)'¡ =.240:t2.391.J.0018 =.240:t.101 ~ (.139, .341)
Chapter 6
ii.1
WI
Ei9~nvalues andei9~nvectnrs of Sd are:
"1 = 449.778,
!1 = (.333, .943)
"2 = 168.082,
~~ = (.943, -.333)
Ellipse cent~rl!d at r = (-9.36,.13.27). Half length of major axis is
20.57 units. Half length of minor axis is 12.58 units. Major and minor
axes lie in :1 and !2 d;r~ctions, respetively.
Yes, the t.est answers the question: Is ô = 0 ins1tfe the 95i confi-
dence e 11 ipse 1
6.2 Using a critical value tn_i(cr/2p) = tio(O.0l25) = 2.6338,
UlWER
Bonferroni ~. I.:
-20 . 57
-2.97
Simul taneous 'C. I.:
-22 . 45
-5.70
UPPER
1.85
29.52
3.73
32.25
Simultaneous confidence intervals are larger than Bonferroni's confidence intervals.
6.3 The 95% Bonferroni intervals are
LOWER
Bonferroni 'C. i.:
Simultaneous C.I. :
UPPER
-21.92
-2.08
-3.31)
20.56
-23.70
-~ . 30
-5 .50
22.70
Since the hypothesize vector '6 = 0 (denoted as * in the plot) is outside the joint confidenæ
r.egion, we reject Ho : '6 = O. Bonferroni C.!. are consistent with this result. After the
elimination of the outlier, the difference between pairs became significant.
95% Simultaneous Conidence Region (or Della Vector
102
.3 0
M 20
U
1
2
10
M
U
2 0
2
- 10
-20 -10
-.3 0
MU11-MU21
o
6.3
Problem
6.4
(a). HoteHing's T2 _
10.215. Since the critical point with cr
Ho : ..
ó =...o.
(b).
Bonferroni C. I.:
T Simultaneous C. 1.:
..
1..
'.0
Lower
Uoner
-1.09
-0.04
-0.02
-1.18
-0.10
0.07
0.69
- 0.05 is 9.4'59, we reject
0.64
95% Cofidence Slips Ab the Me Vecor
...
...
..S
...
0.'
0.'
(o~O)
...
-0.'
-0.2
-0.4
-0."
-1.1i -1.5 -1.4 -1.3 -I.R -1.1 ....0 -o.S -0.. -0.7 _0.. -0.& -0.4 -0.8 -0.2 -e., 0.0 0.' .... o.a
..
ld ...."'1 -it
Figure 1: 95% Confidence Ellpse and
Diffence
'Simultaneous T2 Interv for the Mea
103
(c) The Q-Q plots for In(DiffBOD) and In(DiffSS) are shown below. Marginal
normality cannot be rejected for either variable. The.%2 plot is not straight
(with at least one apparent lJivariate outlier) and, although the sample size
"argue for bivariate normality.
(n =11) is small, it is diffcult to
a-a Plot
o.S
/'
....
ôo _o.s
//
-.,/
./
/////
../".
. .--~/
~/-
ê
..
i5
/
.. -I..
.//-'
~......,
.-......-
////_1.5 . r"''/
//
..../
//
_2.0
. ///..,,~
o 0.&
_1.5
lr' Qlt.. 1_
Q-Q Plots
1.25
1.00
..
0.76
~
0.60
..
0.25
CI
is
..
/.. //~'
.. /
//
//"/
_./
.....
../
_0.25
_0.50
/////"
/-,..
~/ ...//
../.....
../.-///-
o ..5
..1~...1_
Chi -squa-e Aot d th OderedDistcnce
d
.
0-11
,
3
,
-'-
4
.i
7
104
-1
.0--
6.5 a) H: Cii = 0 wher.e C = ('0
1
-~
). ~. = (~1'~2'~3) ·
-32.6)
i:x = (-11.2), CSt' = (55.5
- 6.9 -32.6
66.4
- -
T2 = n(Ci)' (ese' )-1 (ei) = 90.4; n = 40; q = 3
((~~~li)l) Fq_l.n_a+i(.05) = (3~~2 (3.25) = 6.67
o--
Since T2 = 90.4 ~ 6.67 reject H :Cii = 0
b) 951 simultaneous confidence intervals:
111 - 1-2: (46.1 - 57.3) :! -/6.67 J5~õ5 = -11.2 :! 3.0
1-2 - 1-3: ti.9 :! 3.3
111 - 1-3: -4.3 :! 3.3
The means are all different from one another.
IOS
6.'6
a)
Tr"eatment 2:
Sampl e mean vector
-3~2 J
(:l
sampl e covariance matrix
(-3;2
Trea tment 3:
Sampl e mean vector
samplè covariance matrix
(:) ;
-4/3 J
4/3
r~13
Spoled =
-1.4
( 1.6
b)
;1.~
TZ = (2-3, 4-2)
((1 + 1)
-1.4
(1.6
= 3.-88
r (:~J
("1 +n2-2)p _ (5)2 _
-("
p 1 ( .01) - 4 (18) - 45
1 +n2-p 1)- Fp'1n +n
2--
Since TZ = 3.88 ~ 45 do not reject HO=l2 -!!3 = ~ at the ci = .01
1 evel .
c). 99%simul taneous confi-dence intervals:
1121 - l1:n: (2-3) :! I4 Æ~+l)l.,- = -1 :16.5
1122 - l132: 2 :I 7.2
6.7
TZ = (74.4 201.6)
(45 + 55)
21505.5
( 1 1 (10963.!
21505.5 7;4.4
.
= 1'6 1
53661.3 _ 201 .'6 .
JJ-1( J
(ni +"2-2)P
1 FP'"l
+n 2-P1 ( .05)= 6 .~6
"1 +n2-p.
Since r2 = 16.1 ;) 6.26 reject HO:~l - t!2 = ~ at the ct = ..o level.
,. -1ldxi-it=
(- -).
&êrS
_ poo ~ - ;.,
106
.0026
(.001 7 J
6.8 a) For first variable:
trea t:nt
observation
(:
5
8
1
2
5
3
+
mean.
=
: 7)
=
(:
4
4
4
4
4
4
r.esidual
+
4) (2 2 2 2
4
+ 1 -1 a
+ -2 -2 -2
4
-1 -1 -1 -1
SS
· 1 92
mean
. SSobs = 246
. effect
SStr = 36
1 (0-1-12 20.-1
-2 J
SSres :: 18
For second variable:
5 55) (333 3
3 6 3 = 5 5 5 + -1 -1 -1
311355
(79 6 9 9) (5 5
SSobs = 402
5 5 -3 -3 -3 -3
SStr =84
SSmean = 300
3)(-1 1 ~2 1 'J
+ -1 2-1
1 -1 -1 1
SS
res· 18
Cross product contri butions:
275
48
240
-13
b) MANOVA tabl e:
Source of
Vari ation
SSP
Treatment
B = (36
Residual
W -
48
d.f.
48 J
-13J
- -13
rlB
3-1=2.
84
18
35)
Tota 1 (corr~ct~)
35 Hl2
(54
'5+3+4-3-9
n
107
* ~ 155
c) li = TäT = 4283 = .U362
Using Table 6.3 with p = Z and 9 = 3
(1 - IÃ \ (En 1 - 9 - ~ = 17 .02 .
\IK) 9-1) .
Since F4,16(.01) = 4.77 we:~onc1ude that treatmnt differences
exist at e = .01 l~vel.
Al ternat1vely, using Bartl ett' s procedure,
( (p+g.) (5) ( )
- n - 1 - 2 ) ln A* = - 12 - 1 - '2 1n .0362 = 28.i09
Since x;e .01) = 13.28 we again conclude treatment differences
exist at e = .01 level.
6.9 for!! matrix C
_ n..J n"J ..
a = 1 I: d. = C~ 1 1: x.) = C X
and
so
6.10
d. - a = C(x. - x)
..J"
-J" .
n- -J - -J - n-. ..J - ..J" .
. Sd =..1 r(d.-a)(d.-a)' = C(..1 r(x.-x)(x.-x)')C' = t:SC'
.. ... . . ..g
ei 1)'((xi-x)u1 + ... + (xg-x)u )
= x((x1-x)ni + ... + (ig -x)ngJ
= i(nix1 + ... + ngxg-x(ni + ....+ ng)).
= x(("l + ... + "g)i-x(ni +... +" )) = 0
. 9
108
6.11 l(~1'!:2,t) = L(~l ,t)l(~2';)
z~ ",)+nzlexp
2 lt ~;1
2 -1)
j 51.+( "2-1152)
=((
(", +"21p
(tr t-' ((",
+ ",(~, _~,l' t-'(~-~,l + "2(~2-~21' t-l(~2 - !:1)1
". _ A_
using (4-16) and (4-17). The likelihood is maximized with respect
to ~, and ~z at ~l = ~1 and ~2 = ~ respectively and with
. respect to * at
1
Qi+n2-~
12.12
i = n +n ((n1 -1)S, :l (nZ - 2)SZ) = n +n
S
poo 1 l!d
(for the maximization with respect to ; see Result 4.10 with
n,+nZ
b = 2 and B = (n1 -1)S, + (nZ - 2)52)
6.13 . a) and b) For firs.t variab1 e:
. factor 1
Observation = mean + effect +
factor 2 .. residual
effect
,. -2 4 -3J + 2 -1 0-1
-3 -4 3 -4.J 1 1 1 1 . -3 -3 -3 -3
1 -2 4 -3 -2 0 1 ,.
( : -: : ~l = (~. ~ ~ ~J + (-~ -~ -~ .~~J + (1 .:Z 4 -3'1 (0 1 -1 . OJ
SStot = 220 SSmean = 12
SSfac 1 = 104
SSfaC Z = 90
SSres=14
For second variable:
8233 = 3333 +
~ 6
-5T -3
-6 (3
3 33 33 3
(8
Z OJ
3J
SSt~t = 44()
1 -ZJ (-3 0 3 OJ
1 1 1 1 + 3-2 1 -2 + 1 ° -2 1
-6 (3-2
3 -2 1 -2 2 ° -1 -1
(-65 -6
5 -6
5 5)
SSmean = 1\) SSfac 1 = 248
SSfac Z:i 54
SSres · 3Q
109
Sum of ~ross products:
SCP tot = SCP m~an + SCP fac 1 + SCP fac 2 + SCP r~s
227 = 36 + 148 + 51 - 8
c) MANOVA table:
Source of
Variation
SSP
d.f.
9 ...1"=3 -1 =2
Factor 1
148 1481
248J
l04
b-l=4-1-3
Factor 2
51 51)
54
( 90'
(g-l )( b-l) = 1)
Residual
(14 ..8)
-8 .3 0
Total (Corrected)
r 208 1911
. gb - 1 = 11
L191 332J
d) We reject HO:!l =!2 = !3 = ~ at a = .05 l~vel since
((g-l )(b-l) _ (p+l2- (Q-1
= -(61_ 3-2)ln.
r~s
2 \ ))J1nA*
SSP fac
+( SSP
res
, ss
i. IÙ
ë: -5.5 1n ( 356 ) = 19.87 ~ X:( .05) = 9.49
.13204'
and concl ude there are factor 1 effects.
We al so reject HO:~l = ~2 = ~3 = ~4 = ~ at the ~ = .05 level
since
110
. K ~ res
_ ((g-l )(b-l) _ (p+l - 2
(b-l))R.nti*
-(6 _ 3-3)R.n~
res I \)"
2" i==SSPf
., +Iss,p
SSP
r.
0: -6 R.n ( 356 ~ = 17.77 ~ X~ (.05) = 12.59'
6887 .
and concl ude there are factor 2 effe~ts.
6.14 b) MANOVA Tabl e:
Source of
d."f.
SSP
Variation
1841
2
Factor 1
184
(496
208J
24)
'3
Factor 2
.24
36
.0
(32
4:)
(36
Interaction
.6
~S41
12
Residual
Total
c) Since
-84
(312
400J
(Corrected) 23 "
1 24 124J
688
(876
. G .
. I SSP I
-(gb(n-l) - (p+l - (g-l Hb-l n/2)R.nA*. =lSSP1,
-13 5tn
tn+ res
SSP
res
.. -13.5R.n( .808) = 2.88 0( xi: (.05) = 21.03 we ~.! reject
HO:!l1 = !12 = ... = !34 = ~ (no i!lteraction effects) a~ t~
a = .05 level.
111
Si nc~
-(gb(n-l)-(p+i-Cg-1))/2)R-nA*=-11.SLnfac
lssp 1
~e~sp
res r
. ( lssp 1 )
= -;1.SR-n(.24.47) =16.19 ~ XH.05) = 9.49 we rejet:t
-2 -3 -
HO:"t_1 = "C = "t = 0 (no factor 1 effects) at
the a. = .05
1 eve 1 .
Since
.
~
ISSPresl
)
-(gb(n-1)-(p+l-(b-l ))/2)Wi* :I -lZl lssp + SSP r
fac 2 r..s
:: -12R.n(.7949) =2.1fi 0( XU.05) = 12."59 we do not reJect
HO:~l = ~2 ? ~3 = ~4 = ~ (no factor 2 effects) at.the
a = . OS 1 eve 1 .
112
6.15 Example "6.1l. g. b · 2, n · '5;
a) For "0:!1 .;2 .~, A* D .3819
Since
'*
-(gb(n-l)-(p+l-(g-l))/2)tn A =-14.51n(.3819):2
· 13.96 :) X: (.05) = 7.81 .
we reject HO at a = .05 level. For HO: ~1 = ~2 = ~. 14*'= .'5230 and
:~4.5~n (.5230) :0 9~40. Again we reject "0 at a. '.05 level.
These results are consistent with the exact F tests.
-1 a
a--
6.16 H : Cll = 0; Hi: C!: 'I Q where,
c=U
1 -1
o 1
-1~J
.
. Suniary stati stics:
1 906.1
x
=
1749.5
1509.1
1725.0
1 05"625
.
,
S =
94759
87249
94268
1 01 761
761 6ô
81193
91809
90333
1043Z9
- -
r2 = n ( Cx p ( CSC i ) -1 (Cx) = 254.7
. ~(~:~ii)l) Fq_l.n_q+1(a) = (3~~it11F3,27(.05) = 9.54
Since T2 = 254.7 ,;) 9.54 we reject "a at C1 = .OS level.
95i simultaneous confidence interval for -dynamic. versus .static.
means h.11 + ll2) - (1.3 + 1.4) is, with :' = (1 1 -1 -1).
- I
( n-1
q-1
) ()
; I~ :t
(Il~q+
1) fHq-1
,n-q+l
a I rc
=n
= 421.5 :: 174.5 -- (247.
59ó)
113
Arabic G)
6.17 (a)
Q)
Format
ø
Words ø
Different
Same
Party
Effects
Contrast
Party main:
(¡.2 + ¡.4) - (#¡ + #3)
Format main:
(¡.3 + #4) - (¡¡ + #2)
Interaction:
(#2 + #3) - (#1 + #4)
Contrast matrx:
c= -1
-1
(-1
~1 ;1 J
S. T2 31(3) .
ince = 135.9;: -(2.93) = 9.40, reject H 0 : C,u = 0 (no treatment-effects)
at the 5% leveL.
29
(b) 95% simultaneous T intervals for the contrasts:
Party main effect:
-206.4:t.J9.40 20,598.6 -7~-280.3, -125.1)
32
Format main effect:
-307:t.J9.40 42,939.5 -7(-411.4, -186.9)
32
Interaction effect:
22.4:t.J9.40~9,8l8.5 -7 (-32.3, 75.0)
32
No interaction effect. Party effect-"different" resonses slower than
"same" responses. Format effect-"words" slower than "Arabic".
(c) The M model of numerical cognition is a reasonable population model for the scores.
(d) The multivarate normal model is a reasonable model for the scoresconesponding to
the party contrast, the format contrast and the interaction contrast.
114
6.18
Female turtle
Male turtle
A chi-square plot of the ordered distances
(0
. .
10
-ê ~
"C
..
C"
Q)
E
0
.
C\
..
...
...
0
...
. ...
.
..
.
co
.
C'
CD
A chi-square plot of the ordered distances
.
.
C'
~ (0
. . .
.
'C
..
. . .
Q)
~
Q)
"E
..
0
C\
0
0 . ....
4
2
6
8
10
..
0
. .
.......
2
6
4
qchisq
.
0
:2 ui
ë,
c
Q)
..
-i: ~.
:: co
(0
-2
.
..
.. ...
..
-..
..
ëi
i: ,.
Q)
.
.s
. ..
::~
(0
. .
2
1
-2
Quantiles of Standard Normal
..
... ....
..
. .
.
0
.
.
.. .
..
-1
10
CJ
co
.
...
.
..
.
.... ...
8
qchisq
0
-1
2
1
Quantiles of Standard Normal
.
.
co
..
~
.~ (0
-g ..
~
..
-'C ...
-.s.~ ~~
.. . .
:2
.
.
-2
...
.
.
....
... .
...
:5 10
..
0
-1
.
(0
1
2
~
~
.
-
..-C)
Q) .
.s
.- CJ
S ('
..
,.
M
.
-2
.
.
..
. .....
.
.
..
.. .
..
.
..
.. e.
.
.
-2
Quantiles of Standard Normal
..
..
.
..
..
.
.
. ..
...
.
0
-1
-2
1
Quantiles of Standard Normal
.
.
-1
1
0
2
Quantile of Stanar Noral
10
co
M
-,.
-.
10
-§ C"
ëü
..-10
.s ta
('
10
ia
('
..
.
~
..
...
....
...
.
.
.
. ..
.
.
.
-1
t)
1
Quantile ófStandard Norm::l
2
115
mean vector for f~males:
mean vector for males:
X1BAR
SPOOLED
X2BAR
4.9006593
4. 7254436
4. 6229089
3. 9402858
4.4775738
3.7031858
0.0187388 0.0140655 0.0165386
0.0140655 0.0113036 0.0127148
0.0165386 0.0127148 0.0158563
TSQ
CVTSQ F CVF PVALUE
85.052001
8.833461 27.118029 2.8164658 4. 355E-1 0
linear combination most responsible for rej ection
of HO has coeffici.ent vector:
COEFFVEC
- 43. 72677
-8.710687
67.546415
95% simultaneous CI for the difference
in female and male means
Bonferroni CI
LOWER UPPER
0.0577676 0.2926638
0.0541167 0.2365537
0.1290622 0.3451377
LOWER UPPeR
o .07ß8599 0.2735714
0.0689451 0.2217252
0.1466248 0.3275751
116
6.19
a)
~1 = 8.113 i :z = (~~:~:J i.
9.590
_ (12.219J
18. Hi8
223.0134 12.3664 2.9066
51 =
17.5441 4.7731
13.9'633
Sz = 25.8512 7.6857
6543
( 4.3623 . .7599 46.
2.3'621
J;
Spooled = 20.7458 5.8960
'( 15.8112 7.855026.5750
2.6959 J'
(('1
+ pool
l)S ed
)-1 =
n1, n2
.8745 -. 1525
. 5640 ,
L--i'0939 -.4084 -,0203)
,HO: lh - !!Z' = 2
, ((
)-1 (- ~l
-) -:2
= St'.92
Since T2 =(-:1-)-:2
'n 1+ 1
n2) Spooled
:) (n1+n£-2)p . _ (57)(3)
(ni+nZ-p-l) Fp,ni+n2-p-,t.Ol) - 55 F3.SS(.01) = 13.
we reject HO at the a = .01 1 evel. Thereis a diff~rence in the
(mean) cost vectors betw.een gaso1 ine trucks and dies.el trucks.
wax x = -1.ae
,a.) '" _s-l
(- -)
c: pool.ed
-1 - (
-2 3.5,8 J .
-4.48
117
c) 99% simul taneous conf;~ence interval sara:
~ll - ~21: 2.113 t 3.790
~12 - ~22: -2.650:! 4.341
~13 - ~23: -8.578:! 4.913
dl Assumpti on ti = t~.
Since 51 and 52 are quite differant, it may not be reasonab1.e
to pool. However, using "large sample" theory (n1 = 36~ n2 = 23)
wa have, by Result 6.4,
- - )) r: 1 l' )-1 (- - ( )) 1
(~l -.~2 - (~1 - ~2 'Lñ1 51 +ñ2 52 ~1 - ~2 - !:l -!:2 - xp
5inca
(- -) I ( 1 1 )-1 (- -) 2 ( )
. ~1 - :2 ñ1 5, + "2 S2 ~l - ~2 = 43.15 ~)(3 .01 = 11.34
we reject HO: ~l - ~2 = ~ at the a = .01 level. Thisis consistent with the result in part (a).
118
6.20
(I)
31.
260
260
L
a
240
I
1
2201
m
200i
160
.
.
..
.
....
. .
.
. .
... . . .... . ..I. ..
.. .
. .
260
.
..
..
.
300
280
'" i ngm
(b) The output below shows that the analysis does not differ when we delete the
observtion 31 or when we consider it equals 184. Both tests reject the null
hypothesis of equal mean difference. The most critical
linear combination
leading to the rejection of Ho has coeffcient vecor (-3.490238; 2.07955)'
and the the linear combination most responsible for the rejection of Ho is
the Tail difference.
(c) Results below.
Comparing Mean Vectors from Tvo Populations
rObS. 31 Delete~
T2 C
25.005014 5.9914645
Reject HO. There is mean difference
'951. simultaneous confidence intervals,:
LAELCI
Mean Diff. 1:
Mean Diff. 2:
LICIMD
LSCIMD
-11.76436 -1.161905
-5 . 985685 8. 3392202
RESULT
Coefficient Vector:
COEF
-3.490238
2.07955
(Tail difference)
(Wing difference)
119
~omparing Mean Vectors from Two Populations
1,,~\ ,i
lObs. 31 = 184.
T2 C
25.662531 5.9914645
Reject HO. There is mean difference
957'simultaneous confidence intervals:
LICIMD
LABELCI
Mean Diff. 1:
Mean Diif. 2:
LSCIMD
-11.78669 -1.27998
-6.003431 8.1812088
REULT
COEF
Coefficient Vector:
-3.574268
2. 1220203
..s.
95% Cofidence Ellips Ab th Me Veda
-:61 ..e.
~.
5
..
.. ..
.-.~ .
::
-eo
"f iL\" \ t."'
(d) Female birds ai~ g~flerally larger, since the confidence intervl bounds for
difference in Tails (Male - Female) are negative and the
confidence intervl
for difference in Wings includes zero, indicating no significance difference.
120
6.21 (a) The (4,2) and (4,4) entri~s in 51 and 52 differ .con-
siderably. Howev~r, "1 = n2 so the large sample approximation amounts to pooling.
( b) H 0 : ~1 - ~2 = ~ and H1: ~1 - ~2 t ~
T2 = 15.830 :) (3~~(4) F4,3S(.OS) = 11.47
so we reject HO at the ~. .OS level.
( c)
x ) -3.74
= .16
,. S-l
a
a:ed(X-1 _ -2
_ pool
. .01
(-.241
121
(d) Looking at the coeffic1.ents â1'Sii.pooled. whieh apply to
the standardi zed variables. we see that X2: long term interest
rate has the largest coefficient and therefore might be
useful in -classifying a bond as 'lhigh" or "mediumlt quality.
4+16
(e) From (b), T2 = 15.830. Have p = 4 and v = = 37.344 so, at the 5% level, the
.53556
critical value is
vp F (.05) 37.344(4) F (.05)=149.376(2.647)=11.513
v - p + 1 p,v-p+1 37.344 - 4 + 1 4,37.344-4+1 34.344
Since T2 = 15.830 ::11.513, reject H 0: I! -!J2 = 0, the same conclusion reached in
(b). Notice the critical value here is only slightly larger than the critical value in (b).
6.22 (a) The sample means for female and male are:
¡ 0.3136 J ( 0.3972 J
_
jS8' XM
_ 5.3296
XF'.1.1
= 2.3152
= 3.6876 .
38.1548 49.3404
The Hotellng's T2 = 96.487 ). 11.00 where 11.00 is a critical point corresponding
to cr = 0.0~5. Therefore, we reject Ho : J.i - J.2 = O. The coeffcient of the linear
combination of most responsible for rejection is (-95.600,6.145,5.737, -0.762)'.
(b) The 95% simultaneous C. 1. for female mean -male mean:
¡ -0.1697.234, 0.0025.2336 J
-1.4650835, 1.16348346
-1.87"60572, -0.8687428
-17.032834, -5.3383659
(c) \Ve cannot extend the obtained result to the population of persons in their midtwenties. Firstly this was a self selected sample of volunteers (rrienàs) and is not
even a random sample of graduate students. Further, graduate students are -probably
more sedentary than the typical persons of their age.
122
6.23 n1 = n2 = n3 = 50;p = 2, 9 = 3 (~epal width and petal Width)
responses only!
.30~ 1 .18576
~l = (3.428 J; S =. (.143£4 -.00474 J
x =
-2
· 0418 J
.326 J ;
( 1U70
~3 =
2.026 J
(2.974
.,
S2 =
(.09860
.03920
.0471i.4 J
S =
3
(.1 0368
.07563
NAllOVA Table:
Source
Trea tment
d.f.
SSP
-21 .820 J
B = (11.344
75.352
2
4.125 J
R.esidua1
. W = (16.950
14.729
Total
B+W = .
(28.294
.
232.64~
A =* ~ -l
= 2235.64
-17 ·
.147
695J
90.081 .
149
.104
Since (rni-p-2\ (1 - IÃ) ~ !.
P ~ IA 153.3 ~ 2.37 - F4 .292( .OS)
"Ie rej.ect Ho: !l =!2 =!3 at th~ ci. .05 level.
123
6.24 Wilks'
lambda: A* = .8301. Sinceg= 3,(90-4-2'(1~) = 2.049 is anF
4 A .8301
value with 8 and I~8 degrees of freedom. Since p-value = P(F:; 2.049) = .044, we
would
just reject the null hypothis Ho :.1"1 =!2 =.r3 = Q at the 5% level implyig
there is a time period effect.
Fstatistics andp-values for ANOVA's:
F p-value
MaxBrth:
BasHght:
BasLgt:
3.66 .030
0.47 .629
3.84 .025
NasHght:
O.LU .901
Any differences over time periods are probably due to changes in maximum breath
of skull (Maxrth) and basialveolar lengt of skull (BasLgt).
95% Bonferroni simultaneous intervals:
t87(.05/24) = 2.94
BasBrth
BasH;Et
BasLgth
m = pg(g-I)/2 =12,
£11 -£21 :
-1:t 2.94 1785.4(2- + 2-) -- -1:t 3.44
£11 - 'l31 :
-3.1:t 3.44
£21 -£31 :
- 2.1:t 3.44
£12 -£22 :
0.9:t 2.94 1924.3(2- + 2-) -- 0.9:t 3.57
£12 -£32 :
- 0.2:t 3.57
£22 - £32 :
- 1.1:t 3.57
'l13 - £23 :
0.lO:t2.94 2153(2-+2-) -- 0.1O:t3.78
87 . 30 30
87 30 30
87 30 30
'l13 - £33: 3.14:t 3.78
£23 - £33: 3.03:t 3.78
NasH T" - T,,: 0.30 :t 2.94 /840.2 (2- + 2-) -- 0.30:t 2.36
V 87 30 30
'l14 - £34: - 0..o3:t 2.36
£24 - £34: - 0.33:t 2.36
size over
time is marginal. If-changes exist, then these changes might be in maximum breath
and basialveolar lengthofskull frm time periods 1 to 3.
All the
simultaneous intervals include O. Evidence for changes in skull
The usual MA~OV A assumptions appear to be satisfied for thse data.
124
6.25
Without transftlrming the data, A * =IWI =.i 159 and F = 18.98.
IB+WI
Afer transformation, A * :: .1198 and F = 18.52. ~ .FO,98 (.05) = 1.93
There is a clear need for transforming the data to make the hypothesis tenable.
6.26 To test for paralle11 sm, consider H01: C~l = C~2 with C giv~n
by (.6-61).
C(~l - ~2) = - .167 ;
.947
2.014
(CS
c1r1 =
poo 1 es
.616J
1 . 144
L.674
.036
(-- .413J
2.341
11 = 9.58 ;) cZ = 8.0., we reject HO at the 11 = .05 level. The
excess electri~al usage of the test group was much low~r than that
of the control group for the 11 A.rl.. 1 P.M. and 3 P.M. hours.
The s imi 1 ar 9 A.M~ usage for the two groups contradi cts the
parallelism hypothesis. .
6.27 a) . Plots of the husband and wife profiles look similar but seem
di sparate for the 1 evel of acompanionat~ lnve' tha t you feel
for your partneru.
b) Parall el ism hypothesis HO: C~l = ~2 with C given by
(ó~l).
C (~1 - ~2) = -. 17 i
(- ·.33
13 J
.733
.870
CSpool~dCI
= (.685
.029 J
-.028
.095
fo r a = . 05, c 2 = 8.7 ( see (6-62)). Since
T1 = 19.58 ;) c~ = 8.7 we reject Ha at the a. .~ level.
125
"' .. ;t
6.28 T2 = 106.13 ~ 16.59. \Ve reject Ho :¡.i - J12 = 0 at 5% significance leveL. There
is a significant difference in the two species.
Sample Mean for L. torrens and L .carteri :
L. torrens
96.457
42..914
35.371
14.514
25 .629
9.571
9.714
L. carteri
99 . 343
43 . 743
39.314
14.657
30.000
9.657
9.371
Difference
-2.886
-0 .829
-3.943
-0.143
-4.371
-0 .086
o .343
Pooled Sample Covariance Matrix:
36.008
2.426 2.649
1.053 0.934
6.437 o . 692 1. 615 0.211 0.671
3.039 2.407 o . 274 0.229
13.767 0.565 o . 637
1. 213 0.914
0.990
14.595 6.078
16.639 2.764
3.675 9.573
2.992 6.101
Linear Combination of most responsible for rejection
of Ho: L. torrens mean - L. carteri mean = 0 is
(0.006,0.151, -0.854, 0.268, -~.383, -2.187, 2.971)'
951. S imul taneous C. I. for L. torrens mean - L.carteri mean:
UPPER
LOWER
-8 . 73
-4.80
-6.41
-1.84
-7.98
-1.16
-0 . 63
2.96
3.14
-1.47
1.55
-0 . 76
0.99
1.31
The third and fifth components are most responsible for rejecting Ho. The X2 plots look
fairy straight.
CHI-SQUARE
CHI-SQUARE PLOT FOR Lcarteri
fOR L.torr~ns
PLOT
'5
15
ä!
'"
:;
w
a:
'"
:;
'0
10
~
~
0
51
..:i
0
5
5
'5
'0
5
25
20
15
10
5
o
20
25
o-SOARE
O-SOUARE
6.29
(a).
S
XBAR
o .02548
o .05784
Summary Statistics:
0.01056
0.00366259 0.00482862 O. 0~154159
0.00482862 0.01628931 0.00304801
0.00154159 0.00304801 0.00602526
IIotellng's T2 = 5.946. The critical point is 9.979 and we fail to reject Ho :/£1 - Jl2 = 0 at
5% significance leveL.
(b). (e).
LOWER
-0.0057
-0.0079
-0.0294
Bonferroni C. i.:
Simultaneous C. i.:
UPPER
o . 0566
0.1235
o .0505
-0.0128
-0.0228
o . 0637
-0 .0385
o . 0596
0.1385
6.30
HOTELLING T SQUARE -
P-VALUE 0 .3616
9.0218
T2 INTERVAL
xl
x2
x3
x4
xS
xli
The
N
24
24
24
24
24
24
MEAN
0.00012
-0.00325
-0.0072
-0.0123
0.01513
o . 00017
.Bonfer~oni
STDEV
intervals use
the T in"tevals use
-.0443
-.0286
0.04817
0.02751
0.1030
0.0625
0.03074
0.04689
TO
- .1020
-.0701
-.01'30
- .0430
t
the constan-t
(
.0445
.0221
.0876
.0455
.0436
.0434
.(H)417
4.516.
BONFERRONI
)
-
TO
-.0283 .~285
-.0195 .0130
-.0679 .0535
-.0493 .0247
-.0'030 .0333
-.0275 ."0278
2.89 and
126
127
6.31 (8) Two-factor MANOVA of peanuts data
E = Error SSkCP Matrix
XL
X2
X2 49.365
X3
76.48
49.3"65
XL 104.205
I3
76.48
121.995
94.835
352.105
121. 995
XL X2
XL 0.7008333333 -10.6575
H = Type III SS&CP Matrix for FACTORl
X2 -10.6575 162.0675
X3 7.12916666"67 -108.4125
( Loco.+~ot')
X3
7.1291666667
-108.4125
72.520833333
Manova Test Criteria and Exact F Statistics for
the Hypothesis of no Overall FACTORl Effect
H = Type III SSkCP Matrix for FACTORl E = Error SSkCP Matrix
S=l
M=0.5
Statistic
N=l
Value
0.10651620
0.89348380
Wilks' Lambda
Pillai ' s Trace
Hoteiiing-La~iey Trace
Roy's Greatest Root
F
Num DF
3
3
3
3
11.1843
8 . 38824348
11. 1843
11. 1843
8.38824348
11.1843
Den DF
Pr ~ F
4
4
4
0.0205
4
o .0205
o .0205
o .OQ05
XL X2 X3
H = Type III SSkCP Matrix for FACTOR2. (Vat'~e.t~)
XL 196.115 365.1825 42.6275
X2 365.1825 1089.015 414.655
X3 42.6275 414.655 284.101666"67
Manova Test Criteria and F Approximations for
the Hypothesis of no Overall FACTOR2 Effect
H = Type III SS&CP Matrix for FACTOR2 E = Error SSkCP Matrix
S=2 M=O N=l
Statistic
Wilks' Lambda
Pillai's Trace
Hoteiiing-La~iey Trace
Roy's Greatest Root
Value
0.01244417
1.70910921
10.6191
21.375"67504
18.187"61127
10.6878
30.3127
XL X2
F
Num DF
Den DF fir ~ F
6
6
6
3
10 0.0011
6 0.0055
9 .7924
H = Type III SS&CP Matrix for FACTOR1*FACTOR2
XL
12
X3
205.10166£67 363.6675
3153 .~675 760.695
1~7 . 7~583333 254 . 22
X3
1'07.78583333
254.22
~5 . 95166"667
8 0..0019
5 0...012
128
Manova Test ~riteria and F Approximations for
the Hypothesis of no Overall FACTOR1*FACTOR2 Effect
H = Type III SS&CP Matrix for FACTOR1*FACTOR2 E = Error SS&CP Matrix
S=2 M=O N=1
Statistic
Pillai l s Trace
Value
0.07429984
1.29086073
Hotelling-La~iey Trace
7 . 54429038
Roy l s Createst Root
6.82409388
Wilks J Lambda
F
Num OF
3.5582
3.0339
3.7721
11.3735
6
6
6
3
Den DF Pr) F
8 0.0508
10 0.0587
6 0.0655
5 0.0113
seem large in absolute value, but
(b) The residuals for X2 at location 2 for variety 5
Q-Q plots of residuals indicate that univariate normality -cannot be rejected for all
three variables.
1 PRE2 RES2 PRE3 RES3
CODE FACTOR1 FACTOR2 PRED1 RES
a 1 5 194.80 0.50 160.40 -7.30 52.55 -1.15
a 1 5 194.80 -0.50 160.40 7.30 52.55 1.15
b 2 5 185.05 4.65 130.30 9.20 49.95 5.55
b 2 5 185.05 -4.65 130.30 -9.20 49.95 -5.55
c 1 6 199.45 3.55 161.40 -4.60 47.80 2.00
c 1 6 199.45 -3.55 161.40 4.$0 47.80 -2.00
d
d
e
e
f
f
2
2
1
1
2
2
6
6
8
8
8
8
200.15 2.55 1Q3. 95 2.15 57.25 3.15
200.15 -2.55 163.95 -2.15 57.25 -3.15
190.25 3.25 1"64.80 -0.30 58.20 -0.40
190.25 -3.25 1$4.80 0.30 58.20 0.40
200.75 0.75 170.30 -3.50 66.10 -1.10
200.75 -0.75 170.30 3.5066.10 1.10
Figu i: Q-Q P1ø - Red-lfot Yied
..
//"~//
..
/'
--
-.-
--/
,/
*//./
~y/-/
/-
./ ./~
.
--'I -i.._
"""/~
129
..
2: Q-Q Plot - Residual for Sound Mature Kernels /' .,
Figure
..//' /~/",/'
'//
.~.//-
.-......
.~~."'/.;
.' .'
.,/
//,,/
//'
//
/'
.../+
/~/
,... ..
.
_10....1_
//. //.'/'
..","
Figure 3: Q_Q Plot - Reidual for Seed Size
.../'
.
~.
..//,,,/
/ / /'
,//
'/'
.....
/,,/
y'/' /
../ ..
..:./ /,A
..4/.....
...'"
..,".'"
-iI a-...._
(c) Univariate two factor ANOV As follow. *Evidence of variety effect and, for Xl = yield
variety interaction.
and X2 = sound mature kernel, a location
Dependent Variable :
yield;
Sum of
OF
Squares
Mean Square
Model
5
401 .9175000
80.3835000
Error
6
1 04 . 2050000
17.3675000
11
506 . 1225000
Source
Corrected Total
Source
location
variety
location*variety
F
R-Square
Coeff Var
Root MSE
yield Mean
O. 794111
2.136324
4.167433
195.0750
OF
1
2
2
Type III 55
Mean Square
o . 7008333
196 . 11 50000
0.7008333
98.0575000
205. 1016667
102.5"508333
F
Value
Pr ~ F
4.63
0.0446
Value
Pr ~ F
0.04
5.65
5.90
0.8474
0.0418
O. 038~
130
Dependent Variable: sdmatker
Sum of
OF
~quares
Mean Square
F Value
Pr :. F
Model
5
~031 .777500
406 . 355500
6.92
0.0177
Error
6
352. 105000
58.684167
11
2383 . 882500
Source
Corrected Total
A-Square
Coeff Var
Aoot MSE
sdmatker Mean
o . 852298
4 . 832398
7 . 660559
158.5250
OF
Source
location
variety
location*variety
1
2
2
Type II I SS
Mean Square
162.067500
1089.015000
780.695000
162.067500
544.507500
390.347500
F
Value
Pr :. F
2.76
9.28
6.65
O. 1476
Value
Pr :. F
5.60
o . 0292
Value
Pr :. F
4.59
8.99
2.72
o . 0759
0.0146
0.0300.
The GLM Procedure
Dependent Variable: seedsize
Sum of
OF
Squares
Mean Square
Model
5
442.5741667
88.5148333
Error
6
94 . 8350000
15 . 8058333
11
537 .4091667
Source
Corrected Total
F
A-Square
Coeff Var
Aoot MSE
seedsize Mean
o . 823533
7.188166
3.975655
55 . 30833
Source
location
variety
location*variety
OF
Type II I SS
Mean Square
1
72 . 5208333
72.5208333
142.0508333
42.9758333
2
2
284.1016667
85.9516667
F
0.0157
0.1443
131
(d) Bonferroni ~simultaneous comparisoRs of va-ri.ety.
differ, and they differ only on X3.
Only varieties 5 and 8
Bonferroni (Dun) T tests for variable: XL
Alpha= O. Q5 Confiden~e= 0.95 df= 8 MSE= 38.66333
~ritica1 Value of T= 3.01576
Minimum Significant Difference= 13.26
'***' .
Comparisons si~ificant at the 0.05 level are indicated by
Simultaneous
Simutaneous
Difference
Upper
Confidence
Limit
Between
Confidence
Means
Limi t
-8 . 960
-3 . 385
-17 . 560
4.300
9.875
17 .560
-4 . 300
-7.685
-23.135
-18.835
5.575
-9 .875
-5 .575
Lower
F ACTOR2
Compari son
6-8
6-5
8-6
8-5
5-6
5-8
23.135
8.960
18.835
3.385
7.685
Bonferroni \Dun) T tests for variable: X2
Alpha= 0.05 Confidence= 0.95 df= 8 MSE= 141.6
Cri tical Value of T= 3.01576
Minimum Significant Differen~e= 25.375
Comparisons significant at the 0.05 level are indicated by '***'.
F ACTOR2
Difference
Upper
Confidence
Between
Confidence
Limit
Comparison
8-6
8-5
6-8
6-5
5-8
5-6
Simultaneous
Simul taneous
Lower
Limi t
Means
-20. '500
4.875
30 . 250
-3. 175
22 . 200
47.575
-30.250
-8.050
-47.575
-42.700
-4. 875
20 . 500
17 . 325
42.700
3.175
8.050
-22.200
-17.325
Bonferroni (Dun) T tests for variable: X3
Alpha= 0.05 Confidence= 0.95 dr= 8 MSE= 22.59833
Critical Value of T= 3.01576
Minimum Significant Difference= 10.137
-Comparis.ons significant at
the 0.0"5 level are indicated by '***'. .
Simultaneous
Lower
Comparison
Confidence
Limit
FACTOR2
8
8
6
6
5
i:
Simultaneous
Difference
Upper
Between
.confidence
Means
Limi t
- 6
-0.512
9.625
19.7'62
- 5
o . 763
- 8
-19.7"62
1'0.900
-9."625
21.037
0.512
- S
-8.862
1.275
11. 412
-21.'037
- HL 900
_1 "7i:
-0 . 763
- 8
- ~
-11
.11')
R
Qi:"
***
***
132
6.32 (a) MADV A f-or Species: Wilks' lambda A~ = .00823
F= 5.011; p-value = P( F-; 5.011) = .173
F4,2 (.05) = 19.25
'Species effects
Do not reject Ho: No
MADV A for Nutrient: Wilk'S' lambda A~ = .31599
F = 1.082; p-value = P( F -; 1.082) = .562
F2,l (.05) = 199.5
Do not reject Ho: No nutrent effects
(b) Minitab output for the two-way ANOV A's:
560cM
Analysis of Variance for 56QCM
Source
Spec
Nutrient
Error
Total
DF
2
1
2
5
SS
MS
8; 260
23.738
8.260
2.3£1
47.476
4.722
60.458
F P
10 . 06 0 . 09\l
3.50 0.202
720cM.
Analysis of Variance for 720CM
Source
Spec
Nutrient
Error
Total
DF
2
1
2
5
SS
2£2.239
4.489
9.099
275.827
MS
131.119
4.489
4.550
F
28.82
0.99
P
0.034
0.425
The ANDV A results are mostly consistent with the MANDV A results. The
exception is for 720CM where there appears to be Species effects. A look
at the data suggests the spectral reflectance of
Japanse larch (JL) at 720
nanometers is somewhat larger than the reflectance
species (SS and LP) regardless of
the other two
nutrent leveL. This difference is not as
of
apparent at 560 nanometers.
Wilks' lambda statistic does not indicate
Species effects. However, Pilai's trace statistic, 1.6776 with
F = 5.203 and p-value = .07, suggests there may be Species effects.
For MANOV A, the value of
(For Nutrent, Wilks' lambda and Pillai's trace statistic give the sam F
value.) For larger sample sizes, Wilks' lambda and Pilai's trace stati'Stic
would .give essentially the same result for all factors.
133
6.33. (a) MAGV A for Species: Wilk' lambda A~ = .06877
F = 36.571; p-value = P( F ~ 36.571) = .000
F4,52 (.05) = 2.55
Reject H(J: No species effects
MANDV A for Time: Wilks' lambda A'2 = .04917
F= 45.629; p-value =P( F~ 45.629) = .000
F4,'52 (.05) = 2.55
Reject Ho: No time effects
MANOV A for Species*Time: Wilks' lambda A~2 = .08707
F= 15.528; p-value=P(F~ 15.528)=.000
Fa,52 (.05) = 2.12
Reject Ho: No interaction effects
(b) A few outliers but, in general, residuals approximately normally distrbuted
(see histograms bèiow). Observations are likely to be positively correlated
over time. Observations are not independent.
Histogram of the Residuals
Histogram of the Residuals
(nipo Is 560nm)
(..po Is 720rv)
90
90
90
eo
70
,.
70.
60
g 50
tì 60
~ 60
:i
~ 40
Gl
6- 40
I!
u. 30
u. 30
20
20
10
10
o
.s
-6
-2 0
-4
4
.20
o 10
.10
Residual
Residual
(c) Interaction shows up .for the 560nm wavelengt but not for the 720nm
~
wavelengt. See the Mintab ANDV A output below.
Analysis of Variance for
Source
Species
Time
Species*Time
Error
Total
DF
2
2
SS
965.18
1275.25
4
7 9S . 81
27
35
76.66
3112.90
MS
482.59
637.62
198.95
2.84
F
169.97
224.58
7(J. \)7
P
O. 000
0.000
O. (Joa
Analysis of Variance for 720nm
Source
Species
Time
Species
Error
Total
*
Time
DF
SS
2
2026. 8~
2
5573.81
4
193. 5S
27
35
1769.t54
95:3.85
MS
1013.43
2766.90
48.39
65.54
F
P
15.46 0.000
42.52 -0.000
0.74 '0.574
20
30
6.33-,. (Continued) 134
t d) The data might be analyzed using the growth cure methodology discussed in
Section 6.l.. The data might also be analyzed asuming species are "nested"
within date. fu ths case, an interesting question is: Is "Spetral reflctane the
same for all species for each date?
6.34 Fitting a linear gr.owth curve to calcium measurements on the dominant ulna
XBAR
Grand mean
72.3800 .69.2875
MLEof beta
71.1939
73.2933 70.6562
72.4733 71.1812
71 .8273
73.4707 70.5049
-1.9035 -0.9818
64.7867 64.5312
(B'Sp~ (-l)B) - (-1)
93.1313 -5.2393
-5.2393 1 .2948
72.1848
65.2$67
Sl
S2
92.1189 86.1106 73.3623 74.5890
98.1745 97.013489.482486.1111
86 .11~6 89.0764 72.9555 71.7728
97.0134 100. õ960 88.1425 88.2095
73.3623 72.9555 71.8907 63.5918
74.5890 71.7728 63.5918 75.4441
89.4824 88.1425 86.3496 80. 5S06
86.1111 88.2095 80.5506 81.4156
Spooled
W = (N-g) *Spooled
95.2511
91.7500
81.7003
80.5487
91.7500
95.0348
80.8108
80.2745
81.7003
80.8108
79.3694
72.3636
80.5487
80.2745
72.3636
78.5328
Estimated covariance matrix
7.1816 -0.4040 0.0000 0.0000
-0 .4040 0 . 0998 0 . 0000 0 . ~OOO
2762.282
2660.749
2369.308
2335.912
2660.749
2756.009
2343.514
2327.961
2369.308
2343.514
2301.714
2098.544
2335.912
2327.961
2098.544
2277.452
WL
2803.839 2610.438 2271.920 2443.549
2610.438 2821.243 2464.120 2196.065
o . 0000 0 . 0000 6.7328 -0.3788
2271 .920 2464. 120 2531. 625 1~45. 313
0.0000 ~.~OO -0.3788 ~.0936
2443.549 2196.065 1845.313 25S6.818
Lambàa = 1~ I / IWll = 0.201
Since, with
a = 0.01, - IN - ~tp - q + g)) 10g(A) = 45.72 ;: X~4-i-l)2(O.0l) = 13.28,
we reject the null hypothesis of a Iinear fit at a = u.Ol.
135
6.35 Fitting a quadratic growth curve to calcium measurements.on the dominant ulna,
treating all 31 subjects as a single group.
XBAR
MLE of beta
(B 'Sp~ (-l)B) - (-1)
70.7839
71.6039
92.2789 -5 .9783 0 .0799
71. 9323
71.80:65
3 . 8673
-1.9404
-5.9783 9.3020 -2.9033
0.0799 -2.9033 1.0760
64.6548
S
W = (n-l) *8
94.5441
90.7962
80.0081
78.0676
90.7962
93.6616
78.9965
77.7725
80.0081
78.9965
77.1546
70.0366
2836.322 2723.886 2400.243 2342.027
78.0676
77.7725
70.0366
75.9319
2723.886 2809.848 23ß9. 894 2333. 175
2400.243 2369.894 2314.~39 2101.099
2342.027 2333.175 2101.099 2277.957
Estimated covariance matrix
W2
2857.167 2764.522 2394.410 23ß9.674
3.1894 -0.2066 0.0028
-0.2066 0.3215 -0.1003
0.0028 -0.1003 0.0372
2764.522 2889.063 2358.522 2387 .~70
2394.410 2358.522 2316.271 2093.3ß2
2369.674 2387.070 2093.362 2314.'"25
Lambda = I w I / I W21 = 0.7653
Since, with a = V.OI, - (n - Hp - q + 1)) 10g(A) = 7.893 ~ XL2_i(0.01) = 6:635, we
reject the null hypothesis of a quadratic fit at a = 0.01.
6.36 Here
p = 2, n¡ = 45, n2 = 55, In 1 S¡ 1= 19.90948, In I S2 1= 18.40324, In 1 S pool~d 1= 19.27712
so u =(~+~- 1 J(2(4)+3(2)-lJ = .02242
44 54 44+54 6(2+1)(2-1)
and
C = (1- .02242)(98(19.27712) -44(19.90948) -54(18.40324)) = 18.93
The chi-square degrees of freedom v =.! 2(3)(1) = 3 and z; (.05) = 7.81. Since
2
C = 18.93;: Z;(.05) = 7.83, reject Ho : ~¡ = ~2 = ~ at the 5% leveL.
136
6.37 Here
p = 3, n, = 24, n2 = 24, In 1 S, 1= 9.48091, In 1 S2 1= 6.67870, In I Spooled 1= 8.62718
so u =(~+~- 1 Jr 2(9)+3(3)-1) =.07065
23 23 23+23 L 6(3+1)(2-1)
and
C = (1-.07065)(46(8.62718) - 23(9.48091) - 23(6.67870)) = 23.40
The chi-square degrees of freedom v = .!3(4)(1) = 6 and .%;(.05) = 12.59. Since
2
C = 23.40 )0 xi (.05) = 12.59, reject H 0 : 1:, = 1:2 = 1: at the 5% leveL.
6.38 Working with the transformed data, Xl = vanadium, X2= .Jiron, X3 =,Jberyllum,
X4 = 1 ¡f saturated hydrocarbons J , Xs = aromatic hydrocarbons, we have
p = 5, n, = 7, n2 = 11, n3 = 38, In 1 S, 1= -17.81620, In I S2 1= -7.24900,
InIS31=-7.09274,lnISpoled 1=-7.11438
so u=r.!+..+~- 1 Jr2(25)+3(5)-I)
L6 10 37 6+10+37 L 6(5+1)(3-1)
=.24429
and
C = (1-.24429)(53(-7.11438) -6(-17.81620) -10(-7.24900) -37(-7.09274)) = 48.94
The chi-square degrees of freedom v = .!5(6)(2) = 30 and .%;0(.05) = 43.77. Since
2
C = 48.94)0 x;o (.05) = 43.77, reject H 0 : 1:1 =I:2 = 1:3 = 1: at the 5% leveL.
6.39 (a) Following Example 6.5, we have (iF - xM)' = (119.55, 29.97),
-8 +-8 - an i- - . . inee
(28
1 F
1 28
J-'M-r- .108533
.033186 .423508
-.108533) d.. -76 97 S'
r = 76.97 )0 xi (.05) = 5.99, we reject H 0 : PF - PM = 0 at the 5% leveL.
(b) With equal sample sizes, the large sample procedure is essentially the same
as the procedure based on the pooled covariance matrix.
(e) Here p=2, 154(.05/2(2)):: z(0125) = 2.24, (J.8 +J.sJ =(186.148 47.705J, so
28 F 28 M 47.705 14.587
PF' - PM': 119.55:f 2.24.186.148 ~ (88.99, 150.11)
PF2 -PM2: 29.97:f2.24.J14.587 ~(21.41, 38.52)
Female Anacondas are considerably longer and heavier than males.
137
6.41 Three factors: (Problem) Severity, Wroblem) Complexity and (Engineer)
Experience, each at two levels. Two responses: Assessment time,
Implementation time. MANOV A results for significant (at the 5% level) effects.
Effect
Severity
Complexity
Experience
Severity*complexity
Wilks'
F
lambda
.06398
.01852
.03694
.33521
P-value
73.1
.00
265.0
130.4
9.9
.000
.000
.004
the two responses, Assessment time and
Implementation time, show only the same three main effects and two factor
interaction as significant with p-values for the appropriate F statistics less than .01
in all cases. We see that both assessment time and implementation time is affected
by problem severity, problem complexity and engineer experience as well as the
interaction between severity and complexity. Because of the interaction effect, the
main effects severity and complexity are not additive and do not have a clear
interpretation. For this reason, we do not calculate simultaneous confidence
intervals for the magnitudes of the mean differences in times across the two levels
of each of these main effects. There is no interaction term associated with
Individual ANOV A's for each of
experience however. Since there are only two levels of experience, we can
calculate ordinary t intervals for the mean difference in assessment time and the
mean difference in implementation time for gurus (G) and novices (N). Relevant
summary statistics and calculations are given below.
1.217J
Error sum of squares and crossproducts matrix = 1.217 2.667
(2.222
Error deg. of freedom: 11
Assessment time: xG = 3.68, xN = 5.39
95% confidence interval for mean difference in experience:
3.68-5.39 :!2.201.J2.222 2 = -1.71:!.49 -7 (-2.20, -1.22)
11 8
Implementation time: xG = 6.80, xN = 10.96
95% confidence interval for mean difference in experience:
~2.667 2
6.80 -1 0.96 :! 2.201 -- = -4.16:! .54 -7 (-4.70, - 3.62)
11 8
The decrease in mean assessment time for gurus relative to novices is estimated to
138
be between 1.22 and 2.20 hours. Similarly the decrease in mean implementation
time for gurus relative to novices is estimated to be between 3.62 and 4.70 hours.
l39
Chapter 7
7.1
-1~1 (8::)= 11 (-::) = (~:::J
1 l (120
- - 120 -10
ß = (Z'Z)- l'y =--
180
85
123
.. .... 1
Y=LS=_ _ 15
351
199
142
=
12 . 000
15
5.667
8.200
23.400
13.2£7
9
3
,.
,.
£: = Y-Y
- -
=
9 .4'67
25
-
l2 .000
5.667
3.3J3
8 .lOO
-'5.200
.23.400 =
9
13.267
13
9 .467
3 .ooõl
1.~0
-o.2t7
3.533
.. ,.
Residual sum of squares: :1: = 101.467
fitted equation: y = -.667 + 1.2£7 zl
7.2
Standardized variables
zl
z2
Y
- .292
-1 .088
.391
-1 . 166
- . 7.2£
- . 391
- . 81 7
1 .283
- .726
-1 .174
. 3£3
1.695
-.117
.726
- . £5.2
1 . 1 08
1 .451
.130
fi tted equa ti on:
..
Y = 1 .33z1
- .7 9zZ
Al so, pri or to standardi zi ng the variables, zl = 11 .6'ó7,
z1 z12 :z
ž2 = 5.000 and y = 12.tlOO; Is = 5.716, '¡sz z = .2.7'57
and IS = 7.6'67 .
yy
The fi tted equation for the origi na 1 variabl~s is
= 1 33 -
y _ 1 2 (Zl - 11 .6£7)
7.667. 5.716.
.79 2.757;
(Z2 - 5\
,.
y = .43 + 1.7Bz1 - 2.19z2
7.3
- ~ - - -w
Foll.o\'1 hint and note that s* = y* - y* = v-1/2y_v-1/2;æ ami
(n-r-l )02 = Ê*'.Ê*
is distributed as X1
n-r-1.
140
7.4
ii )
v=I
b)
V
-1
,. 1 n n
so ß.w = (zlz)- z'y = t L z.Y.)/( ¿z~).
~ - - - - j=l J J j=l J
is diagonal with jth diagonal -element 1/'1. so
J
n n
""W - . j=l J j=l J
â = (zIV-lz)-l :iv-l~ = (L y.)/( r z.)
cj
y-l is diagonal with jth diagonal element l/z~ ~o
J
n
~W - .. .... J=1
ß = (z'y-1z)-lzIV-ly = (.r (YJ,¡zJ.))/n
7.5 So, ution follows from Hi nt.
7.6
a)
irs t
nO.e
at A.
1,0,...0)
F.
+ th
- --d1ag
. r Ai
-1,...,).
-1
ri +
is a
generalized inverse of il since . .
o
À1
AA- = r 1;1+1
.
so M - A =
:J
.Àr,
= A
+1
a
.1)
.0
Si nce
Z'l = ! )..e.e! = PAP'
. , 1-1-1
1=
ri+1
(Z'Z)- = ¿ ).:' e.e~ = PA-P'
1.= 1 1 - 1_ 1
.
with
PP' = P'P = I , we check that th~ defining relation holds
p
-.~
(Z'Z)(Z'Z)-(Z'Z) = PAp1(PA-P')PAP'
= PM- Api
= PAP' = l Z
ti )
8y
the hint,
,.
lZ8
=
Z'y.
if ze-,. is
the 'Projection t
c) ,
that
In
we show
,.
ze-
.0 =
-
is the
Z' (y-
,.
- or
- ia)
pro je.ct;..o n
of y- .
. 141
_ -1/2
c)
= l,~,..., r 1 +1 . Then
Consider q. - À. Ze. for
_1 1 _1
ri +1
-1 . )ZI
ri +1
Z(Z'Z)-Z' = Z(I.~ 1À.
e.e. =
-1-1
i =1
1
I.
;=1
q.q.
_1_1
The (S11 are r1+l mutually perpendicular unit l~ngth vectors
that span the space of all linear combinations of the columns of
Z. Thé projection of iis then (see R.esul t 2A.2 and
Def; nition 2A.12)
ri+l ri+l
ri+l
'1-1-1- -
I (q!y)q. = ¿ q.(q~y) = ( L q.q~)y = Z(Z'Z)- Z'y
1=
;=1 -1- -1 i=l -1 -1d) See Hint.
7.7
and Z = (Zl h J .
Write
. = ~(2)
~
(_~U1J
..
- =.(2) -
r - :r-q A
Recall from Result 7.4 that ß =(~ii) = (Z'Z)-lZ'Y is distributed
as N +1(ß,a2(Z'Z)-1) indepen4ently of nâz = (n-r-l)sZ which is
distributed as a2 X~-r-l. From the Hint, (~(2'-~(2))'(Cl~~(2'-~(2))
iscl2 and this is distribut~d independently of S2. Ühe latter
follows because the full random vect-or ê is distributed independently
of SZ). The result follows from the definition of a F random variable
as the ratio of .two independent X2 random variables divided by their
degrees of freedom.
7.8
(;t) H2 = Z(Z'Z)-l Z'Z(Z'Z)-i Z' = Z(Z'Z)-i Z' = H.
(h) Since i - H is an idempotent matrix, it is positive semidefinite. Let a be an n x i unit
vector with j th element 1. Then 0 ~ a'(l - H)a = (1- hii)' That is, hji ~ 1. On the
other hand, (Z'Z)-l is posiiÏe definite. Hence hij = bj(Z'Z)-lbi ~ 0 where hi is the i
th row of z.
¿'i~:hij = tr(Z(Z'Z)-iZ') = tr((Z'Z)-iZ'Z) =tr(Ir+1) =r+l.
142
(c) Usill
(Z'Z)-In£J1=1
="':1(z'
¡ ¿~I
zl -l:~1
- i"i:z'ZiJ
n'
J- -z)2
£Ji=1
we obtain
1
(
ßn
)
i_I , 1=1 i=i
hjj - (1 Zj)(Z'ZJ-I ( ;j )
- ní:~ (z' _ z)2 L:z; - 2z; ¿Zi + nzj)
1 (Zj - z)2
- ;; + í:i'i(Zj - z)2
7.9
Z' = (' ,
-2 -1
1 1
:l
a
(ZIZ)-l
'=('0/5
1;10 J
~(1) = (Z'Zi-1Z'l(1) = L~91; ~(2) = (Z'Z)-lZ'~(2) = ri ~5 J
t = (~(l) :1 ~~2)J - (
- - ~9
1 ~5 J
Hence
4.8 -3.0
3.9 -1.5
" ,.
y = Z~ =
3.0 a
2.1 1.5
1.2 3.0
,. ,.
e =y-y=
5
-3
3
-1.
-1
4
2
1
2
3
4.8
3.9
3.0
2.1
1.2
-3.0
-1.5
0
1.5
3.0
=
.2
- .9
.5
1.0
.1.0
- .1
.5
- .2
0
".A A A
Y'Y = y'y + tit
r 55
J-15
l-
0
- 1 SJ ( 53 . 1
-13.SJ + r 1.9
-1. SJ
24 =. -13.5
22 .5 L - i .5
1.5
143
7.10 a) Using Result 7.7, the 95% confidence interval for the mean
reponse is given by
(1, .5) l'"3.0) :t 3.18
.5) (.: .~)I.1 (\9)
or
- .9
(1.35, 3.75).
b) Usi ng Resu1 t 7.8, the 95% prediction interval for the actual Y
is given by
(1, -. 5 J (3 .0 J- :! 3.18
-.9
)11 + (1, os) (0: .~H~KI j9)'or
(~ . 25, 5.35) .
c) Using (7-l.¿) a 95% prediction ellipse for the actual V's is
given by
(YOl -2.55, Y02 - .75)
7.5' 9.5 Y02 - .75
(7.5 7.5J (Y01. -2.SS)
s (1 + .225) ~2)P~ (19) = 69.825
144
7.11 The proof follows the proof of Result 7.10 with rl replaced by A.
n
(Y- ZB ) i (T- Z' B) = I (V. -8 z . )( Y . -B z . ) ,
j=l -J -J -J -J
and
i:~=1 dj(B) = tr(A-1rY'-ZB)'(Y-IS))
Next,
(¥- ZS) i (Y-ZB) = (Y-Z~+Zp-ZB) i (y- zP+ZS-ZB) = ê'€ + '~~-B) i Z i Z(~-B)J
so
i:~=1 dj(B) = tr(A-l£'tJ + tr(A-l(j-B)'Z'I~i-B))
The fi rst tenn does not depend on the choice of B. Usi n9 Resul t
2A. 1 2 ( c )
tr(A-lt~-B)'Z'Z\P-B) = tr(~p-B)'Z'Z(s-8)AJ
= tr(Z i Z (S-B )A(~-B) i)
,. ,.
= tr(Z'(f3-B )A(S-ß)' Zi)
~ C i Ac ) 0
- -
~/here ~ is any non-zero row of ~(~-B). Unless B = i, Z(S-B)
will have a non-zero row. Tl)us ~ is the best 'Choice f-or any positive d'efi ni te A.
145
7.12
(a)
(1))
best linear pr~di~tor = -4 + 2Z1 - Z2
+-1
mean square error = cr - a i + az = 4
yy _ zy zz - y
a a
i t-1
(c)
PY(x)
= -zyayy
zz -zy
_ IS _
- '3 - .745
(d) Following equation (7-5b), we partition t as
t = iL ~ -i ~J
1 1 ii 1
and detenni ne cava r; ance of ( 1 given z2 to be
:,
( : : J - ( : J (1 ) - , (1. 1) = l: ~). Therefore
=IiT=
2
Py Z i · Z 2 =
7.13
.¡ If
(a) By Result 7.13, ß_ =zz
s-l-s
_zy
(b) Let !(2) = (Z2,Z3J
= r 3.73)
L 5. 57
R =
zl (Z2Z3)
=/3452.33 =
VS691 .34 .78
(c) Partition ~ = t l~11 so
.707
1
s
-z(2)zl
çl s
z~ 2)z(2)-z(2 )zl
s
zizl
146
S691.34 r
S I s'
s i
i S
f----------
= z(l )Z(l): -Z3Z.(,)
i
S = '600.51
126.05 i
_________l-___
217.25 23.37 i l23.11
L _z3z(,) i '3z3
and
s - s' s-l s
380.821
z(l )z(l) -z3z(1) z3z3 -z3z(,) = r3649.04
380.82
' 02.42
Thus
380 . 82
r z, z2.z3
7.14
= .£2
/3649.04 1''02.42
(a) The large positive correlation between a manager's experience
and achieved rate of return on portfolio indicat~s an apparent
advantage for managers with experience. The negative correla-
tion between attitude to\'iard risk 'and achieved rate of return
indica tes an apparent advantage for conservative managers.
(b) from (7-S1)
s s
syz,
_ YZ2s 2,
ryz, · z2
s z z
Zi
yz'.ZZ =ß') 2
=
/s
· Is
i S syz
yy-z2
z,zl.z2
_ --.
S2
yy z2z2
s zl zl
ryz,
- YZ2
r r2, z2
11 -
r~-YZ2I' -zlr~-z2
=
zl Z2
s 'S
z2z2
= .31
Removifl9 lIy.ear'S of eXl'eriencell from ,consideration, we no\'1 have a
positive c.orr-elation between "attitude towar.d riskll and "achieved
147
returnll. After adjusti ng for years of experience, there ;s an
apparent advantage ,to mana~ers who take ri sks.
7.15 (a) MINlTAB computer output gives: y = 11 ,870 + 2634'1 + 45.2z,z;
residual sum of squares = 2tl499S012 with 17 degrees of freedom.
Thus s = 3473. Now for example, the ~stimated standard devia-
,. ,.
..
tion of ßO is /1.996152 = 4906. Similar calculations give
the estimated standard deviations of ß1 and ß2.
are no apparent
(b) An analysis of the residuals indicate there
model inadequacies.
(c)
The 95% predi~tion interval is ($51 ,228; $60,23~)
(d)
Using (7-",Q), F = (45.2)( .0067)-1 (45.2) = .025
12058533 .
Since fi,17(.OS) = 4.45 we cannot reject HO:ß2 =~. It appears
as if Zl is not needed in the model provided £1 is include~
in the model.
7.16
Predictors
P=r+1
C.o
1.025
Zl
2
Z2
2
12.24
3
3
Zl 'Zz
148
sales and assets follows.
7.17 (a) Minitab output for the regression of profits on
Profits = 0.~1 + 0.0'6~1 Sales + 0.00577 Assets
Predic-tor
Constant
Sal.es
Assets
S =
SE Coef
(;oef
0.013
0.02785
0.004946
0.0'6806
0.005768
p
0.999
0.045
0.282
R-Sq(adj)
R-Sq = 55.7%
3.86282
T
0.00
2.44
1.17
7.'641
=
43.0%
Analysis of Variance
Source
DF
SS
2
131. 26
Regression
Residual Error
Total
7
9
104.45
235.71
MS
F
65.63
14.92
4.4()
P
0.058
(b) Given the small sample size, the residual plots below are consistent with the
usual regression assumptions. The leverages do not indicate any unusual
observations. All leverages are less than 3p/n=3(3)110=.9.
Resîdual Plots for Prots
;-",:¡':-,:--,. ,',',,",.', -"',:-,:;:'Nónf~tProbabtltypìól: of the
ResdualsÝetthe fi Value
Residiials
99 ,
. 5.0
90 :.
.."
ii 2.5
! 0.0
~2,5
"5.0
1
.0
"10
10,0 125 15.0 17.5 20.0
10
Fi Value
.Reual
Residuals Versus the Order of the Data
llistni..oftt~ Residuals
4:
5.0
ii..
D' 3
..
Ii
I
" 2
f 1
-2.5
o
,Reua'
(c) With sales = 100 and assets = 500, a 95% prediction interval for profits is:
(-1.55, 20.95).
(d) The t-value for testing H 0 : ß2 = 0 is t = 1.17 with a p value of .282. We cannot
reject H 0 at any reasonable significance leveL. The model should be refit after
dropping assets as a predictor varable. That is, consider the simple linear
regression model relating profits to'sales.
149
7.18 (a) The calculations for the Cp plot are given below. Note that p is the number of
model parameters including the intercpt.
2 (sales)
2.4
2 (assets)
7.0
3 (sales, assets)
3.0
(b) The AIC values are shown below.
p (predictor)
AIC
2 (sales)
29.24
2 (assets)
33.63
3 (sales, assets)
29.46
7.19 (a) The "best" regression equation involving In(y) and Z¡, Z2,..' ,Zs is
In(y) = 2.756-.322z2 +.114z4
with s = 1.058 and R2 = .60. It may be possible to find a better model
using first and second order predictor variable terms.
(b) A plot of the residuals versus the predicted values indicates no apparent
problems. A Q-Q plot of the residuals is a bit wavy but the sample size is
not large. Perhaps a transformation other than the logarthmic
transformation would produce a better modeL.
iso
7.20 Ei genva 1 ues 'Of the carrel atÍ\Jn matrix of the predi ctor vari able'S 2:1,
z2,...,z5 are 1.4465,1.1435, .8940, .8545, .6615. The correspoml-
of '1' z2,...,z5 in the
ing eigenvectors give the coefficients
principle component. for example, the first principal component,
written in terms of standardized predictor variables, is
.. * * * * *
Xl = .60647.1 .3901Z2 .6357Z 3 - .2755Z4 - .0045zS
A regression of Ln(y) on the first principle component gives
"
..
1n(y) = 1.7371 - .070.li
with s = .701 and R% = .015.
A regression of 1n(y) on the fourth principle ~ompon~nt produ~~s
the best of the one pri ncipl e component pr.edictor variable regress ions.
..
In this case 1n(y) = 1.7371 + .3604x4 and s = .618 and R1 = .235.
7.21' This data set doesn1t appear to yield a regr.ession relationship whkh
explains a larg.e proportion of the variation in the r~sponses.
(a) (i) One reader, starting with a full quadratic model in t~e
predictors z1 and z2' suggested the fitted regressi'On
equation:
"
Yl = -7.3808 + .5281 z2 - .0038z2 z
with s = 3.05 and R% = .22. (Can you do bett.er than
this?)
of the residuals versus the fitted values SU99~sts
(ii) A plot
the response may not have constant variance. Al so a Q-Q
plot of the residuals has the fOllO\'ling gen,eraT ap?ear-
ance:
151
Normal probabilty plot
.
co
. ....
(0
.,........
C/
ei
::
'C
ïñ
-.
.....
C\
Q)
0:
0
-.
I
....~..
...-
..;¡.
......
..~
......
. . ~...,
.
. . ....
..'
-2
-1
o
1
2
Quantiles of Standard Normal
Therefore the normality assumption may a 1so b~ suspect.
Perhaps a better regr.ession can be obtained after the
responses have been transformed or re-expressed in a
di fferent metri c.
(iii) Using the results in (a)(i), a 95~ prediction interval
of zl = 10 (not needed) and z2 = 80 is
10.84 :! 2.0217 or (5.32,16.37).
152
7.22 (a) The full regression model relating the dominant radius bone to the four predictor
variables is shown below along with the "best" model after eliminating non-
significant predictors. A residual analysis for the best model indicates there is
no reason to doubt the standard regression assumptions although observations
19 and 23 have large standardized residuals.
Q) The
regression equation is
DomRadius = 0.103 + 0.276 DomHumerus
- 0.165 Humerus + 0.357 DomUlna
+ 0.407 Ulna
Coef
0.1027
0.2756
Predictor
Constant
DomHumerus
Humerus
0.3566
0.4068
Ulna
P
-1. 20
1. 80
1. 87
0.246
0.088
0.076
0.97 0.346
2.40 0.02"6
0.1064
0.1147
0.1381
0.1985
0.2174
-0.1652
DomUlna
T
SE Coef
R-sq(adj) = 66.1%
R-Sq = 71.8%
S = 0.0663502
- .- ~ -,------~~-------------"'-"~_._~~- - The regression equation is
DomRadius 0.164 + 0.162 DomHumerus + 0.552 DomUlna
predictor
Constant
DomHumerus
DomUlna
Coef
0.1637
0.16249
0.5519
S = 0.0687763
P
T
SE Coef
0.128
0.012
0.002
1. 58
0.1035
0.05940
0.1566
2.74
3.53
R-sq(adj) = 63.6%
R-Sq = 66.7%
Analysis of variance
Source
DF
Regression
Residual Error
Total
(ii)
SS
0.20797
0.10406
0.31204
2
22
24
F P
MS
0.10399 21. 98 0.000
0.00473
Residual910ts for DomRadius..Dom Hi:metus and 'Dm Ulna PtedictlS
Normal probabilty Plot
"
'O
the Reduàls
Reiduals Verus the Fitt Value
. 1l
90
..
-I
,~ 50
..
. ..
:D;
10
1
1l J. ~
'0.1
0;0
0;6
0.1
0;7 OJ! 0.9
1.0
Resual
Fi Value
Histogl"in of the Residuàls
ResidualsVelsus tleOrder ofthè Ðlt
8
f&
:!
i
.. 4
!
II .2
'0.1
~ò16 "8.08 .O¡O 0.08 0.16
ll..1
~Or
2 4 6 8 10 12 14 16 18 20 22 24
153
(b) The full regression model relating the radius bone to the four predictor varables
is 'Shown below. This fitted model along with the fitted model for the dominant
radius bone using four predictors shown in part (a) (i) and the error sum of
squares and cross products matrix constitute the multivanate multiple regression
modeL. It appears as if a multivariate regression model with only one or two
predictors wil represent the data well. Using Result 7.11, a multivarate
regression model with predictors dominant ulna and ulna may be reasonable.
The results for these predictors follow.
The regression equation is
Radius = 0.114 _ 0.0110 DomHumerus + 0.152 Humerus + 0.198 DomUlna + 0.462 Ulna
Coef
Predictor
Constant
o . 11423
DomHumrus
Humerus
DomUlna
Ulna
-0.01103
0.1520
0.1976
0.4625
SE Coef
T
1.27
0.08971
0.09676
-0.11
1.31
1.18
2.52
0.11'65
0.1674
0.1833
P
0.217
0.910
0.207
0.252
0.020
S = 0.0559501 R-Sq 77.2% R-Sq(adj) = 72.6%
Error sum of squares and cross products matrix:
The regression equation is
Radius = 0.178 + 0.322 DomUlna + 0.595 Ulna
The regression equation is
DomRadius 0.223 + 0.564 DomUlna + 0.321 Ulna
Predictor
Coef
0.2235
DomUlna
o . 5645
Constant
Ulna
0.3209
SE Coef
0.1120
0.2108
0.2202
T
2.00
2.68
1.46
Predictor
p
Constant
0.059
0.014
0.159
DomUlna
Ulna
SE Coef
0.08931
0.1680
0.1755
T
2.00
1.92
3.39
Analysis of Variance
Analysis of Variance
Source
DF
5S
2 0.184863
Res idual Error
22 0.127175
24 0.312038
Total
Coef
0.17846
0.3220
0.5953
MS
0.092431
0.005781
F
15.99
P VIF
0.058
o . 0'68 2 . 1
0.003 2.1
S = 0.0'606160 R~5q = 70.5% R-Sq(adj) = '67.8%
S = 0.07'60309 R-Sq = 59.2% R-sq(adj) = 55.5%
Regression
.050120 .050120J
.062608
(.088047
P
0.000
Source
DF
5S
2 0.193195
Residual Error
22 0.080835
24 0.274029
Regression
Total
Error sum of squares and cross products matnx:
MS F I
0.09'6597 26.29 O.OOC
0.003'674
.064903J
.064903 .080835
(.127175
154
7.23. (a) Regression analysis using the response Yi = SalePr.
Sumary of Backward Elimination Procedure for Dependent Variable X2
Variable Number Partial Model
Step
1
2
3
Removed In R**2 R**2
K9
7
0.0041
0.5826
X3
0.0043 0.5655
0.5782
X5 56 0.0127
6.3735
6.4341
Sum of Mean
DF Squares Square
Model
F
o . 66g7
o . 7073
C Total
R-square
425.05739
Parameter Estimates
Root MSE
Variable
DF
INTERCEP
1
XL
1
1
X4
X6
X7
1
1
1
18
o .4033
o .1538
2.0795
F Value
Prob) F
18.224
0.0001
5 16462859.832 3292571.9663
70 12647164.839 180673.78342
75 29110024.671
Error
Prob)F
0.4161
SalePr
Dependent Variable: X2
Analysis of Variance
Source
C(p)
7 .6697
o . 5655
Parameter
-Standard
Estimate
Error
-5605.823664 1929.3g86440
-77.633612 22.29880197
-2.332721
o . 75490590
389.364490
1749.420733
89.17300145
701.21819165
133. 177'529
46 .66673277
T for HO:
Parameter=O
Prob ) ITI
-2.905
-3.482
-3.090
4.366
2.495
2.854
o . 0049
o .0009
o . 0029
o . 0001
0.01'50
o .0057
The 95% prediction interv~l for SalePr for %0 is
z~ß:f t70(0.025) /(425.06)2(1 + z~(Z/Z)-lZO)'
SalePr:~reed .) FtFrBody J Frame ~ BkFat) SaleHt)
(a) Residual plot
oo
II
~
o
ll .......
0c:
oo
..o
'"
1ã
i:::
1i
Gl
a:
~b) Normal probability plot
00
ll
~
..
c:
~
'"
o
ii::
i:
'Uj
..
00
ll
o
o
'9·....
.-~.
u;
..
1000
2000
Predicted
/:...-..-..
Gl
a:
. ... ~ . .
0
o .._.................'W......:;.._.~..........................._..
.... . .
. . e.
00
3000
/' .-
.~.......~.;:~..
.
-2 -1
o
2
Quanties of Standard Normal
155
(b) Regression analysis using the r.esponse Yi = In(SalePr).
Sumary ofBa~kward Elimination Procedure f~rDependent
Variable LOGX2
Variable Number Partial Medel
Removed In R**2 R**2
Step
X3
76 0.0033
0.6368
X7
0.0057
0.6311
i9 5 0.0122 0.6189
X4 4 0.0081 0.6108
1
2
3
4
C(p)
F
Prob~F
7.6121
6.6655
0.6121
'0 .4368
1. 0594
6 . 9445
2 . 2902
o .3070
o . 1348
6.4537
1 .4890
o . 2265
Dependent Variable: LOGX2
Analysis of Variance
'Sum of
Mean
Source
DF
'Squares
Square
F Value
Prob~F
Mode 1
4
71
75
4.02968
1. 00742
27.854
0.0001
2 . 56794
0.03'617
Error
C Total
6.597-'2
0.19018
R~ot MSE
R-square
Parameter Estimates
0.6108
Parameter
Estimate
'Standard
Error
Parameter=O
Prob ~ ITI
0.91286786
5.736
0.0001
o . 00846029
o . 00827438
-5 .841
-3 .337
o . 000 1
1
5.235773
-0.049418
-'0.027613
0.183611
o .058996
4.599
3.060
o . 0001
1
0.03992448
0.01927655
Variable
DF
INTERCEP
1
XL
1
X5
X6
X8
1
T for HO:
0.0013
0.0031
The 95% prediction interval for In(SalePr) for Zo is
z~ß:f t7o\O.025) J~O.19.Q2)2(1 + z~(ziz)-izo).
The few outliers among these latter residuals are not so pronounced.
In(alePrFfl3r.ed S PrctPF8 j Frame i SaIeHt)
(b) Normal probabilty plot
(a) Residual plot
.
c:
C\
II
iü
::
c:
"0
.¡¡
'"
a:
y/
""
. ...
.- .'....
c:
..
.
.... .".
. .
. .:".. .
C\
II
c:
II
0
iü
::
:2
.- ."
j........
'"
~ . ...............................................................................
c:
.. .:. ..1.\ .: ~ . :
C\
c?
""
..
. ..
9
7.0 7.2 7.4 7.6 7.8 8.0
Predicted
a:
J
C\
9
....~~;.
......
~ ........
9 '.
-2 -1
o
2
Quantiles of Stadard Nonnal
156
7.24. (a) Regression analysis using the response Yi = SaleHt and the predictors Zi = YrHgt
and Z2 = FtFr Body.
SaleHt
Dependent Variable: X8
Analysis of Variance
Sum of
Mean
OF
Squares
Square
F Value
Model
2
235.74'533
117 .87267
131.165
Error
73
75
65.60204
301.34737
o . 89866
Source
C Total
R-square
0.94798
Root MSE
Parameter Estimates
o . 7823
Standard
Error
Parameter
Estimate
Prob)F
0.0001
Variable
OF
INTECEP
1
1
7.846281
3.36221288
X3
o . 802235
o . 08088562
X4
1
o . 005,773
0.00151072
T for HO:
Prob )
Parameter=O
2.334
9.918
3.821
ITI
'Û . 0224
o . 000 1
o .0003
The 95% prediction interval for SaleHt for z~ = (1,50.5,970) is
53.96:f t73(0.025) \/0.8987(1.0148). = (52.06,55.86).
SaleHt:r~rHgt) FtFrBody)
(b) Normal probabilty plot
(a) Residual plot
......,.
N
N
....
. e. :.......:
.'.' .
/~:.
./
.t'
.- ... ....
Ul
ii:i
'l
c
a:
...
ëii
Gl
Ul
. ..
ll .- . ,,'
.. .:. e.
....... ............wr........ v-....... 60.............._;.. ........
ii:i
'l
0
a:
..,
C)
54
Predicted
56
..pa
....
C)
52
././
ëii
Gl
58
,,".......:.
.
.2 -1
o
2
Quantiles of Standard Normal
157
(b) Regression analysis using the response 1í = SaleWt and the predictors Zi = YrHgt and
Z2 = FtFrBody.
SaleWt
Dependent Variable: X9
Analysis of Varian~e
Sum of Mean
Source
DF Squares Square
Model
2 390456.63614 195228.31807
73 873342.99544 11963.60268
75 1263799.6316
Error
C Total
109.37826
Parameter Estimates
Root MSE
Variable
DF
INTERCEP
X3
X4
1
Parameter
Estimate
R-square
1
Prob;)F
16.319
0.0001
o . 3090
Standard
Error
675.316794 387 . 93499836
9.33265244
2.719286
0.17430765
0.745610
1
F Value
T for HO:
Parameter=O
1.741
Prob ;) ITI
0.291
4.278
0.771'6
o .0859
o .0001
The 95% prediction interval for SaleWt for z~ = (1,50.5,970) is
1535.9:: t73(0.025)V1l963.6(1.0148) = (1316.3,1755.5).
SaleW~rHgt) FtFrBody)
(a) Residual plot (b) Normal probabilit plot
oo
C'
o
o
C'
o
o
N
II
ñi
='
'0
'¡¡
Gl
IX
o
o
-
... .'.
..
. .'
ooC\
. .. ..
.... .. .
.... .
~
'
.
....
o ...
o .. ._.._.;....._.....;~.._;.. .-_..................................
II
ñi
='
'0
.¡¡
Gl
IX
o
8
.
ci
Predicted
r
/"
/
¡..-
.or
o
oo
...
1500 1600 1700 1800
. .'
, .,'.'
,. .....
oo
-
-.
o ......rI.
-. .
ci
.- .....
... .....
.,
. ...,
......
.2 -1
o
2
Quantiles of Standard Normal
158
Multivariate regression analysis using the responses Yi = SaleHt and Y2 = SaleWt and
the predictors Zi = YrHgt and Z2 = FtFrBody.
Multivariate Test: HO: YrHgt = 0
Multivariate Statisti~s and Exact F Statistics
S=1 M=O N=35
Statistic
Value
F
Wilks i Lambda
o . 38524567
57.4469
Pillai's Trace
Hotelling-Lawley Trace
0.61475433
57 . 4469
Roy
i s Greatest Root
Multivariate Test:
HO:
Multivariate Statistics
S=l
1 .59574625
57.4469
1.59574625
57 .4469
Num DF
2
2
2
2
Den DF
Num DF
2
2
2
2
Den DF
Pr ~ F
72
72
72
72
0.0001
0.0001
Pr ~ F
72 0.0001
72 0.0001
72 a .cQOO1
72 0..0001
FtFrBody = a
and Exact F Statistics
N=35
M=O
Statistic
Value
0.75813396
0.24186604
0.31902811
0.31902811
Wilks i Lambda
Pillai's Trace
Hotelling-Lawley Trace
Roy's Greatest Root
F
11. 4850
11 .4850
11. 4850
11 .4850
a .0001
0..0001
The theory requires using Xa (YrHgt) to predict both SaleHt and'SaleWt, even though
this term could be dropped in the prediction equation for Sale\Vt. The '95% prediction
ellpse for both SaleHt and SaleVvt for z~ = (1,50.5,970) is
1.3498(1'i - 53.96)2 + O.000l(Yó2 - 1535.9)2 - O.0098(Yói - 53.96)(ìó2 - 1535.9)
2(73)
- i.OI4872F2,72(O.05) = 6.4282.
The 95% predicion ellpse
for both SaleHt andSaleWt
Chi-square plot of residuals
o
-
~
Ò 'l...:/'
oo
~
co
l:
II
~l! '"
N
o
~
ai
o
2
4
qchisq
oo
io
-
o
, o-
CO
6
B
10
51 S2 53 S4 5S 55 57
Y01
159
7.25. (a) R-egression analysis using the first response Yi. The backward elimination proce-
dure gives Yi = ßoi + ßl1Zi + ß2iZ2. AU variables left in the model are significant at
the D.05 leveL. (It is possible to drop the intercpt but We retain it.)
Dependent Variable: Y1
Analysis of Variance
TOT
Sum of Mean
Source
Model
DF Square s Square
F Value
Prob~F
2 5905583.8728 2952791.9364
22.962
o .0001
Error
14 1800356. 3625 128596.88303
C Total
16 7705940.2353
R-square
358 . 60408
Root MSE
o . 7664
Parameter Estimates
Parameter
Estimate
Standard
Parameter=O
Frob ~ ITI
1
56. 720053
507.073084
Eror
o . 328962
0.274
2.617
6.609
o . 7878
o . 0203
1
206.70336862
193.79082471
0.04977501
Variable
DF
INTERCEP
1
Zl
Z2
T for HO:
0..0001
The 95% prediction interval for YÍ = TOT for z~ = (1,1,1200) is
958.5:l ti4(O.025)y'128596.9(1.0941) = (154.0,1763.1).
TOT~eN ; AMT)
(a) Residual plot
0co0
(b) Normal probabilty plot
00
co
. .....' -'
. .' .'
. .....
00
0N0
In
ãi
::
Q
"C
,ñ
.............................................................
~
..
CD
ir
0
0
y
r' .'
N
In
ãi
::
"C
üi
0
,.'
,... .
.....~..
00
y
....
"
oo
~
500 1000
2000
Predicted
3000
.....
...... .
..... .
CD
ir
. .
....
.'
......
g
.
~
-2
-1
o
Quantiles of Standard Normal
2
160
(b) Regression analysis using the second response Y2' The backward elimination procedure
gives 11 = ß02 + ßi2Zi + ß22Z2. All variables left in the model are significant at the
0.05 leveL.
Dependent Variable: Y2 AMI
Analysis of Variance
of Mean
Sum
Source
DF Squares Square
F Value
Prob.)F
Model
2 5989720.5384 2994860.2692
14 1620657.344 115761.23886
16 7610377.8824
25.871
0.0001
Error
C Total
R-square
340.23703
Root MSE
Parameter Estimates
Variable
DF
INTERCEP
1
Zl
1
Z2
1
o . 7870
Parameter
Standard
Estimate
Error
-241.347910 196.11640164
T for HO:
Parameter=O
Prob .) ITI
-1.231
3.298
6.866
o . 2387
183.86521452
606 . 309666
o . 324255
o . 04722563
0.0053
o . 0001
The 95% prediction interval for 1'2 = AMI for z~ = (1,1,1200) is
754.1 :l t14(0.025) Jii5761.2(1.0941) = (-9.234,1517.4).
AMi=t(eN J AMT)
(b) Normal probabilty plot
(a) Residual plot
oo
CD
oo
CD
(/
¡¡
::
ooC\ .
.' .'
.......
..
en
.
¡¡
i::i
oo
C\
........
!t0P'"
o
oo
i:
o ......A.............................................................
G)
oo
ïii
c:
i.Gl
c:
C)
C)
oo
oo
...~...
....o¡ .
....;.
.'
....
............
.
..'
'9
'9
500 1000
2000
Predicted
.2
.1
o
Quantiles of Standard Normal
2
161
(c) Multivariate regresion analysis using Yi and 1"2.
Multivariate Test: HO: PR=O. DIAP=O. QRS=O
Multivariate Statistics and F Approximations
8=2 M=O N=4
Statistic
Wilks' Lambda
Value
0.44050214
Pillai 's Trace
o . 60385990
Hotelling-Lavley Trace
Roy's Greatest Root
1.16942861
1.07581808
F
1 .6890
1 .5859
Num DF
1.7541
6
3
Den DF
6
Û
3 .9447
Pr ~ F
20 .( . 1755
22 .( . 1983
18 0.1657
11
0.0391
Based on Wilks' Lambda, the three variables Zs, Z4 and Zs are not significant. The
95% prediction ellpse for both TOT and AMI for z~ = (1,1,1200) is
4.305 x 10-5(Yå1 - 958.5)2 + 4.782 x 10-5(l'2 - 754.1)2
s( ? 2(14) ( )
- 8.214 x 10- 101 - 958.5)(1'Ó2 -754.1) = i.0941-iF2,1s,\O.D5 - 8.968.
The 95% prediction ellpse
for both TOT and AMI
Chi-square plot of residuals
U)
gII
-
II
i: ..
lI
'C
'C
N
1:
!! CO
~
CI
o'E
N
0
00
0-
00
10
..
.. ...
..
. .
0
,
0
2
3
4
qchisq
5 1l 7
o
500 1000 1500 2000
Y01
162
7.26 (a) (i) The table below summarizes the results of the "best" individual regressions.
Each predictor variable is significant at the 5% leveL.
73.6%
s
1.5192
76.5%
75.4%
.3530
.3616
80.7%
.6595
75.7%
.3504
R2
Fitted model
Yi = -70.1 + .0593z2 + .0555z3 + 82.53z4
27.04z4
Y2 =-21.6-.9640z1 +
26.12z4
Y2 = -20.92+.01 17z3 +
44.59z4
Y3 =-43.8+.0288z2 +.0282z3 +
Y4 = -17.0+.0224z2 +.0120z3+ 15.77z4
(ii) Observations with large standardized residuals (outliers) include #51, #52
and #56. Observations with high leverage include #57, #58, #60 and #61.
Apar from the outliers, the residuals plots look good.
(ii) 95% prediction interval for Y 3 is: (1.077, 4.239)
(b) (i) Using all four predictor variables, the estimated coefficient matrix and
estimated error covariance matrix are
-74.232 -24.015 -45.763 -17.727
-.550
-1.486
- 1.185
- 3.120
B=
i
I=
.098
.009
.047
.029
.049
.008
.025
.011
85.076
28.755
45.798
16.220
2.244 .398 .914 .511
.398 .118 .193 .089
.914 .193 .419 .210
.511
.089
.210 .122
A multivariate regression model using only the three predictors Zi, Z3 and Z4
wil adequately represent the data.
(ii) The same outliers and leverage points indicated in (a) (ii) are present.
Otherwise the residual analysis
suggests the usual regression assumptions
are reasonable.
(ii) The simultaneous prediction interval for Y 3 wil be wider than the
individual interval in (a) (iii).
163
7.27 The table below summarizes the results of the "best" individual regressions.
Ea~h predictor variable is significant at the 5% leveL. (The levels of Severity are
coded: Low= 1, High=2; the levels of Complexity are coded: Simple= 1, Complex=2;
Exper are coded: Novice=l, Guru=2, Experienced=3.) There are no
the levels of
signifcant interaction terms in either modeL.
Fitted model
Assessment = -1.834 + 1.270Severity + 3.003Complexity
Implementation = -4.919 + 3.477 Severity + 5.827 Complexity
74.1%
s
.9853
71.9%
2.1364
RZ
For the multivariate regression with the two predictor variables Severity and
Complexity, the estimated coeffcient matrix and estimated error covariance matrix
are
B = 1.270 3.477.
3.003 -4.919J
5.827
(-1.834
î: = ( .9707 1.9162J
1.9162 4.5643
A residual analysis suggests there is no reason to doubt the standard regression
assumptions.
164
Chapter 8
8.1 Eigenvalues of * are À1 = 6. À2 = 1. The
principal campenents are
Yl = ..894X1 + .447X2
,.
Y2 = .~47X1 - .894XZ
Vareyi) = À1 '= 6. Therefore. proporti()n of tatal population variance
explained by ~l is 6/(15+1) = .86.
8.2
.6325
1
£ = (1
. .6325J
(a) Y, = ,.707L, + .7!J7L¿
Var(Yi) = À, = 1.6325
Proportien of total population
'2 = .7n7Z, ~ .707Z2
varianceexpl ained by Yl is
1.6325/(1+1. = ..816
(b) No. The two (standardized) variables contribute ~qually to the
principal components in 8.2(a). The two variables contribute
unequally to the principal components in 8.1 because 'Of their
unequal varian~es.
(c) Py L = .903;
1 1 .
PYl Zz
= .903;.
Py Z = .429
2,
8.3 Ei~envalues of tare 2.' 4. 4. E;genvect~rs assaciate with the ei~en-
values 4. 4 are not unique. One choi~~ is =i = iO 1 O)çnd
:~ =(0 0 1). With these assignments .the 'principal components are
y 1 = Xl' Y 2 = X2 and Y 3 = X3 .
8.4 figenvalues of * are selutions.of 1;-À11 = (a2-Àp-2t~2_ÀH.a2,p)2 = 0
Thus ~0'2-À)H.a2_À)2_2cr4p2J = 0 S'O À = 0'2 'Or À ='"2~lt~hl,). For
À1 = (12,~i = (l/ff,.o,-l/I2J. 'For À2 =O'2(l+i'Ii; ~ fj/Z.)1NEiZ). fer
À~'=a2'tl-pI2). ~~ = 0/". -1/12; 1/12
1.65
Propert ion of Total
Pri nci'fa 1
Component
Vari ance
1 1
02
1/3
. a2(l+pm
1 (1+1'12)
Y 1 = a Xl - 12 X3
1 1 1
1 1 1
Variance Explained
y 2 = "2 Xl + 12 X2 + 2" X3
0'2 (1-.p12)
y 3 = '2 Xl - /ž X2 + 2" X3
1 (l-pl2)
8.5 (a) Eigenva.l ues of 2 satisfy
IE-ul = (l-À)3 + 2.p3 - 3(1:'À)p1 = 0
or (l +29-À)(1-p-À)2 = O. Hence À1 = 1 + 2.p; À2 = À3 = 1 - ?
and results are consistent with (8-16) for p = 3.
1 1
(b) By direct multiplication
.i y.p - y~ -
.ø ( c 1) = (1 + (P-1)9 H c 1 )
thus varidying the fir~t eig~nvalu~-eigenv~ct~~ pair. further
~ :i= (l-p)~i' ; = 2.3....,p .
166
8.6 (a)
Yi = .999xl + .041x2 Sample variance of Yi = -l = 7488.8
variance
Y2 =-.041xl +.999x2 Sample
of Y2 =t =13.8
(b) Proportion of total sample variance explained by Yi is -l/(-l + t) = .9982
(c) Center of constant density ellpse is (155.60, 14.70). Half length of major axis is
102.4 in direction ofyi' Half length of perpndicular minor axis is 4.4 in
direction of Y2'
19 1 .' 2
(d) r)~ x = 1.000, ry" x = .687 The first component is almost completely deterined
by Xi = sales since its variance is approximately 285 times that of X2 = profits.
This is confirmed by the correlation coefficient ry'¡.xi = 1.000.
8.7 (a)
Yi = .707z1 +.707z2
Y2 = .707z1 -.707z2
Sample varance of Yi = -l = 1.6861
Sample
variance
of Y2 =t =.3139
(b) Proportion of total sample variance explained by Yi is -l /(-l + t) = .8431
(c) rýi.i¡ = .918, rÝ1'l2 = .918 The standardized "sales" and "profits" contribute equally
to the first sample principal component.
(d) The sales numbers are much larger than the profits numbers and consequently,
sales, with the larger variance, wil dominate the first principal component
obtained from the sample covariance matrix. Obtaining the principal components
from the sample correlation matrix (the covariance matrix of the standardized
variables) typically produces components where the importance of the varables,
as measured by correlation coefficients, is more nearly equal. It is usually best to
use the correlation matrix or equivalently, to put the all the variables on similar
numerical scales.
167
8.8 (a) rý¡,Zl == êik.J i == 1,2 k = 1,2,.. .,5
Correlations:
i'\k
1
2
1
.732
-.437
2
.831
-.280
4
.604
.694
3
.726
-.374
5
.564
.719
The correlations seem to reinforce the interpretations given in Example 8.5.
(b) Using (8-34) and (8-35) we have
k rk
1
2
3
4
5
.353
.435
.354
.326
.299
r = .353
T= 103.1;: .%;(.01)=21.67 so
r=2.485
would
reject Ho at
the 1%
leveL. This test assumes a large random sample and a
multivariate normal parent population.
lti8
8.9 (a) By (S-lt)
_ ..
2
£! E!!l
e
max LÜi~t) -'
~.r -
( 21) 2 \ n-n1 ) 2 I S 12
The same resul t appl ;ed to each variable independently 9; ves
n
max
e-i
n n n
L(l1. ~O' ..) =
1 11
(2ir)'2 (n-l)2
s~.
n 11
11.
..
i ~o11
~ 1 11 .
p
Under HO ~ max L(ii.rO) = .II L(\1.~a..)
11.+0 1=1
and the 1 i kel i hood ratio stat; stic becomes
. il:fo L(~.tO)
A = 'max Ui =
.-
:~.; -
; =1 11
n
PT
n 5..
. n
and (4-17) \.¡e get
(b) When t = 0'2 I . using (4-l6)
1
max L(\l ,O"~I) =
ii
1 -2aLttr(~n-l)$))
.e
!Y !æ
(2n) 2 (cr2.) 2
1"69
8.9~ Continue)
50
np e
( )np/2 -np/2
max l.(ll ,a2 I) =
-). .0'2
(lr)np/2(n_l )np/2(tr(s))np/2
e -np/2
=
n p
1 np/2
(21T)np/Z (.!) (1 tr (5) )np/2
and the result follows. Under HO there' are P lJ; 's and. '01Ì
v~riance so the dimension of the parameter space;s YO = p. + 1.
The unrestricted case has dimension p + p(p+l)lZ so the X2 has
p(p+l )/2 - 1 = (p+2)~p-l )/2 d. f.
8.10 (a) Covariances: JPMorgan, CitiBank, WellsFargo, RoyDutShell, ExxonMobii
JPMorgan
JPMorgan
CitiBank
WellsFargo
RoyDutShell
ExxonMobil
0.00043327
0.00027566
0.00015903
0.00006410
0.00008897
CitiBank
WellsFargo RoyDutShe1l
ExxonMobi1
0.00043872
0.00017999
0.00018144
0.00012325
o .00022398
0.00007341 0.00072251
0.00006055 0.00050828
0.00076568
Fargo, RoyDutShell, Exxon
Principal Component Analysis: JPMorgan, CitBank, Wells
Eigenana1ysis of the Covariance Matrix
103 cases used
Eigenvalue
Proportion
Cumulative
Variable
JPMOrgan
CitiBank
WellsFargo
RoyDu.tShel1
ExxonMi 1
0.0013677
0.529
0.529
PC1
0.223
0.307
0.155
0.639
0..651
0.00.07012
0.271
0.801
PC2
PC3
0.0002538
0.098
0.899
PC4
0.000142ti
0.055
0.954
PC5
-0.625 -0.326 0.663 -0.118
-0.570 0.250 -.0.414 0.589
-0.345 0.038 -0.497 -0.780
0.248
0.309 -0.149
O. .642
0.094
0.322 -0.64ti -0.216
0.0.001189
.0 .04ti
1.000
170
(b) From par (a),
~ = .00137 t = .00070 .t = .00025 14 = .00014 is = .00012,
(c) Using (8-33), Bonferroni 90% simultaneous confidence intervals for Âi Â. ~ are
íl: (.00106, .00195)
Â.: (.00054, .00100)
~: (.00019, .0036)
(d) Stock returns are probably best summarized in two dimensions with 80% of the
total variation accounted for by a "market" component and an "industry"
component.
8.11 (a)
3.397 - 1. 102
9.673
s=
4.306 - 2.078
.270
-1.513 10.953 12.030
55.626 - 28.937
-.440
89.067
9 :570
31.900
(Symetric)
(b)
~ = 108.27
A
ei
t =43.15
.t = 31.29
14 = 4.60
is = 2.35
ê3
ê4
ês
ê2
.
-0.037630
0.118931
-0.479670
0.858905
0.128991
0.554515
-0.062264 0.040076
-0.249442 -0.259861 -0.769147
-0.759246 0.431404 -0.-027909
o .068822
-0.315978 0.393975
0.308887
-0.767815
-0.507549
0.828018
0.514314
-0.081081
-0.049884
-0.202000
171
.91 =-.038xl +.119x2 -.480x3 +.8S9x4 +.129xs
5'2 =-.062xl -.249x2 -.759x3 -.316x4 -.508xS
(c) Correlations between variables and components:
Xl
X2
X3
X4
Xs
r.y¡,x;
-.212
.398
-.669
.947
.238
r.Y2,X¡
-.222
-.527
-.669
-.220
-.590
The proportion of total sample variance explained by the first two principal
Components is (108.27+43.15)/(108.27+43.15+31/29+4.60+2.35)=.80.
The first component appears to be a weighted difference between percent total
employment and percent employed by government. We might call this
component an employment contrast. The second component appears to be
influenced most by roughly equal contributions from percent with professional
degree (X2), percent employment (X3) and median home value (xs). We might
call this an achievement component. The change in scale for Xs did not appear
to have much
affect on the first sample principal component (see Example 8.3)
but did change the nature of the second component. Variable Xs now has much
more influence in the second principal component.
172
-2. 768
2 . 500
8.12
(300.
51G)
- .378
-.4'64
- .586
-2.23'5
.171
. 3.914
- 1 .395
6.779
30.779
..624
1 .522
.673
2.316
2.822
.142
1.182
1 .089
,:.811
.177
11 .364
3 . 1 33
1 .04'5
3Q.978
..593
S =
.479
(Symmetrk)
1.0
-.101
1.0
-. 1 94
- .27-0
- .110
- .254
. 15'6
.183
- .074
.11£
.319
.052
.502
.557
.411
.166
.297
- .1 34
.235
.167
.448
1.0
1.0
1.0
R =
1.0
( Symetri c )
.154
1.0
Using $:
~1 = 304.2£; ~2 = 28.28; ~3 = ll.4~; ~4 = 2.52; ~~ = 1.28;
~6 = .53; 5:7 = .21
The first sampl-e princi-pal component
,.
Y1 = -.Oinxi +.993x2 +.014x3 -.OO5x4 +.024xS +.112xii +;OO2x7
accounts f-or 87% of the total sampl-e variance. Tliefirst .c'Ompont is
essentially IIso1ar r-adiation". ~Nete t~ large sample varianc~ f"()r x2
in S).
173
Usingji:
,. A
~1 = 2 .34; ~2 '=: 1.39;
,.
,. A
1.3 = 1.20; '"4 = .73; À5 = :65;
À£ = .54; À 7 = .16
The first thre,e sample
principle components are
A
Yl = .~37Z1 -.~05z2 -.551z3 ,-.378z4 -.498zS -.324z6. -.319z7
,.
Yi = -.278z,- +.527z2 +.007z3 -.435z4 -..199z5 +.5S7zti .-.308z7.
,.
Y3 = .ó44z1 +;225z2 -.113z3 -.407z4 +.197z5 +.1~9z6 +.541z7
These components ~cceunt fer 70% of the total sample vari ance.
The first camponent contrasts "\'/ind" with the. remaining
variables. It might be some general measur.e of the pol1uti()n
level ~ The second component is largely cemposed of "solar
radiati,on".. and the pollutants "NO" and Iln3". It might represent
the effects of solar radiåtion since solar radiation is involved
in the production of NO and D3 fro!l the other pollutants. The
third 'c-omponent is -eampos-d largely of ii..tind" and certain pollu-
tants (e.g. "NO" and "He"). It might represent a wi~ transport
. effect. A "better" interpretation of the components \'iould depend
on more .extensive subject matter knowledge.
The data can be eff€ctive1y summarized in three or few~r
dimensions. The choice of S' or R makes a difference.
174
8.13
(a) Covariane Matrix
XL
X2
XL
X2
X3
4.6'54750889
0.93134537C
0.931345370
0.612821160
0.110933412
o .589699088
O. 1184"69052
o .087004959
l1C933412
0.571428861
o .
X4
" .58g699088
'0.276915309
X5
1 .074885"659
o .388886434
X6
0.15815'0852
-0.024851988
0.347989910
0.110131391
X4
X5
X6
0.276915309
0.118469052
1 .074885659
o .388886434
o .087004959
0.347989910
0.110409072
0.21740"5649
o . 217405"649
.0.862172372
-0.008817'694
X3
XL
X2
X3
X4
X5
X6
0.021814433
o .
15815.Q52
-0.024851988
o .
11'(131391
0.021814433
-0.008817694
0.861455923
Correlati~n Matrix
XL
X2
X3
14
X5
X-ô
XL
1 .0000
o . 5514
0.3616
o . 53"66
0.0790
X2
X3
0.5514
1.0000
0.1875
o .3863
o .4554
o .3464
- . 0342
-0. 157"
1.0000
o .5350
o .4958
o .704'6
0.7'Û46
1 . 0000
- .0102
0.0707
-.0102
1 . 000
)(4
X5
X6
o .3616
o .3863
o . 4554
o . 5350
o .536'
0.0790
- . 0342
o . 1875
1.'ÛOOO
o . 3464
0.4958
0.1570
0.0707
(b) We wil work with R since the sample variance of xl is approximately 40 times lai.ger
than that of x4.
Eigenvalues of the Correlation Matrix
PRIN1
PRIN2
PRIN3
PRIN4
PRINS
PRIN6
Proportion
'Cumula t i va
0.47738
0.77764 0.12733
0.477385
0.179408
0.129607
0.65031 0.2"6228
0.1-08386
0.89479
0.38803 0.14478
o . '064672
o .040543
0.9594"6
Eigenvalue Difference
2.86431 1.78786
1 .-07"645 0 .29881
o . 2432"6
o . 65'679
o .78640
1 .00000
175
Eigenvectors
PRIN2
PRINl
PRIN3
PRIN4
PRIN5
-.551149
-.061367
-.421060
0.665604
-.600851
PRING
O. 146492
o . 687297
o .076408
0.331839
0.211635
o .532689
- .116262
o .4458
- .026600
0.339330
o .498607
X4
o .429300
o .358773
o . 402854
-.291738
0.380135
XS
0.521276
XL
X2
X3
X6
- . 020959
- . 073090
o . 873960
o .055877
-.628157
- .124585
- .203339
o .200526
-.207413
-.103175
o .429880
0.178715
o . 053090
-.794127
(c) It is not possible to summarize the radiotherapy data with a single component. We
nee the
fit four components to summarize the data.
(d) Correlations between principal components and Xl - X6 are
PRINl
PRIN2
PRIN3
PRIN4
-0.02766
-0 .302ti8
X4
o . 78335
X5
o .88222
o . -09457
0.39440
-0.02175
-0.07646
0.43969
-0 .55393
-0.10986
-0.17931
-0.44446
-0.04949
X3
o .75289
o .72056
o .60720
o . 29923
o . 90675
o .37909
XL
X2
X6
-0.339"55
o .53'67"6
0.16171
0.14412
8.14 S is given in Example 5~Z_
~l = 200.5, ~2 = 4..5. . Å3 = 1.3
The first sample principal component explains a proporticn
AI J
200.5/(200.5 + 4.5 + 1.3) = .97 of the total sample variance.
Also,
=1 = (-.051. -.998. .029
,.
Hence Yl = -.051x1 -.998x2 +.029x3
176
The first principal cQmponent is essentially Xz = sQdium content.
"s"dium in S). A
(NQte the (r,elativ.ely) large sample vtlriance for
Q_Q plot of the Yl values is shown bel-ow. Theseàata appear to
be approximately normal with no suspect observations.
o.
,.
Yl (1)
*
-'15.
w
..
w
..
-30.
li
'¡w
"...
*
oW
'f'
-45.
;¡
'I'
** *
..
..
-60. '
** **
w
'..
*
...
.,.
-75.
1
-2.0
-1.0
1
I
0.0
1.0
,.
Q-Q plot for Yl.
2.0
i..i~
3.\)
q(i )
177
1088.40
8.15
831 .28
1128.41
S =
7'63.23
784.09
850.32
92'6.73
1336.15
904.53
(Symmetri c)
1395.1"5'
~ A A A
À1 = 3779.01; À2 = 4'68.25; À3 = 452.13; À4 = 24~.72
Consequent1y~ the first sample principal component aCt:ounts for a
proportion .3779.01/~948.l1 = .76 of the total sample variance.
A 1 so ,
""
:1 = (.45. . .49. .51, .53)
Co nsequent 1 y ~
,.
Y, = .45xi + .49x2 + .5lx3 + .53x4
The interpretation of the first component is the same as the
interpretation of the first component, obtained from R. in
Example
8.6. (Note the sample variances in S are nearly equal).,
178
8.16. Principal component analysis of Wisconsin fish data
.(') An are positively correlated.
(b) Principal component analysis using xl - x4
Eigenvalues -of R
2.153g 0.7875 0.6157 0.4429
Eig~nvectors of R
O. 7~32 0 . 4295 O. 1886 -0.7'071
0.6722 0.3871 -0.4652 ~.4702
0.5914 -0.7126 -0.2787 -0.3216
0.6983 -0.2016 0.4938 0.5318
pel pc2 pe3 pe4
St. Dev. 1.4676 0.8874 0.7846 0.66£5
Prop. of Vax. 0.£385 0.1969 0.1539 0.1107
Cumulative Prop. 0.5385 0.7354 0.8893 1.0000
The first principal component is essentially a total of all four. The second contrasts
the Bluegil and Crappie with the two bass.
(c) Principal component analysis using xl - x6
Eigenvalues of R
2.3549 1.0719 0.9842 0.6644 0.5004 0.4242
Eigenvectors of R
-0.6716 0.0114 0.5284 -0.'0471 0.3765 -0.7293
-0.6668 -0.0100 0.2302 -0.7249 -0.1863 0.5172
-0.5555 -'0.2927 -0.2911 0 .1810 ~O. 6284 -0.3'081
-0.7'013 -'0.0403 0.0355 0.6231 0.34'07 '0.5972
0.3621 -0.4203 0.0143 -0.2250 0.5074 0.0872
-'0.4111 0.0917 -0.8911 ~O.2530 0.4021 -0.1731
pe 1 pe2 pe3 pc4 peS pe6
St. Dev. 1.5346 1.0353 0.9921 0.81£1 0.7074 0.6513
Prop. of Var. (). 3925 0.1786 0.1640 0.1107 0.0834 0.0707
Cumulative Pr~p. '0.392'0.5711 0.7352 0.84£9 0.9293 1. 0000
The \Va.liey~ is eontrasted with aU the others in the first principal eompoont ,look at
theLOvariance pattern). The
second principal component is essentially the 'Walleye and
somewhat th,e largemouth bas. The thkd principal component is nearly a contrast
betV'æ..n Northern pike and BluegilL
179
8.17
COVARIANCE MATRIX
-----------------
xl
x2
x3
x4
xS
x6
..Q13001'6
.0103784
..Q223S.Q0
.0200857
.0912071
..0079578
.0114179
.0185352
.0210995
.0085298
.0089085
.0803572
.06677"62
.0168369
.0128470
.0694845
.0177355
.0167936
.0115684
.0080712
.01'05991
The eigenvalues are
o .lS4
0.018
0..008
o .003
0.0.02
0.001
and the first two principal components are
.218 , .204, .673, .633 , .181 , .159
.337 , .432 , -.500 , .024 , .430 , .514
-x
x
...
180
8.18 (a) & (b) Principal component analysis of the correlation matrix follows.
Correlations: 100m(s), 200m(s), 400m(s), 800m, 1500m, 3000m, Marathon
100m(s)
().941
200m(s)
400m(s)
800m
1500m
3000m
Mara thon
200m(s)
400m(s)
800m
1500m
3000m
0.909
0.820
0.801
0.732
0.680
0.806
0.720
0.674
0.677
0.905
0.867
0.854
0.973
0.791
0.799
0.871
0.809
0.782
0.728
0.669
Eigenanalysis of the Correlation Matrix
0.0143
0.6287 0.2793 0.1246 0.0910 0.0545
0.002
Eigenvalue 5.8076
0.008
0.013
0.018
0.040
0.090
1.000
proportion 0.830
0.998
0.990
0.977
cumulative 0.830 0.919 0.959
Variable
100m(s)
200m(s)
400m(s)
800m
1500m
3000m
Mara thon
PC1
0.378
0.383
0.368
0.395
0.389
0.376
0.355
PC2
-0.407
-0.414
-0.459
0.161
0.309
0.423
0.389
PC3
0.141
0.101
-0.237
-0.148
0.422
0.406
-0.741
PC4
-0.587
-0.194
PC5
0.167
-0.094
-0.327
0.819
-0.026
-0.352
-0.321 -0.247
0.645
0.295
0.067
0.080
Pe6
PC7
0.089
0.745 -0.266
0.127
-0.240
0.017 -0.195
0.731
0.189
-0.240 -0.572
0.082
0.048
-0.540
)71 = .378z1 + .383z2 + .368z3 + .395z4 + .389z5 + .376z6 + .355z7
)72 =-A07z1 -A14z2 -AS9z3 +.161z4 +.309z5 +A23z6 +.389z7
Zi
Zz
Z3
Z4
r.Yi,l;
.911
.923
.887
.952
r.Y2'Z¡
-.323
-.328
-.364
.128
Z6
'l7
.937
.906
.856
.245
.335
.308
Z5
Cumulative proportion of total sample varance explained by the first
two components is .919.
(c) All track events contribute about equally to the first component. This
component might be called a track index or track excellence component. The
second component contrasts the times for the shorter distanes (100m, 200m
400m) with
the times for the longer distances (800m, 1500m, 3000m, marathon)
and might be called a distance component.
(d) The "track excellence" rankings for the first 10 and very last countries follow.
These rankings appear to be consistent with intuitive notions of athletic
excellence.
1. USA 2. Germany 3. Russia 4. China 5. France 6. Great Britain
7. Czech Republic 8. Poland 9. Romania 10. Australia .... 54. Somoa
nn
8.19 Principal component analysis of the covariance matrix follows.
Covariances: 100m/s, 200m/s, 400m/s, 800m/s, 1500mls, 3000m/s, Marmls
3000m/s
1500ml s
800m/s
40 Oml s
200m/s
100ml s
0.0905383
lOOmIs
o .0956u63
200m/s
400m/s
800m/s
Marml s
0.0966724
0.0650640
0.0822198
0.0921422
0.0810999
Marml s
0.1667141
l500m/s
3000m/s
o . 114'6714
0.1377889
0.1138699
0.0749249
o . -0809409
0.0735228
0.10831'64
0.0997547
0.0943056
0.0954430
0.0%01139
0.1054364
0.0933103
0.1018807
0.08'64542
0.12384.Q5
0.1765843
0.1465604
0.1437148
0.1184578
Marml s
Eigenanalysis of the Covariance
Eigenval ue
Proportion
Cumulati ve
Variable
lOOmIs
20 Oml s
400m/s
800m/s
1500m/s
3000m/s
Marml s
0.73215
0.829
0.829
PC1
0.310
0.357
0.379
0.299
0.391
0.460
0.423
0.08607
0.097
0.926
PC2
Matrix
0.01498
0.017
0.981
0.03338
0.038
0.964
PC3
-0.376 0.098
-0.434 0.089
-0.519 -0.274
0.053 -0.053
0.435
0.211
0.427
0.396
0.445 -0.730
PC4
0.00885
0.010
0.991
0.00617
0.007
0.998
PC5
PC6
PC7
0.127
0.236
-0.199
0.081
-0 . 499
0.00207
0.002
1.000
-0.585 -0.046 -0.624 0.138
-0.323 -0.030 0.689 -0.311
0.132
0.667 -0.187 -0.124
0.894 -0.136 -0.2'65
0.128
0.055
0.184
-0.237
-0.357
-0.136
0.734
0.095
5'1 =.3 lOx¡ + .357 x2 + .379x3 + .299x4 + .391xs + .460x6 + .423X7
5'2 =-.376x¡ -.434x2 -.519x3 +.053x4 +.21 IXs +.396x6 +.445x7
Xl
I
X2
X3
X4
Xs
X6
X7
r.YllXi
.882
.902
.874
.944
.951
.937
.886
r.Yi,X¡
-.367
-.376
-.410
.057
.176
.276
.320
Cumulative proportion of total sample variance explained by the first two
components is .926.
The interpretation of the sample component is similar to the interpretation in
Exercise 8.18. All track events contribute about equally to the first component.
This component might be called a track index or track excellence component.
The second component contrasts times in mls for the shorter distances (100m, 200m
400m) with the times for the longer distances (800m, l500m, 3000m, marathon)
and might be called a distance component.
The "track excellence" rankings for the countries are very similar to the rankings
for the countries obtained in Exercise 8.18.
182
8.20 (a) & (b) Principal component analysis of the correlation matrix follows.
Eigenanalysis of the Correlation Matrix
Eigenvalue 6.7033
proportion 0.838
cumulative 0.838
0.'6384
Variable
PC2
PC1
0.332
0.346
0.339
0.353
0.366
0.370
0.366
10,000m
Marathon 0.354
100m
200m
400m
800m
1500m
5000m
0.080
0.918
0.529
0.470
0.345
-0.089
-0.154
-0.295
-0.334
-0.387
0.2275
0.028
0.946
0.2058
0.026
0.972
PC3
PC4
o .0976
0.012
0.984
PC5
0.300
0.0707
0.009
0.993
PC6
-0.362
o .04'69
0.OQ6
0.0097
0.001
1.000
PC7
PC8
0.999
0.348
-0.381
-0.217 -0.541 0.349 -0.440
o . 114
0.077
0.133
0.851
-0 .067
0.259
-0.783 -0.134 -0.227 -0.341 -0.147
0.530
0.652
-0
.
233
-0.244
0.072 -0.359 -0.328
0.055
0.183
0.087 -0.061 -0.273 -0.351
0.244
0.594
0.375
0.335 -0.018 -0.338
0.344
-0.004
-0.066
0.061
-0.003
-0.039
-0.040
0.706
-0.697
0.069
Yi = .332z1 + .346('2 + .339 ('3 + .353z4 + .366z5 + .370('6 + .366z7 + .354z8
Y2 =.529z1 +.470'2 +.345z3 -.089z4 -.154z5 -.295z6 -.334z7 -.387('8
Zs
('6
('7
('8
.878
l4
.914
.948
.958
.948
.917
.276
-.071
-.123
-.236
-.267
-.309
('1
Z2
('3
r.YI,Z¡
.860
.896
r.Y2'Z¡
.423
.376
Cumulative proportion of total sample variance explained by the first
two components is .918.
(c) All track events contribute aboutequally to the first component. This
component might be called a track index or track excellence component. The
second component contrasts the times for the shorter distances (100m, 200m
400m) with the times for the longer distances (800m, 1500m, 500m,
lu,OOOm, marathon) and might be called a distance component.
(d) The male "track excellence" rankings for the first 10 and very lasti:ountris
follow. These rankings appear to be consistent with intuitive notions of athletic
excellence.
1. USA 2. Great Britain 3. Kenya 4. France 5. Australia 6. Italy
7. Brazil 8. Germany 9. Portugal 10. Canada ....54. Cook Islands
The principal component analysis of
the women.
the men's track data is consistent with that for
183
component analysis of the covariance matrx follows.
8.21 Principal
Covariances: 1oom/s, 2oom/s, 400m/s, 8oom/s, 1500mls, 5000mls, 1o,oom/s,lfiJQ~~
10Om/s 200m/s 40Om/s 800m/s 1500m/s
0.0434979
0.0482772
0.0434632
0.0314951
0.0425034
0.0469252
0.0448325
0.0431256
100m/s
200m/s
400m/s
800m/s
lS00m/s
5000m/s
10,OOOm/s
Marathonm/S
5000../5
10,OOOm/s
Marathonø/S
0.0648452
0.0558678
0.0432334
0.0535265
0.0587731
0.0572512
0.0562945
0.0688217
0.0428221
0.0537207
0.0617664
0.0599354
0.0567342
0.0761i388
0.0745719
0.0736518
0.0942894
0.0909952 0.0979276
Covariance Matrix
Eigenanlysis of the
0.01391
0.024
0.947
Eigenvalue 0.49405 0.04622
proportion
0.0729140
10,OOOm/s Marathonm/s
5000../s
0.0959398
0.0937357
0.0905819
cumlative
0.0468840
0.0523058
0.0571560
0.0553945
0.0541911
0.079
0.923
0,844
0.844
0.01332
0.023
0.970
0.00752
0.013
0.983
0.00575
0.010
0.993
0.00322
0.006
0.998
Eigenvalue 0.00112
proportion
cuulative
Variable
10Om/s
200m/s
400m/s
BOOm/s
1500m/s
5000m/s
10,OOOm/s
Marathonm/s
0.002
1. 000
PCL
0.244
0.311
0.317
0.278
0.364
0.428
0.421
0.416
pc3
PC2
-0.432
-0.523
-0.469
0.173
0.235
-0.684
0.436
0.439
-0; 033
0.063
0.261
0.310
0.387
-0.111
-0.187
-0.128
pc4
pc5
PC6
-0.450 -0 .390
-0.318 0.341
0.420 0.046
0.332
0.543
0.317 -0.303
-0.016 -0.374
-0.100 -0.215
-0.339 0:584
0.119
-0.247
0.177
pC7
0.584
-0.535
0.039
pc8
-0.119
0.096
-0.008
-0.070
-0.044
-0.368 0.432
0.608 -0.327
-0.334 -0.006 0.'696
-0.352 -0. ,180 -0.6,93
0.074
0.215
0.391
j\ = .244xl + .311x2 +.317 X3 + .278x4 + .364xs+ .428x6 + .421x7 + .416xs
5'2 =-.432xl -.'S23x2 -.469x3 -.033x4 +.063xS +.261x6 +.3lOx7 +.387xs
Xl
X2
X3
X4
Xs
X6
X7
Xs
r.YI,X¡ ,
.822
.858
.849
.902
.948
.971
.964
.934
r.Y2'X¡
-,445
-.442
-.384
-.033
.050
.181
.217
.266
Cumulative proportion of total sample varance explained by the first two
components is .923.
The interpretation of the sample component is similar tt) the interpretatìon in
Exercise 8.20. All track events contribute about equally to the first component.
This component might be called a track index or track
excellence component. ,
The second component contrasts times in rns for the shorter distances (100, 200
400m, 800m) with the times for the longer distances (1500m, 'SooOm, 10,0Q,
marathon) and might be called a distance component.
The "track excellence" rankings for the countries are very similar to the rankings
for the countres obtained in Exercise 8.20.
184
8.22
Using S
Eigenvalues of the CovarianeeMatrix
Cumulative
Eigenvalue
Oi ffe renee
proportion
20579.6
4874.7
5.4
15704.9
2.8
0.4
0.808198
0.191437
0.000213
0.000130
0.000018
0.1
o . 000003
PRIN1
PRIN2
PRIN3
PRIN4
PRINS
PRIN6
PRIN7
~!!S!!.2
2.1
3.3
0.5
0.1
./
0.000000
0.0
Eigenveetors
X3
X4
X5
X6
X7
X8
X9
PRIN3
PR IN4
PRINS
PRIN7
PRIN2
PRIN6
PRIN1
0.005887
0.487047
o . 009680
0.286337
0.608787
_ .003227
_.425175
0.311194
o .535569
o . 000444
o . 008388
-.509727
-.000457
0.010389
0.024592
_.000253
~
o . 008526
0.003112
0.000069
0,009330
(Õ. e72697 .;
0.029196
0.004886
_ .000493
0.008577
_ .487193
- .034277
0.904389
0.133267
_ ,018864
0.284215
0.004847
0.593037
0.390573
0.011906
_.748598
-.005597
o . 002665
-.005278
O. 855204
0.043786
0.082331
_.000341
Plot of Y1.Y2. Symbol is value of X1.
(NOTE: 10 obs hidden. I
2500
8
Y1
8
1
8115
2000
5
8 1
5
1
8
81 8 8
18
5
885
5111 11 551
51
15 111 1 1 8
155
55 18
1
1
8
8
8 1
8
8
8
8
8
1
5
5
1500
-100
a
100
Y2
200
300
0.014293
_.037984
0.998778
0.013820
_ . 000256
yrhgt
ftfrbody
prctffb
fraiie
bkfBt
saleht
salewt
185
8.22 (C"Ontinued)
Using R
~igenvalues of the ~orrelation Matrix
Ugenvalue
Difference
Proportion
'Cuaiulative
2.78357
0.581171
0.191018
0.105912
0.060204
0.58867
0.77969
4.12070
1.33713
0.74138
0.42143
0.18581
0.14650
0.04706
PRINI
PRIN2
PRIN3
PRIN4
PRINS
PRIN6
PRIN7
o . 59575
0.31996
o .~3562
o . 88560
0.94580
0.97235
0.99328
o . 02644
0.03930
0.09945
0.020929
0.006722
1 .00000
Eigenvectors
X3
X4
X5
X6
X7
X8
X9
PRINS
PRIN6
PRIN7
0.065871
_.072234
_.177061
0.127800
_.434144
0.208017
0.799288
-.276561
0.774926
0.017768
PRINl
PRIN2
PRIN3
PRIN4
0.449931
0.412326
0.355562
0.433957
...186705
0.452854
0.269947
... 042790
0.129837
-.415709
- .038732
0.113356
0.247479
0.314787
0.242818
0.618117
_ . 176650
-.215769
-.109535
0.253312
_ . 582433
0.290547
0.450292
0.568273
_.452345
..315508
0.007728
0.714719
0.101315
0.600515
Plot
Syiibol
of Vl.Y2.
-.719343
0.579367
0.142995
0.160238
yrhgt
o . 042442
ftfrbody
pr(:tffb
freiie
bkfat
- . 236723
0.047036
sa leht
salewt
- .002397
- . 582337
is value of Xl.
(NOTE: 27 obs hidden.)
1200
8
8 8
8 88181
1000
8 118851 8151
Vl
1
800
8 88811111 1 8 15 1
1111155 55
1 1 5
5
600
i
i
800
900
1000
1200
1100
1300
V2
Plot of VL.02.
(NOTE:
Syiibol used
is
Plot of Yl.02.
(NOTE:
FOA S
36 obs hidden.)
38 obs hidden.)
1200
2500
VL
SymbOl used is
hi( ~
*****
1000
VL
2000
. .
800
.*. ...
.......
......
........
***......
.- .. ..
600
1500
1
-3
.2
-1
0
.02
2
3
.3
.2
i
i
-1
a
Q2
1
2
3
1~6
8.23 a) Using S
Eigenvalues of S
4478.87 152.47 32.32 8.12 1.52 0.54
Eigenvectorsof S (in colums)
-0.849339
-0.368552
-0.194132
-0.314€78
-0.043918
-0.064458
0.470832 -0.22€606 0.074260 -0.008692 -0.000202
-0.846078 -0.368132 0.012754 -0.110784 -0.019105
-Q.058127 0.303143 -0.928388 -0.012289 -0.070597
-0.216748 0.848576 0.355060 -0.082353 0.032666
-'0.060354 0.001815 -0.060162 0.440119 0.892805
-0.092026 0.033880 0.052267 0.887138 -0.443264
The first component might be identified as a "size" component. It is domiated
by Weight, Body lengt and Gir, those varables with the largest sample
varances. The first component explains 4478.87/4673.84 = .958 or 95.8% of
the
total sample varance. The second component essentially contrasts Weight with
the remaining body size varables, Body length, Neck, Gir, Head lengt,
component
and Head width, although the sample correlation between the second
and Neck is small (-.05). The first two components explain 99.1 % of
the total
sample varance.
These body measurement data can be effectively sumarze in one dienion.
b) Using R
R
1.0000
0.8752
0.9559
0.9437
0.9025
0.9045
0.8752
1.-0000
0.9013
0.9177
0.9461
0.9503
0.9559
0.9013
1.0000
0.9635
0.9270
0.9200
0.9437
0.9177
0.9635
1.0000
0.9271
0.9439
O. 9025
0.9461
0.9270
0.9271
1.0000
0.9544
0.9045
0.9503
O. 9200
0.9439
0.9544
1.0000
Eigenvalues of R
5.6447 0.1758 0.0565 0.0492 0.0473 0.0266
Eigenvectors of R (in colums)
-0.558334
0.532348
-0.409938 -0.389366
-0.411999 -0.222694
-0.4091 £2 0.318718
-0.41'0333 0.319513
-0. 403'672
-'0.4'04313
0.286817 0.261937 -0.598371 0.128024
-0.186741 0.719785 .0.0-04276 0.012490
0.035396 0.073950 -0.561034 -0.599053
-0.581252 -0.228969 0.231095 0.580499
0.695916 -0.291938 0.251473 0.313431
-0.243840 -0.519785 -0.458838 -0.435168
l87
8.23 (Continue)
Again, the first principal component is a "size" component. All varables
contribute equally to the
first component. This component explains
5.6447/6 = .941 or 94.1 % of
the total sample variance. The second principal
component contrasts Weight, Neck and Girth with Body length, Head lengt
and Head width. The first two components explain 97% of
the total sample
variance.
These data can be effectively sumarzed in one dimension.
c) The results are similar for both the covarance matrx S and the correlation
matrx R. The fist component in each analysis is a "size" component and
aInost all of
the varation in the data. The analyses differ a bit with respect
to the second and remaining components, but these latter components explain
very little of the total sample varance.
188
8.24 An ellipse format chart based on the first two principal.cmponents of the Madison,
Wisconsin, Police Department data
XBAR
3557.8 1478.4 2676.9 13563.6 800 7141
S
-72093.8
367884 .7
-72093..8
85714.8
222491.4
1399053.1
43399 .9
139692.2 -1113809.8
1698324. 4 ~244 785.9
-44908 . 3
110517.1
101312.9
11'61018.3
-244785.9 224718 .~ 4277'67 .S
-462615.6 42771)7 .5 24138728.4
85714.8
222491.4 -44908 .3
43399 .9
139692.2 110517 .1
1458543.~ -1113809.8 330923.8
330923.8
1079573.3
1~1312. 9
111)1018.3
1079573.3
-4'6261S .6
Eigenvalues of S
4045921.9 2265078.9 761592.1 288919.3 181437.0 94302.6
Eigenvectors of S
-0.0008 -0.0567 -0.5157 0.6122 0.4311 -0.4126
-0.3092 -0.5541 0.5615 0.4932 -0.1796 -0.0810
-0.4821 0.3862 -0.3270 0 .3404 -0.5696 0 . 2667
0.3675 -0.6415 -0.4898 -0.0642 -0.4308 0.1543
-0'.1544 0.0359 -0.0316 -0.3071 -0.4062 -0.8453
-0.711)3 -0.3575 -0.2662 -0.4094 0.3269 0.1173
Principal components
yl y2 y3 y4 y5 y6
1 1745.4 -1479.3 618.7 222.6 7.2 178.1
2 -1096.6 2011.8 652.5 -69.5 636.9 560.2
3 210.6 490.6 365.8 -899.8 -293.5 -15.2
4 -1360.1 1448. 1 420.1 523.5 -972.2 88.5
5 -1255.9 502.1 -422.4 -893.8 359.9 -273.7
6 971.6 284.7 -316.9 -942.8 -83.5 -70.1
7 1118.5 123.7 572.9 319.9 -60.8 -598.5
8 -1151.6 1752.0 -1322.1 700.2 -242.2 -158.8
9 -497.3 -593.0 209.5 -149.2 101.6 -586.2
10 -2397.1 1819.6 -9.5 -147.6 -109.9 207.8
11 -3931.9 -3715.7 924.1 35.1 -274.2 152.9
12 -1392.4 -1688.0 -2285.1 372.1 444.0 85.2
13 326.8 650.8 1251.6 728.8 809.S -140.0
14 3371.4 -379.1 -499.9 -114.6 -324.3 286.9
15 3076.S -199.1 -105.7 419.8 -122.3 3.4
16 2261.9 -1029.3 -53.7 -104.5 123.8 279.6
189
2.5 X 10-7 yl + 4.4 x lL-7 yi = 5.99
The 95% 'Control ellipse base on the
first two principal.cmponents of overtime hours
ooo
~
ooo
"'
-400
o
2000 4000
y1
8.25 A control chart based on the sum of squares dij. Period 12 looks unusuaL.
Sum of squares of unexplained t:omponent of jth deviation
.
It
~
0
~
.. M
~
en
en
iq
0
d
.
.
.
.
2
.
.. ..
4
6
.
8
Period
..
1-0
.
12
14
1'6
190
8.26 (a)-(c) Principal component analysis ofthe correlation matrix R.
Correlations: Indep, Supp, Benev, Conform, Leader
Indep
-0.173
-0.561
-0.471
0.187
Supp
Benev
Conform
Leader
Supp
Benev Conform
0.018
0.298
-0.327
-0.401
-0.492 -0.333
Cell Contents: Pearson ~orrelation
Principal Component Analysis: Indep, Supp, Benev, Conform, Leader
Eigenanalysis of the Correlation Matrix
1. 3682
0.439
0.274
0.713
0.7559
0.151
0.864
PCL
PC2
PC3
Eigenvalue 2.1966
0.439
l'ortion
Cumulative
Variable
Indep
Supp
Benev
Conform
Leader
0.5888 0.0905
0.118 0.018
1.000
0.982
PC4
PC5
-0.521 0.087 -0.667 -0.253 -0.460
0.351 -0.454
0.187
0.788
0.121
0.115 -0.733 -0.386
0.548 -0.008
0.525 -0.451
0.439 -0.491 -0.295
-0.469 -0.361 0.648 0.007 -0.480
Using the scree plot and the proportion of variance explained, it appears as if 4
components should be retained. These components explain almost all (98%) of
the variabilty. It is difficult to provide an interpretation of the components
without knowing more about the subject matter. All four of the components
represent contrasts of some form. The first component contrasts independence
and leadership with benevolence and conformity. The second component
contrasts -support with conformty and leadership and so on.
SG-llot of Indap,
t.o
0;5
0.0
1
2
3
Component Number
4
5
191
. Scatterplot of y2hatvs 11l1lt .. .
. 1
U'
.... . .. 2
.
-:
..
.' .
.
. ..
..
.
.. .... . ...
. ..
-3
..
-4
.. ..
.., ... .~
. . .
. ..
. '.. .
.
.
"
.. .
.' .
fi ..I. .. .
..
"
..
.2 yII1
-I
~3
. ...
o
2
1
3
.':. -',::
.:--:'-.,
.. .... .. .."", ," ".." .: - -,-: ~\
'" - " .:---- ,::'--'.......
....._-,.
.__,..___,-::-":___.:::_,'::'/-.:--d"::,":-:
SCàlterplot òfy2h:al vsyiihat
.
.
.
.
.
.'
-:
.. .
.
.
,., .. .~
. . .
.' . .
. ..
..
/till
o
.
-4
.3
. ..
-2
..
.
.. ....
. ...
., .
.
. .-
..
.
yll
.. e.
.
.
"
...
.
..
". .. .
.. .. . .
i
3
The two dimensional plot of the scores on the first two components suggests that
the two socioeonomic levels cannot be distinguished from one another nor can
the two genders be distinguished. Observation #111 is a bit removed from the
rest and might be called an outlier.
192
covarance matrix S.
(a)-(d) Principal component analysis of the
Coyariances: Indep, Supp, Seney, Conform, Leader
Indep
34.7502
-4.271;7
Inde
Supp
Benev
Conform
-18.0718
-15.9729
5.7165
Leader
Benev
Conform
29.8447
9.3488
-13.9422
33.0426
Supp
17.5134
0.4198
-7.8682
-8.7233
-9.9419
Leader
26.9580
Principal Component Analysis: Indep, Supp, Seney, Conform, Leader
Eigenanalysis of the Covariance Matrix
68.752
0.484
0.484
Eigenvalue
Proportion
Cumulative
Variable
PC1
Indep
-0.579
Leader
-0.380
0.042
0.524
0.493
Supp
Benev
Conform
31. 509
0.222
0.706
23.101
0.163
0.868
pc3
PC2
-0.643
0.140
0.119
-0.422
0.079
0.612
0.219
-0.572
16.354
0.115
0.983
PC4
0.309
-0.515
0.734
-0 .304
0.090
0.612
-0.494
2.392
0.017
1.000
pc5
0.386
0.583
0.352
0.398
0.478
Using the scree plot and the proportion of variance explained, it appears as if 4
components should be retained. These components explain almost all (98%) 'Of
the variabilty. The components are very similar to those obtained from the
correlation matrix R. All four of the components represent contrasts of some
form. The first component contrasts independence and leadership with
benevolence and conformity. The second component contrasts support with
conformity and leadership and so on. In this case, it makes little difference
whether the components are obtained from the sample 'Correlation matrix or
the sample covariance matrix.
of ItidèP# -.1 LeaCler--Cv Mamx
Scre Plot
50
11
i "1
~
.=i 30
¡¡
20
10
o
1
2
3
CompolientNumbe
4
5
193
Scatterplbt of y2hatcov vs ylhatcov
15
. 1
. 2
..
.
...
. ..l.
u. So'.
....... .
~ .
.. .
... ..... -.
".
... ....
. ..
.. . .
. .. _... .. e.- .
..
. ..
.
.. . .
.. . ..
. . . ".
""
#111
ø
. ..
1#
~
l lö4
-15
-20
.10
io
o
y1hav
20
~
.Sctterplot of y2hatatv VB y1hàtcv
Lj
15
to
..
.-. . .
.. .
. . _'.
.. ..
... . e.
.. .
... .....
....,. ." . . .. .
. ..
fill
.
ø
""
.
.. -.. .
. . .. ... .
. . . i...
. . .
.. . ..
1#
. ..
i.
.
-iO
.15 ø
-J 10,!
.20
.Uil
o
iO
20
yilhatcv
The two dimensional plot of the scores on the first two components suggests that
the two socioeconomic levels cannot be distinguished from one another nor can
the two genders be distinguished. Observations #111 and #104 are a bit removed
from the rest and might be labeled outliers.
Large sample 95% confidence interval for Â.i:
(l-1.96-21130
((l+1.96.21130
68.752 , 68.752
)=(55.31,90.83)
194
8.27 (a)-(d) Principal component analysis of the correlation matrix R.
Correlations: BL, EM, SF, BS
BL EM SF
EM 0.914
SF 0.984 0.942
BS 0 . 988 0 . 875 0 . 975
Cell Contents: Pearson correlation
Principal Component Analysis: BL, EM, SF, BS
Eigenanalysis of the Correlation Matrix
Eigenvalue 3.8395
Proportion 0.960
0.960
Cumulative
o . 1403
Variable
PC2
BL
EM
SF
BS
PC1
0.506
0.485
0.508
0.500
0.035
0.995
0.0126
O. 003
0.998
PC3
0.0076
0.002
1.000
PC4
-0.261 -0.565 0.597
0.819 -0.194 -0.237
-0.020 0.800 0.318
-0.510 -0.053 -0.698
The proportion of variance explained and the scree plot below suggest that one
principal component effectively summarzes the paper properties data. All the
variables load about equally on this component so it might be labeled an index of
paper strength.
Component ftJlmbe
195
The plot below of the scores on the first two sample principal components
does not indicate any obvious outliers.
Sætterplot ofylhat vs y2hat
. .
. .~. . e. .. .
o.:..
..
~
.
. o.
. .:
..
.- ...
'O e.
..
~3
-4
-050 "0.25 0.00
0.25 0;50
y2hiit
U)O
0.75
1.5
(a)-(d) Principal component analysis of the covariance matrix S.
Covariances: BL, EM, SF, BS
EM
SF
BS
0.513359
0.987585
0.434307
2.140046
0.987966
0.480272
BL
BL
EM
SF
BS
8.302871
1. 88£636
4.147318
i.972056
Principal Component Analysis: BL, EM, SF, BS
Eigenanalysis of the Covariance Matrix
Eigenvalue
proportion
cumulative
Variable
BL
EM
SF
BS
11.295
0.988
0.988
0.104
0.009
0.997
PC1
PC2
0.856
0.198
0.431
0.204
0.032
0.003
0.999
PC3
0.006
0.001
1. 000
PC4
-0.332 0.155
0.786 -0.497 -0.3Hl
0.259
0.733
0.458
-0.201 0.325 -0.901
-0.364
The proportion of variance explained and the scree plot that follows suggest that
one principal component effectively summarzes the paper properties data. The
loadings of the variables on the first component are all positive, but there are
some differences in magnitudes. However, the cOl'elations of the variables with
196
the first component are .998, .928, .990 and .989 for BL, EM, SF and BS
respectively. Again, this component might be labeled an index of paper strength.
Component NurilJ
The plot below of the scores on the first two sample principal components
does not indicate any obvious outliers.
'Stàtb~r,plot of ylhatcov vs y2håtc .
27.5
..
.
..'
..- a.
.
..
. .... .
,
..
. ..
.. .
".
. a...
17;5
.
15.0 .
0.0
0.4 0.8
y2hatcov
/
1.2
1.6
197
8.28 (a) See scatter plots below. Observations 25, 34, 69 and 72 are outliers.
Scttei¡løt øf'family YS Distad
160
.~~
.
. .
.." ..
. .,.
...
"1
i'. ..
.
21)
:. '
tt 1
I)
..
. .
il ?i
114'1:
200
100
0
Dist
300
40
50
5cttl'1CJtCJfPistRlI"SiCatte
500
.
#" r.'1
"1
i
..
300
.... l=..
¡
¡OO
100
0
.
I
.
.
" ". r.. . .. .. . .
0
20
li 3~.
.
40
Catt
60
8Ø
100
(b) Principal component analysis of R follows. Removing the outliers has some but
relatively little effect on the analysis. Five components explain about 90% of
given the
the total variabilty in the data set and seems a reasonable number
scree plot.
198
.3 Coirpone
45 Numbe
6
Prlnclp81 Compon8nt An8lysls: AdjF8m, AdjDlstRd, AdjCotton, AdjMalz AdjSorU..Outle 25.34,68,72
remove)
Eigenalysis of the correlation Matrix
proportion
culative
0.121 0.088
0.745 0.833
0.160
0.625
0.465
0.465
0.3661 0.2400
0.041 0.027
0.941 0.968
0.6043
0.067
0.900
1.0845 0.7918
1. 4381
Eigenvalue 4.1851
O. 1718
0.019
0.987
Eigenvalue 0.1182
proprtion 0.013
C\lative 1.000
variable
AdjFam
AdjOistRd
AdjCotton
AdjMaize
Adj Sorg
AdjMillet
AdjBull
AdjCattle
AdjGoats
pe
0.434
0.008
0.446
0.352
0.204
0.240
0.445
0.355
0.255
PC2
PC3
0.098
-0.569
0.132
0.388
-0.111
PC4
0.171
0.496
-0.027
0.240
-0.059
0.616
0.065
-0.497
-0.009
-0.353
0.604
0.415 -0.116
-0.068 -0.030 -0.146
-0.284 0.014 -0.373
0.049 -0.687 -0.351
PC5
0.011
-0.378
-0.219
-0 . 079
-0.645
0.527
-0 . 028
0.218
0.249
PC6
-0.040
0.187
-'0.200
-0.273
0.246
0.181
-0.134
0.759
-0.402
PC7
PC8
PC9
-0.797 -0.263 -0.249
0.021 -0.048 -0.065
0.361 0.329 -0 . 675
-0.024 0.363 0.574
-0.021 0.126 0.293
0.241 0.077 0.048
0.396 -0.751 0.190
-0.011 0.169 0.038
0.274 0.149
-0 . 131
Princlp81 Component An8lysls: F8mlly, Dlsd, Cotton, Møz Sor9, MIII8 BulL. .. ·
Eigenanlysis of the Correlation Matrix
proportion
4.1443
0.460
0.460
Eigenvalue
0.1114
0.012
Eigenvalue
cuulative
proportion
C\lative
variable
Family
OistRd
cotton
Maze
sorg
Millet
Bull
Cattle
Gots
1. 2364
1. 0581
0.9205
0.102
0.818
0.6058
0.067
0.885
0_5044
0.056
0.941
0.137
0.598
0.118
0.715
PC2
PC3
PC4
PC5
-0. 002
-0 .123
-0. 089
-0 . 127
0.100
-0.216
0.129
0.110
0.770
o . 043
0.2720 0.1470
0.030 0.016
0.971 0.988
1. 000
PCL
0.444
-0 .100
-0.033 -0.072 -0.831 0.502
0.411 -0.342 -0. 068 0.030
0.337 -0.554 0.170 0.164
0.311 0.452 -0.069 -0 .229
0.043 -0.385 -0.606
0.269
0.440 -0.029 0.122 0.197
0.247 0.458 0.278 0.486
0.309 0.379 -0.173 0.100
PC6
-0.194 -0.051
-0.134 O. 053
-0.361 -0.632
-0.182 O. 594
-0.392
0_407
PC7
-0.579
-0.045
0.509
-0.352
0.055
0.089
0.458
-0.012
-0.242
PC8
PC9
0.454 -0.461
0.041
0.082
-0.372 -0 . 504
0.499
-0.360
0.300
-0 .139
0.077
-0.097
0.357
0.621
-0.215 -0..225
-0.242 0.095
199
(c) All the variables (all crops, all livestock, family) except for distance to road
(DistRd) load about equally on the first component. This component might be
called a far size component. Milet and sorghum load positively and distance
to road and maize load negatively on the second component. Without additional
subject matter knowledge, this component is difficult to interpret. The third
component is essentially a distance to the road and goats component. This
component might represent subsistence farms. The fourth component appears
to be a contrast between distance to road and milet versus cattle and goats.
component appears to
Again, this component is diffcult to interpret. The fifth
contrast sorghum with milet.
8.29 (a) The 95% ellpse format chart using the first two principal components from the
covariance matrix S (for the first 30 cases of the car body"2
assembly
data) is
"2
shown below. The ellpse consists of all YI':h such that Yl + ~2 S X; (.05) = 5.99
Â, Â.
lie outside the ellpse.
where -l = .354, t = .186. Observations 3 and 11
Scalterplot of y2hat-y2bar vs ylhat-yl_
.1111
-T
-1.5
-1
o
1
2
ylhat-ylbar
(b) To construct the alternative control char based upon unexplained components of
the observations we note that di = .4137, S~2 = .0782 so
e .0782 = .0946 v = 2 (.4137)2 = 4.4. Conservatively, we set the chi-
2(.4137) , ;U782
squared degrees of freedom to 1) = 5 and the VCL becomes
ex; (.05) = .0946(11.07) = 1.05 or approximately 1.0. The alternative control char
is plotted on the next page and it appears as if multivariate observation 18 is out
of control. For observation 18, y; makes the largest contribution to d~18 and
200
getting the most weight in Y 4 are the thickness measurements Xl
and X2. Car body #18 could be examined at locations 1 and 2 to determine the
cause of the unusual deviations in thickness from the nominal levels.
the variables
t.
l. =ä 5(.05)
." 1.0
201
Chapter 9
9.1
.8' .63 .45
L' = (.9 .7 .5);
LL' = .63 .49 .35
.45 .35 .25
so 2 = LL' + 'l
9.2
å) For m-'
h1 = 9.Ìi = .81
h1 - III = 49
. 2 - lY21' .
hi = 9.ii = .25
The communalities are those parts of the variances of the
variables explained by the single factor.
b) corr(Zi'F,) = Cov(Zi'Fi)' i = J,2,3.' By (9-5) cov(Zi,F,) = .lil.
Thus Corr(Zp'F1) = 111 = .9; Corr(~,F1) = 9.21 =. .7; .corr(Z3,Fi) =
9.31 = .5.. The first .variab1e, Zl' has the largest correlation
with the factor and therefore will probably' carry the most weight
in naming the factor(. .6251
9.3
a)
L = r'':1 = 1i . ~93. =
. ' .507,
.711 "
.831 . Slightly different
(.87'61
from result in Exercise 9.1.
b) Proportion of total variance explained = ~ = , .i6 = .65
.451
9.4
i (.81 .63
.e = f - '¥ = LL = .45
.63 .49
.35
.35
.25 .
' L = h1 ~1 = Ii .5ti23 =,.7
.40Hi . . (.91
.'5
(.7~29J
202
Result is consistent with results in Exercise 9.1. It shoul.c
- - _. -
be since m = 1 common factor completely determin.e~ e = 2 - 'l .
9.5
Since V is diagonal and S - LL' -, has zeros on the diagonal,
(sum of squared entri es of S - LL i - V) S (sum of squared .entries of
.,. ,. A " I .
S - LL). By the, hint, S - LL =,P(2)A(2)P(3) \'1hich has sum of
squared er1tri es
A ,. Ai ,. A ¡Ai. A,. Ai Ai
tr(P (2)A(2)P (2) (p (2)A(2)P (2)) J = tr(P (2)A(2f (2)P (2))
,. "'i Ai A A,.i
= tr(A(2)A(2)P(2)Pc2)) = tr(A(2)A (2))
,.,. A
m+ m+. '11
= ~2 1 + ~2 2 + ... + l!
Therefore,
..1 - ,. It A
(sum of squared entri es of : S - LL - ,) s ~~+l + À~+2 + . .. + Àp
9.6
a) Follows directly from hint.
b) Using the hint, \'ie post multiply by (Ll' +'1) to get
I = ('1-1 -'1-1L(I +L,'¥-1L)-1L''1-1)(LL' +'1)
= '1-1 (lL' +'1) -'1-1L(I +L''l-lL)-1L'v-1(LL' +'i)
'-(use part (a))
= ,-1 (Ll + '1) - '¥-l L( I ~'(I + L' '1-1 L) -1) l'
- '(-1 LÙ + L ''1-1 L) -1 L'
= ,-1Ll +1 -'1-1LL' +'1-1L(I +L'V-1L)-lL'
_ '1-1i(i +LI'1~1L)-lL' = I
Note all these multiplication steps are reversibl~.
c) Multinlyin~ the result in (t) by L we get
203
(Ll' +V)-1L = 1f-\_iy-1LlI +L''i-lLrll''¥-~
..
(use part (a))
= '¥-lL-V-\(1 _ (I i-L1'1-1L)-1) = iy-'L(I +L''1-1L)-1
Result follows by taking the transpose of both sides of the
final equal ity..
9.7 Fran the equation ~ = Ll ..' '¥, m = 1, we have
;1 9.z~
rii ai ~
, ~12 a2~
121 +"'d
9.11121
= (111 +~1
'so aii = 9.11 +,wl' a2i = ~21 +"'2 and a12 = 111121
let p = a12/lan la22 . Then, for any choice Ipl/a22 s 121
:S /aZ2 t set .lll = alZ/121 an~ check. a12 = 9.119.21. We
2
on
't1
=
aii
lYll
=
(111
.l~1
~
(111
pZau
obtai ,Ii 112 al2 . a12 - -a11 -~V11
--0
and tPZ = a22 - 12.1 ~ 0'22 -0'22 = o. Since i21 \.¡as arbitrary
of
within a suitable interval, there are an infinite number
solutions to the factorization.
9.8 . 1: = Ll + 'i for m = 1 imp 1 i es
= .l; 1 + il1
.. ¥ = 9.,,9.21
1 ,: 9.21 + *2
( 1
.., = 111131 )
.i = 121131
1 = 9.31 + "'3
No\'1 i~ ~ = :; and .l119.21 = .4 , so 9.; 1 = (:;)(.4) and
9." =:! .717. Thus .l21 =':! .55&. finally, from .9 =
111R.31 ~ -w have .t31 =:! .9/.717 = :t 1.255 ·
204
Note all the loadings must be of the same sign because all the
have
covari ances åre positiv.e~ ' We
~o 717 J
.4
LL'.= .558 (.717 .558 1.255 J.
.3111
.9
1.255
= (:¡14
.7
.9 J
.7
1 .575
so' ~3 = 1 - 1 ~~7S-= ~.57~, which is inadmissible as a varianc~.
9.9
(a) Stoetzel's interpretation seems reasonable. The first factor
seems to contrast sweet witb strong 1 iquors.
,(b)
---"-"'-_._...
__ 0' -_... .-...._..__.
. Factor 2 .::.......... .. ,1 .0 -- - ._-. ._....:-...-
-_.._... -- ... ..' - _.... -. -_.. .,. ... - _...- ..
-'_._"--- ...._..__._-
_._--~. ". -. .... ....
,,
--_.... .... ....-;.
.. .. _..
- - -- ---~ - .. .....,. _....
._---- ._~...;...... : ...... ~..=:-.... ., -:._:..~ -. ',...
---_._...._. .-_.'
. ... _. .., . .... ..
:~:::~~~ .:. ~~:="~.""'" ~ -':': ," .. '..
-_........_-
. '.' ,.
.,-........-..-.------~__---.--.
- ....... _. . ._--- ------_...
.. O....n ..... __.________..._
0.. ,-..;- ... ....-.- '--7- ~
._......- ._.._._------.. . "': ..... ....... -..._-~.__. ~.._~-
. ,.,.
....... ... _. ..-. ._--: _.._--
o Rum
.. .. .... .. ..... ........_..-.. ... ....... -_.
.... . ._ ."M. ._...._~_ .. ..'_'''_._
, '
" Marc.
'0
..__._. ._.._..
- ." ..... ... . .'-.'-' -'. .- _..._-----
'.- .. ....-. -- -_._.- ..__....._.
._.- ':'--
.5
--_...... ... ..
-- .- - --'-- _........ - .. .-_..--.. -'" .....-: .
.. ...- . ~. .... - ..
... - -_. ~.. _...._... ':'.:
_. .... . . ....~.. .... .. _.. n .. .. ..
-:.~
-- ....;
.- ...__....
- .....
_.. . n..... ..__..
.....
.:... ._.:~:_'.~:"~.- .'- ..'-.:.:
. ........._.
, -_.....
"
.
. -..1 . N .-:..
. ....__._.
. . ...
.. .
, Ca 1 vados
L... ._._...._
-_. .,...- ,... , .' .." . ... .. --factorl --.......... -_. ...... .. . -
_. ._- - - ' ' .5 Li quors'- ....' .. .._-,.._--'r-. . --
.----_...-_... .........._- C
._. ....'---.7- :_h. .....~:-.--l
a ." ,o~.~~c..-.~5 '-:". .. . ~.. Kirsch" . ,1.0 ~--. .., ---,_.::;.::..:
k _...- _.. .., .... --.-.--.. ... -.._--_..~.- _.--- -- . 'A a oc ' ,. .. Wh.
Mirabelle
--'" .... -'--'-'-' --,--=~:_~::::__ ~ =~~ d:b.~~~!.~~.::i :. ... :- _.~ ~::~: ;...:~~: ~::::::. _: .~:_._-=::~?n~ ::~:=::£--=
-----:~--~_. . . .... -----_. _.__._--;---------"'------.--..
~-':'=-_-:;_.C:-:~-~.'~=:.:_~.~ .' :......, :.:'--:..' is e~ ._........-.';.. .... -----......---;-.;..-..--:.-
..
. . ._~_____:- _.. _. '-.5' .-.::__ _.. .__.. ." _ '" __.__'-___.0.
~ --~_......_-----_.. _.. ...._.-:~-_.....--:.--:~-~
...------_..._-- ." _.. ..... -----~.._._--_.... ".._._. ._"- --_.._.._-_....
.._..~-----_.
~___~.:..-~_.
..------. ...
-----:-~7-. ~-~.. .. ---~-~... - . ._..... -~_.._..--.._-_..
i:\.
,-
It doesn't appear as if rotation of the
factor axes is necessary.
(a) & (b)
The speci f; c variances and communal ities based on the unrotated
factors, are given in the f~llcwing table:
205
Speci fi c Vari ance
Vari abl e
Communa 1 ity
.5976
.4D24
Skull breadth
.7582
.2418
Femur 1 ength
.1221
.8779
Ti bi a 1 ength
.0000
1 .0000
Humerus 1 ength
.0095
.9905
Ulna length
. 0938
.9062
Skull
1 ength
(e) The proportion of variance explained by each factor is:
Factor 1 :
~ ;=1
r
9.;;
=
Factor 2 :
! r 12i
6 0 1
=
1=
(c)
4.0001
6
.4177
6
or
66.7"h
or
6.7%
,. A ,.
R-Lz l-'i=
z
0
.193
-.017
-.032
0
.000
.000
0
- . 000
.001
.000
.000
0
- . 001
-.018
.003
.000
.000
.000,
9.11
0
0
Substituting the factor loadings given in the table (Exerci'Se
9.10) into equation (9-45) gives.
Y (unrotated) = .01087
y' (rotated) = .04692
Al though the rotated 1 cadi ngs are to be preferred by the vari-max
("sim.pl.e struct-ur.ell) cri terion, interpretation -of the fa(:tor-s
206
seems clearer with the unrotated loadings.
9.12
The covariance matrix for the logarithms of turtle meaurements is:
S = 10-3 x 8.0191419 6.4167255 6.0052707
8.1596480 6.0052707
6.7727585 J
( 11.0720040
8.0191419 8.1596480
The maximum likelihood estimates of the factor loadings for an m=1 model are
Estimated factor
loadings
Variable
1. In(length)
2. In(width)
0.1021632
3. In(height)
0.0765267
Fi
0.075201 7
Therefore,
i = 0.0752017 ,
( 0.0765267
0.1021632 J
it' = 10-3 X 7.6828 5.6553 5.7549
7.8182 5.7549
5.8563 J
( 10.4373
7.6828 7.8182
(b) Since li~ = Îti for an m=l model, the communalities are
'" 2 . A 2 .... A 2 . ", _
hi = 0.0104373, h2 = 0.0056053, h3 = 0.0058563
(a) To fid specific variances .,i'S, we use the equation
.. A 2
.,i = 8¡¡ - hi
the maximum
Note that in this case, we should use 8n to get 8¡i, not S because
likelihood estimation method is used.
n - 1 23 (10.6107 7.685 7.8197 J
Sn = -8 = -2 S = 10-3 X 7.685 6.1494 5.7551
n 4 7.8197 5.7551 6.4906
Thus we get
.Ji = 0.0001 734, .J2 = 0.0004941, .J3 = 0.0006342
(c.) The proportion explained by the factor is
.. 2 .. 2 .. 2
hi
+ h-i + h3 = 0.0219489 = .9440
811 + 822 + 833 0.0232507
(.:) From (a)-(c), the residual matrix is:
8n - it' - \Î = 10-6 X 2.1673 0 00.112497.
0.1124971.4474 J
( 1.4474
0 2.1673
207
9.13
Equation (9-40) requires m ~ ¥2P+l - ¡g). Hêre we have m = 1,
P = 3 and the sti"ict inequality docs not hold.
9.14 Since
"'~ Ä_l A~ Al ""1,. ,. A
1f 1f '1 = I, /i ~/i ~ = /i and E f E = I ,
'"
'" 1.. ',.~ "!!S~-1 "'!."'..I ..l, "'At. ALIl ,.
L''l- L = /i"tl1f~ V..Et~~:: /i~fEA"l = /i"'/i"S = A.
9.15
(a)
variable
HRA
HRE
HRS
RRA
RRE
RRS
Q
REV
variance
communality
0.188966
0.133955
0.068971
0.100611
0.079682
0.096522
0.02678
0.039634
0.811034
0.866045
0.931029
0.899389
0.920318
0.903478
0.97322
0.960366
(b) Residual Matrix
o 0.021205 0.014563 -0.022111 -0.093691 -0.078402 -0.02145 -0.015523
0.021205 0 0.063146 -0.107308 -0.068312 -0.052289 -0.005616 0.036712
0.014563 0.063146 0 -0.065101 -0.009639 -0.070351 0.006454 0.013953
-0.022111 -0.107308 -0.065101 0 0.036263 0.058416 0.00696 -0.033857
-0.093691 -0.058312 -0.009639 0.036263 0 0.032646 0.008864 0.00066
-0.078402 -0.062289 -0.010351 0.068416 0.032645 0 0.002626 -Q. 004011
-0.02145 -0.005516 0.005464 0.00696 0.006854 0.002626 0 -0.02449
-0.015523 0.035712 0.013953 -0.033867 0.00066 -0.004011 -0.02449 0
The m=3 factor model appears appropriate.
(c) The first factor is related to market-value measures -(Q, REV). The second factor is
related to accounting historical measures on equity (HRE, RRE). The third factor is
historical
related to accounting historical measures on sales (HRS, RRS). Accounting
meaures on assets (HRA,RRA) are weakly related to all factors. Ther-efore, market-
value meaures provide evidence of profitabilty distinct from that provided by the
accounting measures. However, we cannot separate accounting historical measures of
profitabilty from accounting replacement measures.
208
PROBLEM 9.15
HRE .
RRE
0.8
R :¡
NO.6
o
t;
HRA
a:
"".. '
it 0.4
HRS
02
Q
REV
02
0.6
0.6
0.4
FACTOR 1
Roia FaCr Panem
0.9
HRS
0.8
RRS
0.7
'"
a:
0
t;
c
u.
0.6
0.5
R~RA
0.4
RE
0.3
Q
RRE
HRE
02
0.4
.
T
0.6
0.6
FACTOR 1
Rotatl FaClr Pattem
0.9
HRS
0.8
RS
0.7
I'
a: 0.6
gc
u.
HRA
0.5
RRA
0.4
REV
0.3
Q
.02
0.4
FACTOR 2
ROlated Factr Panem
0.6
0.8
RRE
HRE
209
9.16
'" '" 1A " 1
fJ. = Â- L''Y- (x.-x) and
:
~J ~
n A 1'" "1 n
L U l. j=l
!,J. -! = _. .
J=l
From (9-50)
,\" _fJ' = A - L"- \" ( ) 0
"'A A
1"'''
Since
1 ~1"'Al
fjfj = Â- L ''l- (Cj - &HìSj - &)''1- L6. - .
n "',. '" 1.. "1 "r ( -)(' -)1"'-1;"-1
'" 'f, f I. . = ti- L' '1- x . - x x . _ x UI I A
J.;l -J-J' . -J - -J - x LU,
J=l '
n,
"1SAlA"
= n ti",- 1"
L' '1V- U-1
Us; ng (9A-l),
n
"'1'"
"'l...1n. ""'1
r fJ.fJ~ = n ti- LI'1- ~-~(I +ti)ti-
j=l
Al" "'.., ""
= n ti- ti(I+6)Â- = n(I+ti- ),
a diagonal matrix. Consequently, the factor scores have sampl e mean
vector Q and zero sampl e covarfances.
9.17 Using the information in Example 9.12, we have
A I A i A i (.2220 -.0283J
(Lz 'l; Lz)- = which, apar from rounding error, is a
-.0283 .0137
diagonal matrix. Since the number in the (1,1) position, .2220, is appreciably
different from 0, and the observations have been standardized, equation (9-57)
suggests the regression and generalized least squares methods for computing
factor scores could give somewhat different results.
210
9.18. Factor analysis of Wisconsin fish data
(a) Principal component solution using Xl - X4
1 2 3 4
Ini tial Factor Method: Principal Components
Eigenvalue 2.1539 0.7876 0.6157 0.4429
Difference 1.3663 0.1719 0.1728
Proportion 0.5385 0.1969 0.1539 0.1107
Cumulati ve 0.5385 0.7354 0 .8893 1.0000
Factor Pattern (m = 1)
Factor Pattern (m = 2)
FACTORl FACTOR2
F ACTORl
BCRAPP IE
0.77273
0.73867
SBASS
LBASS
o . 64983
o . 76738
BLUEGILL
BLUEGILL
BCRAPPIE
SBASS
LBASS
0.77273 -0.40581
o . 73867 -0.36549
o . 64983 ~ .67309
0.76738 0.19047
(b) lvlaximum likelihood solution using Xl - X4
Ini tial Factor Method: Maximum Likelihood
Factor Pattern (m = 1)
FACTOR1
BLUEGILL
BCRAPPIE
SBASS
LBASS
0.70812
o . 63002
o . 48544
0.65312
Factor Pattern (m = 2)
F ACTOR1 F ACTOR2
BLUEGILL
0.98748 -0.02251
BCRAPPIE
o . 50404 0.25907
SBASS
0.28186 0.65863
LBASS
0.48073 0.41799
(c) Varimax rotation. Note that rotation is not possible with 1 factor.
Principal Components
Varimax Rotated Factor Pattern
BLUEGILL
BCRAPPIE
SBASS
LBASS
FACTOR1
0.85703
0.80526
0.08767
0.48072
FACTOR2
0.16518
0.17543
0.93147
0.62774
Maximum Likelihood
Varimax Rotated Factor Pattern
F ACTOR1 F ACTOR2
BLUEGILL
0.96841 0.19445
BCRAPPIE
o .4350i 0 . 36324
SBASS
0.13066 O. 70439
LBASS
0.37743 0.51319
For both solutions, Bluegil and Crappie load heavily on the first factor, while large-
mouth and smallmouth bass load heavily on the second factor.
211
(d) Factor analysis using Xl - X6
1 2 3 4
Initial Factor Method: Principal Components
Eigenvalue 2.3549 1.0719 0.9843 0.6644
Difference 1.2830 0.0876 0.3199 0.1640
Proport ion 0.3925 0.1786 0.1640 0.1107
Cumulative 0.3925 0.5711 0.7352 0.8459
Factor Pattern (m = 3)
F ACTORl F ACTOR2
o . 72944 -0.02285
BLUEGILL
0.72422 0.01989
BCRAPPIE
o . 60333, 0 .58051
SBASS
0.76170 0.07998
LBASS
WALLEYE
NPIKE
5
o . 5004
6
o .4242
0.0762
o . 0834
o . 0707
o .9293
1 .0000
F ACTOR3
-0.47611
-0.20739
o . 26232
-0 . 39334 0 . 83342
-0.03199
-0.01286
0.44657 -0.18156
o . 80285
Varimax Rotated Factor Pattern
F ACTORl F ACTOR2 F ACTOR3
o . 85090 -0.12720 -0. 13806
BLUEGILL
0.74189 0.11256 -0.06957
BCRAPPIE
0.51192 0.46222 0.54231
SBASS
LBASS
WALLEYE
NPIKE
0.71176 0.28458 0.00311
-0.24459 -0.21480 0.86227
0.05282 0.92348 -0.14613
Initial Factor Method: Maximum Likelihood
Factor Pattern
FACTORl
FACTOR2
F ACTOR3
o . 00000
BLUEGILL
1 . 00000
o . 00000
0.18979
BCRAPPIE
0.49190 0.23481
o . 96466
SBASS
o . 26350
o . 00000
0.29875
LBASS
o . 46530
o . 29435
O. 12927 -0.22770 -0.49746
WALLEYE
0.24062
NPIKE
o . 06520
o . 46665
Varima Rotated Factor Pattern
F ACTOR1 F ACTOR2 F ACTOR3
BLUEGILL
BCRAPPIE
SBASS
LBASS
WALLEYE
NPIKE
o . 99637 0 . 06257 0 .05767
0.46485 0.21097 0.26931
0.20017 0.97853 0.04905
0.42801 0.31567 0.33099
-0.20771 O. 13392 -0.50492
o . 02359 0 . 22600 0 .47779
The first principal component factor influences the Bluegil, Crappie and the Bas.
The Northern Pike alone loads heavily on the second factor, and the Walleye and
smallmouth bass on the third factor. The MLE solution is different.
212
9.19 (a), (b) and (c) l1aximum Likelihood (m = 3)
lJNROTATED FACTOR LOADINGS (PATTRN)
FOR l1AXIMU~' LIKELIHOOD CANONICAL FACTORS
Factor
'1
Growth
1
Profits
2
3
4
5
6
7
Newaccts
Creati ve
r~echani c
Abstract
Math
VP
0.772
0.570
'0..774
0.389
0..509
0.968,
0.632
26'Z
3.
Factor
Factor
2
3
0.295
0.347
0.433
0.527
0.721
0.355
0.000
0.334
0.921
o .426
,..-0.250
0.181
O~OOO
1 .520
1 .566
0.729
ROTATED FACTOR LOADINGS (PATTERN)
Growth
Prof; ts
Newaccts
Creat; ve
Mechani c
. Abstract
Math
1
2
3
4
5
6
7
VP
Factor
Factor
Factor
1
2
3
0.374
0.316
0.544
0.437
0.794
0.912
0.653 .
O~ 919
0.054
0.179
0.437
0.019
0.208
0.953
0.295
3~ 180
1 . 720
1 . 4"54
0.255
0.541
0.300
Communa 1; ti es
1 _ Growth
2 Profi ts
3 Newaccts
4 Creative
5 Mechani c
6 Abstract
7 Math
O. 1 84
0.9615
0.9648
O. 967
O. 464
Specifi~ Variances
1 . nOOO
.0385
.0352
.0876
.0000
.4481
.0000
0.9631
.'0369
'0.9124
1 .0000
0.S519
213
1.0
.926
1.0
.884
.843
1.0
R =
.542
.708
.746
.674
.465
.700
.637
.641
1.0
.591
.147
.386
.572
1.0
1.0
(Symetri c)
1.0
.923
1.0
Ll
,.
.912
.848
.572
.542
.700
1.0
+ 'l =
. '575
.56£
1.0
1.0
""A
.927
.944
.853
.413
.694
.679
.674
.455
. .696
.641
.591
.147
.386
1.0
1.0
(Symmetri c)
.925
.948
.826
.413
.646
.566
1.0
It is clear from an examination of the r.esidual matrix
,. A
R - (LL i +'1) that an m = 3 factor sol ution repr.esents the observed carrel ations quite well. However, it is dlfficul t to
. provide intei:-retations for the factors. If we consióer the,
. rotated loadings, we see that the last two factors ar.e dominated
by the- single variables IIcreativell and "abstra'Ct" r.espectively.
The first factor links the salespeople performance variables
wi th ma th a bi 1 i ty.
'(4) Using (9-39) \.iith n = 50, p. = 7, m = 3 we have
43 833 1 n (. 00007593l\ = 62 1 ;) x32(.,o1)= 11.3
, . .000018427).
214
so \'le reject HO:r = LL' + 'l for m = 3. Neither.of the m = 2,
m,= 3 factor
models appear to fit by the' x'- criteri-on. He
, AA "
note that the matrices R, LL' + V have small determinants and
rounding error could affect the calculation of the test statistic.
Again, t~e residual matrix above indicates a good fit for m = 3.
(e.) ~' = (1.522, -.852, .465, .957, 1.129, .673, .497)
Using the regression method for computing factor scores, we
, A_1
have; wi th f = LzR~ :
-
Principal components (m = 3) Maximum 1 ikel i hood (m = 3)
f' = (.686, .271,1.395) f' = (-.70Z, .679, -.751)
,computed
Factor scores using weighted least squares can 'only be
A_l
for the principal component sol utions si nce '1 cannot be com"
puted for the maximum likelihood solutions. ('1 has zeros on the
main diagonal for the maximum lik~lihood solutions). Using (9-50),
Principal components (m = 3)
l' = (..344, .2~3, 1.805)
9.20
Xs
~
-.59 -2.23
6.78 3u.78
11.36 3.13
31.98
L(symetric)
2.;; -;~7
S = 300.52
)
215
(a)
Princi pa 1
components (m = 2)
Factor 1
Factor 2
1 oadi ngs
1 oadi ngs
i
Xl el.lind)
X2 ~solar rad.)
Xs (N02)
X6 (03)
-.17
17.32
-.37
i
-.61
I
.42
.74
1.96
I
5.19
I
Cb) Maximum 1 ikol ihood estimates of the loadings are obtained from
L = ~z where Lz a~e the l.oadings obtåined from the sample
A
Z '.
correlation matrix R. (For t see problem 9.23). Note:
Maximum 1 ikol ihood estimates of the loadings for m = 2 may be
di ff1cul t to obtain for some computer packages without good
. ,
estimates of the communålities. One choice for initial esti-
mates of the comnunallties are the communalities from the m = 2
principal components solution.
(c) Haximum likelihood estimation (\.,ith m = 2) does a better job.
of accounti ng for the covari ances inS than the m = 2
principal component sol uti
on. On the other hand, the pri ncipal
component sol ution generally produces uniformly small~r ~stimates
of the specific variances. For thë unrotated m = L solution,
the first factor is dominated by Xl = solar ,radiation and Xs =
°3. The second factor seems t~ be a contrast ,between the paJr
Xl = wind; X2 = solar radi~tion and the pair X5 = NOZ and
~6 = OJ .
~gain the ff.rst factoi. is dominated by solar radiation and,. to
som~ extent, ozone. The second' factor" might ba interpretad as a
contrast bebieen wind and the pair of pollutants N02 and 03.
Recall solar radiation and ozone have the larg~st sample variances.
This will affect the estimated loadings obtained by the principal
component method.'
"
9.22 (a) Since, for maximum 1 ikel ihood estimat.es, ,.
L =i D~Lz
and
S = O'lRO\ the factor scores gener~ted by the equations for tj
in (9-58) will be identical. Similarly, the fact~r scores
generated by the we; ghted 1 eas~ squares formul as in (9-SQ) wi 11 be
identical.
l"e factor scores generated by the regression method wi th
..
maximum likelihood estimates (m =2; seeproblem9.23~) are giv€n
-l
below for the first 10 case~.
Case
2
3
,.
f1
-
0..316
0.252
0.129
4
0.'332
5
6
0.492
0.515
0.530
7
8
9
10
:¡ .070
0.384
-::0._,179
"
f2
-0.544
-0.546
-0.509
-0.790
-0.01.2
-0.370
-0.456
0.724
-0. tl23
p.io:
217
(b) Factor scores using principal component estimates (m = 2) and
(9-51) for the fit.st 10 cases are given below:
Case
1
2
3
,.
f1
'"
1 .203
-0.368
'f . 646
~ 1 . 029
1.447
0.717
0.856
4
0.795
o . 811
0.518
0.950
~O. 083
1 .1 68
0.410
~0.492
10
-0.937
-0.049
0.394
5
6
7
8
9
f2
0.259
0.072
(c) The sets of factor scores are quite different. Factor scores
depend heavily on the method used to estimate loadings and
specific variances as well as the method us~d to g~nerate them.
9.23
, Principal components (m = 2)
Factor 2
Rotated load; ngs
1 oadi ngs
loadin~s
Facto r 1
-.56
Factor 1
.
X2 (solar rad.)
.65
-.24
-.52
Xs ( NOZ)
,.48
.74
.77
~'.20
Xl
Xs
(wind)
(°3)
-.31
C!
-.05
Q£
2
Factor
I -.53 I
- .04
(Æ
.30
218
l1aximum likeli'hood (m = 2)
Factor 1
Factor 2
1 oadi ngs
loadings
Factor 1
Factor
-.38
.32
-.09
-X
' 2 (solar rad.)
.50
.27
C:
em
IX5 (N02)'
.25
-.04
.17
- .19
~6 (°3)
.65
-.03
C&
I -.43 J
~
1
(wi nd)
Rota teet loadi ngs
2
-.10
Examining the rotated loadings, we see that both solution methods yield
similar estimated loadings for the first .factr. It mi ght be called a
"ozone pollution factor'l. There are some differences for the s,econd factor-.
However, the second factor appears to compare one of the pollutants with
wind. It might be called a "pollutant transportU factor. \4e note that the
intèrpretations of the factors might differ depending upon the choice of
R or S
(see problems 9.20 and 9.21) for analysis. Al so the two sol ution
methods give somewhat different results indicating
the solution is not ve~
stabl e. Some of the observed carrel ations between the variables are vary
small implying that a m = 1 or m = 2 factor model for these 'four
variables will not be a completely satiSfactory description of the under~
'lying structure. We may need about as many factors as vari~blas. If this
is ,the ca~e, there is nothing to be gained by proposing a fa-ctor model.
219
9.24
-.192 .313 -.119
.026
-.192
1.0 - .065 .373
.685
R = .313
-.065 1.0 -.411
-.010
1.0
-.119
.026
.373 -.411 1.0
.180
.685 -.010 .180
1.0
The correlations are relatively small with the possible exception of .685, the
correlation between Percent Professional Degree and Median Home Value.
Consequently, a factor analysis with fewer than 4 or 5 factors may be
problematic. The scree plot, shown below, reinforces this conjecture., The scree
plot falls off almost linearly, there is no sharp elbow. However, we present a
factor analysis with m = 3 factors for both the principal components and
maximum likelihood solutions.
SçreêPlqlofPopulation, .., MedianHøme
2.0
1.5
lI
:i
ii
~ 1.0
lI
ai
iü
0.5
0.0
2
4
3
Factr
5
Numbe
Principal Component Factor Analysis (m = 3)
Unrotated Factor Loadings and Communalities
Factor1 Factor2 Factor3 Communality
0.9'62
-0.371 -0.541 -0.729
0.870
0.153
0.837 -0.381
PerCen tProDeg
0.756
0.209
-0.460 -0.708
PerCentEmp::16
0.807
-0.512
0.295
0.676
PerCen tGovEmp
0.830
0.064
-0.584
0.696
MedianHorne
Variable
Population
Variance
% Var
1.9919
0.398
1.3675
0.274
0.8642
0.173
4.2236
0.845
220
Rotated Factor Loadings and Communalities
Varimax Rotation
Variable
Population
Factor1 Factor2 Factor3
Coiiunal i ty
0.102 C-d.801ì -0.321
11.756
-0.059
-0.118 ~
~ 0.160 0.147
PerCen tProDeg
PerCentEmp~16
0.962
0.870
MedianHome
~ 0.009 -0.068
0.807
0.830
Variance
1.7382
0.348
1.4050
0.281
4.2236
0.845
PerCen tGovEmp
% Var
0.277 _.Q_85Q'/ -0.082
1.0803
0.216
Factor Score Coefficients
Factor1 Factor2 Factor3
0.138 -0.940
-0.019
Variable
Population
-0.028
-0.577
0.658
-0.099
0.522
0.169
0.052
0.544
PerCentProDeg
PerCentEmp~16
PerCen tGovEmp
MedianHome
0.109
-0.135
-0.278
-0.070
Score Plot of Population, .., MedianHome (PC:)
4
.
.
3
2
.~
.
~ 1
æ
. .
.
.
.
. .~. . . .
'a
.. ..
. . ..
.
. .
I 0
-1
.
.
.
.
.
...
. . .
. ,.
.
.
.
.
.'
.
.
i
.
-2
.
-3
-2
-1
o
Firs
i
Faêtr
2
4
3
Maximum Likelihood Factor Analysis (m = 3)
* NOTE * Heywood case
Unrotated Factor Loadings and Coiiunalities
Factor1 Factor2
-0.047 -0.999
0.146
0.989
PerCen tProDeg
-0.020 -0.313
PerCentEmp~16
0.103
0.362
PerCen tGovEmp
Variable
Population
MedianHome
Variance
% Var
Factor3 -Coiiunality
-0.0011
-0.000
0.941
0.701
-0.059
-0.395
-0.015
1.6043
0.321
1.1310
0.226
1.0419
0.208
1.000
1.000
0.984
0.298
0.49'6
3.7772
0.755
221
~ c¡ ~
0.145
1. 000
1. 000
-0.165
0.041
-0.061
0.984
0.298
0.496
1.1740
0.235
1. 0282
3 .7772
Factor1 Factor2
-0.177
0.137
-0.053
1. 017
PerCen tProDeg
1. 025
0.070
PerCentEmp::16
-0.001 -0.010
PerCen tGovEmp
-0.000 -0.001
MedianHome
Factor3
-1.046
-0.046
0.159
-0.002
-0.000
Rotated Factor Loadings and Communalities
Varimax Rotation
Factor1 Factor2
0.155
-0.036
-0.090
PerCentProDeg
0.047
PerCentEmp::16
-0.430
0.333
PerCen tGovEmp
Variable
population
~
MedianHome
-. 4
1.5750
0.315
Variance
% Var
Factor3 Conuunality
0.755
0.206
Factor Score Coefficients
Variable
population
Plot
Score
Population, .., MedianHoníé (MU£')
of
.
2
.
..
.
.
1
..
..
~
: 0
.,.
.
~ .
. . ...
..
-ø
i:
8
.
iX -1
.
. ..
.
. ..
.
.
.
.
.
.
.
..
.
. .
.
.
.
.
.
.
.
.
.
-2
.
.
-3
-2
-1
o
1 2
3
4
fil'Fllctr
A m = 3 factor solution explains from 75% to 85% of the variance depending on
the solution method. Using the rotated loadings, the first factor in both methods
has large loadings on Percent Professional Degree and Median Home Value. It is
difficult to label this factor but since income is probably somewhere in this mix, it
might be labeled an "affluence" or "white collar" factor. The second and third
factors from the two solutions are similar as well. The second factor is a bipolar
factor with large loadings (in absolute value) on Percent Employed over 16 and
Percent Government Employment. We call this factor an "employment" factor.
The third factor is clearly a "population" factor. Factor scores for the first two
factors from the two solutions methods are similaro
222
9.25
105,625
S
-'
94,734
87,242
94,Z80
101,761
76,186
81 ,204
91 ,809
90,343
H)4,329
(Symmetric)
A m = 1 factor model appears to represent these data qui te well .
Pri nci pa 1 Components
Factor 1.
loadings
Maximum Likelihood
Fa ctor 1
loadings
Shocki./ave
317.
320.
Vibration
293.
291.
Stati c test 1 .
287.
275.
Static test 2
307.
297.
90.1%
86.9%
Proportion
. Variance
Expl ained
Factor scores (m = 1) using the ~gression method for the first 'few
cases are:
Principal Components
Maximum L i kel ihood
-.009
-.033.
1 .530
1.524
.808
.719
- .804
- .802
The factor scores produced from the two sol ution methods ar.e v.ery
similar. The correlation between the two sets of sc~~es is .992.
T1i'Outli.ers, spet:imens 9 and 16, were i'Óentifi..d in 'Exæipl,e 4.15.
223
9.26
a)
Principal Compûn~nts
L m = i(
Factor 1
loadi nos
lm=2(
'1 .
Factor 2
'1 oadi naS
I,Factor
1
11 oadi nQS
1
'P .
,
Litter 1
21.9
309.0
27.9
-6.2
271 .2
Li tter 2
" 30.4
205.7
30.4
-4.9
182.2
Litter 3
31.5
344.3
31.5
18.5
1.7
Li tter 4,
32.9
310.0
32.9
-8.0
245.8
Percentage
Variance
Explained
76.4i
76.4%
l!
b)
"
9.4i
Maximum Likelihood
Litter 1
, Factor
10adinas
26.8
' '~
v.i
370.2
Litter 2
30.5
1 98.2
Litter 3
28.4
529.6
Litter 4
',30.4
471. 0
Percentage
Vari ance
' ,
68.8i
Explained,
The maximum likelihood. estimates of the factor loadings for ii = Z we're
not o,btained due to convergence difficul ti es in the computer program.
c) It is only necessary to r~tate the m = 2 solution.
224
Principal Components (m = 2)
Rata ted 1 oadi ngs
FactOr 1 'Factor 2
Litter 1
26.Z
11.4
li tter 2
27.5
13.8
Litter 3
14.7
33.4
Litter 4
31.4
12.8
Percentage
Var; ance
.53.5~
32.4%
Explained
9.27
,Principal Components, (m = 2)
Rotated loadin9s
'l .
Factor 1
Factor. 2
10a~ings
..
loadings
Litter 1
.86
.44
.06
.33
.91
Li tter 2
.91
.12
.15
.59
.71
Li tter 3
.85
-.36
.14
.87
.32
Litter 4
,.87
-°.21
.20
.78
.44
45.4%
40.6%
i
Factor 1 , J
Factor 2
..
Percentage
Variance
Expl ained'
76.5%
9.5~
225
Maximum Likelihood (m = 1)
,.
Factor 1
'1 .
loadings
1
Li tter 1
.81
.34'
Litter 2
.91
.17
litter 3
.78
.39
litter 4
.ßl
Percentage
Variance
68.81
Expl ai ned
'"
"-1
f = L R z
z. _
= .297
,
.34
,
226
9.28 The covariance matrix S (see below) is dominated by the marathon since the
marathon times are given in minutes. It is unlikely that a factor analysis wil
be useful; however, the principal component solution with m = 2 is given below.
Using the unrotated loadings, the first factor explains about 98% of the variance and
the largest factor loading is associated with the marathon. Using the rotated
loadings, the first factor explains about 87% of the varance and again the largest
loading is associated with the marathon. The second factor, with either unrotated or
rotated loadings, explains relatively little of the remaining variance and can be
ignored. The first factor might be labeled a "running endurance" factor but this
factor provides us with little insight into the nature of the running events. It is
better to factor analyze the correlation matrix R in this case.
Covariances: 100m(s), 200m(s), 400m(s), 800m, 1500m, 3000m, Marathon
100m(s)
200m(s)
400m(s)
800m
1500m
3000m
100m(s)
200m(s)
400m(s)
800m
o .02770
0.86309
2.19284
0.06617
0.20276
0.55435
10.38499
6.74546
0.18181
0.50918
1.42682
28.90373
0.00755
0.02141
0.06138
0.15532
0.34456
0.89130
Marathon
0.08389
0.23388
4.33418
Marathon
Marathon
270.27015
1. 21965
Principal Component Factor Analysis of S (m = 2)
Unrotated Factor Loadings and Communalities
Variable Factorl Factor2 Communality
0.124
-0.230
0.267
100m (s)
0.749
-0.582
0.640
200m( s)
6.725
-1.881
1.785
400m(s)
0.006
-0.027
0.075
800m
0.052
-0.073
0.217
1500m
0.453
-0.158
3000m
Mara thon
Variance
% Var
~
16.438-'
0.238
270.270
274.36
0.984
4.02
0.014
278.38
0.999
Rotated Factor Loadings and Communalities
Varimax Rotation
Variable Factor1 Factor2 Communality
0.124
-0.308
0.172
100m(s)
200m(s)
400m(s)
800m
150 Om
3000m
0.401
1. 030
0.061
0.178
o ~~
Marathon (i5.517'
Variance 242.38
0.869
% Var
-0.767
-2.380
-0.051
-0.143
-0.373
-5.431
0.749
6.725
0.006
0.052
0.453
270.270
36.00
0.129
278.38
0.999
1500m
3000m
0.07418
0.21616
3.53984
0.66476
10.70609
227
The correlation matrix ~ for the women's track records follows.
Correlations: 100m(s), 200m(s), 400m(s), 800m, 1500m, 3000m, Marathon
100m(s)
0.941
0.871
0.809
0.782
0.728
0.669
200m(s)
400m (s)
800m
150 Om
3000m
Mara thon
200m (s)
400m (s)
800m
1500m
3000m
0.909
0.820
0.801
0.732
0.680
0.806
0.720
0.674
0.677
0.905
0.867
0.854
0.973
0.791
0.799
The scree plot below suggests at most a m = 2 factor solution.
Scree Plot of iOOm($l, _.,Maràthon(èortlilàtion lbtnx)
II
,~
¡c 3
,ii
ØI
¡¡ 2
1
o
1
3 4 5
i
6
F.ctrNumbet
Principal Component Factor Analysis of R (m =2)
Unrotated Factor Loadings and Communalities
Communality
0.933
Variable Factor1
100m(s)
200m(s)
400m(s)
800m
150 Om
3000m
Marathon
Variance
% Var
0.910
0.923
0.887
0.951
0.938
0.906
0.856
5.8076
0.830
0.960
0.919
0.921
0.940
0.934
0.828
0.6287
0.090
6.4363
0.919
7
228
Rotated Factor Loadings and Communalities
Varimax Rotation
Variable
Communali ty
0.933
0.960
0.919
0.921
0.940
0.934
0.828
100m(s)
200m(s)
400m(s)
800m
1500m
3000m
Marathon
3.3530
0.479
Variance
% Var
6.4363
0.919
3.0833
0.440
Factor Score Coefficients
Variable Factor1 Factor2
-0.240 -0.480
100m(s)
-0.244 -0.488
200m(s)
-0.288 -0.525
400m(s)
0.259
0.035
800m
0.172
0.386
1500m
0.280
0.481
3000m
0.255
Marathon
0.445
Plot of 10011(5), ..,Marâthôll(PC:, rn=2,
Score
2
!1
.
..
j 0
:.
,;c
.
.
~-1
tI
. .
. .. .
. .. . .
.
..
.
.
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. #11
-3
-2
-1
o
1 Factr
2
3
Firs
Maximum Likelihood Factor Analysis of R (m = 2)
Unrotated Factor Loadings and Communalities
Variable
Communality
100m(s)
200m(s)
400m(s)
o . 90'6
0.976
0.848
0.856
0.984
0.972
800m
1500m
3000m
Marathon
Variance
% Var
o . 6'62
5 . 6 i 04
o .592?
0.801
0.085
6.2032
0.886
4
5
229
Rotated Factor Loadings and Communalities
Varimax Rotation
Variable Factorl
Marathon
0.455
0.449
0.395
.728
0.879
0.915
0.690
Variance
3.1B06
100m(s)
200m(s)
400m(s)
800m
150 Om
3000m
% Var
0.454
Communality
0.906
0.976
0.84B
0.B56
0.984
0.972
0.662
3.0225
0.432
Factor Score Coefficients
6.2032
0.886
Variable Factor1 Fat:tor2
100m(s)
-0.107
0.237
200m(s)
-0.481
1.019
400m(s)
-0.077
0.lS7
0.036
0.772
0.595
0.024
BOOm
1500m
3000m
Marathon
0.025
-0.317
-0.369
-0.003
ScorePJol of 100m(s), ..., Maràthon(JiL~, m=2)
3
. #'1
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2
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4
The results from the two solution methods are very similar. Using the unrotated
loadings, the first factor might be identified as a "running excellence" factor. All
the running events load highly on this factor. The second factor appears to
contrast the shorter running events (100m, 200m, 400m) with the longer events
(800m, 1500m, 3000m, marathon). This bipolar factor might be called a "running
speed-running endurance" factor. After rotation the overall excellence factor
disappears and the first factor appears to represent "running endurance"-since the
running events 800m through the marathon load highly on this factor. The second
factor might be classified as a "running speed" factor. Note, for both factors, the
remaining running events in each case have moderately large loadings on the
factor. The two factor solution accounts for 89%-92% (depending on solution
method) of the total variance. The plots of the factor scores indicate that
observations #46 (Samoa), #11 (Cook Islands) and #31 (North Korea) are outliers.
230
9.29 The covariance matrix S for the running events measured in meters/second is
given below. Since all the running event variables are now on a commensurate
measurement scale, it is likely a factor analysis of S wil produce nearly the same
results as a factor analysis of the correlation matrix R. The results for a m = 2 factor
analysis of S using the principal component method are shown below. A factor
analysis of R follows.
Covariances: 100m/s, 200m/s, 400m/s,800m/s, 1500m/s, 3000m/s, Marmls
3000m/s
1500ml s
800m/s
400ml s
200m/s
100ml s
Marml s
0.0905383
0.0956063
0.0966724
0.0650640
0.0822198
0.0921422
0.0810999
Marml s
0.1667141
lOOmIs
200m/s
400m/s
800m/s
1500m/s
3000m/s
0.1146714
0.1138699
0.0749249
0.0960189
0.1054364
0.0933103
0.1377889
0.0809409
0.0954430
0.1083164
0.1018807
0.0735228
0.0864542
0.0997547
0.0943056
Marml s
Principal Component Factor Analysis of S (m = 2)
unrotated Factor Loadings and communalities
communality
0.083
0.110
0.128
0.066
0.116
0.168
0.148
variable
lOOmIs
200m/s
400m/s
800m/s
1500m/s
3000m/s
Marml s
Variance 0.73215 0.08607
% Var 0.829 0.097
0.81822
0.926
Rotated Factor Loadings and Communalities
Varimax Rotation
communality
0.083
0.110
0.128
0.066
0.116
0.168
0.148
variable
10 Oml s
200m/s
400m/s
800m/s
1500m/s
3000m/s
Marrl s
Variance 0.45423 0.36399
% Var 0.514 0.412
Factor Score Coefficients
Factor2
variable Factor1
-0.171 -0.363
lOOmIs
-0.222 -0.471
200m/s
-0.306 -0.603
400m/s
800ml s
1500ml s
3000m/s
Marmls
0.104
0.287
0.542
,Q . sse
-0.025
0.08'5
o . 28'0
-0 . 33S
0.81822
0.926
0.1238405
0.1437148
0.1184S78
0.1765843
0.1465-604
231
Using the unrotated loadings, the first factor might be identified as a "running
same size loadings on
excellence" factor. All the running events have roughly the
factor. The second factor appears to contrast the shorter running events (100m,
200m, 400m) with the longer events (800m, 1500m, 3000m, marathon). This
bipolar factor might be called a "running speed-running endurance" factor. After
rotation the overall excellence factor disappears and the first factor appears to
represent "running endurance" since the running events 800m through the marathon
have higher loadings on this factor. The second factor might be classified as a
"running speed" factor. Note, for both factors, the remaining running events in
each case have moderate and roughly equal loadings on the factor. The two factor
this
solution accounts for 93% of the varance.
The correlation matrix R is shown below along with the scree plot. A two factor
solution seems waranted.
Correlations: 100m/s, 200m/s, 400m/s, 800m/s, 1500m/s, 3000m/s, Marm/s
lOOmIs
0.938
0.866
0.797
0.776
0.729
0.660
200m/s
400m/s
800m/s
1500ml s
3000m/s
Is
Marr
2 OOml s
400m/s
800m/s
1500ml s
3000ml s
0.906
0.816
0.806
0.741
0.675
0.804
0.731
0.694
0.672
0.906
0.875
0.852
0.972
0.824
0.854
Scree Plot of lOOmIs, .., Marm/s (Correlation Matñx)
0.8
fl.7
D,6
.:lI
0.5
¡,i: 0.
II
! D.3
0.2
0.1
0.0
1
2
3 4 5
component Number
6
7
232
Principal Component Factor Analysis of R (m = 2)
Unrotated Factor Loadings and Communalities
Communal i ty
Vari.able
0.932
0.960
0.911
0.914
0.941
0.947
0.875
lOOmIs
20 Oml s
40 Oml s
800m/s
1500m/s
3000m/s
Is
Marr
Vari.ance
% Var
5.8323
0.833
6.4799
0.926
0.6477
0.093
Rotated Factor Loadings and Communalities
Varima Rotation
Variable Factor1
10 Oml s
20 Oml s
400m/s
80 Oml s
l500m/s
3000m/s
Marml s
Variance
% Var
Communality
0.932
0.418
0.436
0.400
0.771
0.839
0.886
0.871
0.960
0.911
0.914
0.941
0.947
0.875
3.3675
0.481
6.4799
0.926
3.1125
0.445
Factor Score Coefficients
Variable Factor1 Factor2
-0.252 -0.489
lOOmIs
-0.243 -0.484
20 Oml s
-0.265 -0.499
400m/s
800m/s
15 OOml s
3000m/s
Marr/ s
0.248
0.358
0.455
0.484
0.025
0.142
0.249
0.293
ScoreP1..tíifl'ØOm/s, ..,Marm/s (PC,m=2)
3
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0
.
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2
233
Maximum Likelihood Factor Analysis of R (m = 2)
Unrotated Factor Loadings and communalities
Communa1i ty
Variable
0.896
0.983
0.836
0.850
0.971
0.984
0.737
lOOmIs
20 Oml s
400ml s
80 Oml s
1500m/s
3000m/s
MannI s
% Var
6.2560
0.894
0.5716
0.082
5.6844
0.812
Variance
Rotated Factor Loadings and communalities
Varimax Rotation
Communality
0.896
0.983
0.836
0.850
Variable Factor1
0.441
0.435
0.412
rO.
100ml s
200m/s
400m/s
800m/s
26
0.971
0.984
0.737
0.859
0.914
0.765
1500ml s
3 OOOml s
MannI s
3.2395
0.463
Variance
% Var
6.2560
0.894
3.0165
0.431
Factor Score Coefficients
Variable Factor1 Factor2
-0.167
-0.073
10 Oml s
-0.521 -1.122
2 OOml s
-0.106
-0.048
40 Oml s
0.039
0.379
0.949
0.041
80 Oml s
1500m/s
3000m/s
MannI s
-0.014
0.124
0.518
0.017
scOl'ePlotøf100mls, ..., Marmls (MLE, m=2)
.~ .ii
3
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-3
-4
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Firs Fac:or
o
1
2
234
The results from the two solution methods are very similar and very similar to the
principal component factor analysis of the covariance matrix S. Using the
unrotated loadings, the first factor might be identified as a "running excellence"
factor. All the running events load highly on this factor. The second factor appears
to contrast the shorter running events (100m, 200m, 400m) with the longer events
(800m, 1500m, 3000m, marathon). This bipolar factor might be called a "running
speed-running endurance" factor. After rotation the overall excellence factor
disappears and the first factor appears to represent "running endurance" since the
running events 800m through the marathon load highly on this factor. The second
factor might be classified as a "running speed" factor. Note, for both factors, the
remaining running events in each case have moderately large loadings on the
factor. The two factor solution accounts for 89%-93% (depending on solution
method) of the total variance. The plots of the factor scores indicate that
observations #46 (Samoa), #11 (Cook Islands) and #31 (North Korea) are outliers.
women's track records when time is
measured in meters per second are very much the same as the results for the m = 2
factor analysis of R presented in Exercise 9.28. If the correlation matrix R is factor
analyzed, it makes little difference whether running event time is measured in
The results of
the m = 2 factor analysis of
seconds (or minutes) as in Exercise 9.28 or in meters per second. It does make a
difference if the covariance matrix S is factor analyzed, since the measurement
scales in Exercise 9.28 are quite different from the meters/second scale.
235
9.30 The covariance matrix S (see below) is dominated by the marathon since the
marathon times are given in minutes. It is unlikely that a factor analysis wil
be useful; however, the principal component solution with m = 2 is given below.
Using the unrotated loadings, the first factor explains about 98% of the variance and
the largest factor loading is associated with the marathon. Using the rotated
loadings, the first factor explains about 83% of the varance and again the largest
loading is associated with the marathon. The second factor, with either unrotated or
rotated loadings, explains relatively little of the remaining variance and can be
ignored. The first factor might be labeled a "running endurance" factor but this
factor provides us with little insight into the nature of the running events. It is
better to factor analyze the correlation matrx R in this case.
Covariances: 100m, 200m, 400m, 800m, 1500m, 5000m, 10,OOOm, Marathon
5000m
1500m
800m
400m
200m
100m
100m
200m
400m
800m
1500m
500 Om
10 i OOOm
Marathon
10 i OOOm
Marathon
0.048973
0.111044
0.256022
0.008264
0.025720
0.124575
0.265613
1. 340139
0.300903
0.666818
0.022929
0.066193
0.317734
0.688936
3.541038
10 i OOOm
Mara thon
2.819569
14.342538
80.135356
2.069956
0.057938
0.168473
0.853486
1.849941
9.178857
0.002751
0.007131
0.034348
0.074257
0.378905
Principal Component Factor Analysis of S (m = 2)
Unrotated Factor Loadings and Communalities
Variable Factor1 Factor2 Communality
0.034
-0.107
0.152
100m
0.234
0.401 -0.270
200m
2.049
-0.979
1.044
400m
0.002
-0.015
0.043
800m
0.019
0.134 -0.033
1500m
0.537
-0.125
0.722
5000m
10.000m
Mara thon
Variance
% Var
~
~.
-0.223
0.179
2.643
80.130
84.507
0.983
1.141
0.013
85.649
0.996
Rotated Factor Loadings and Communalities
Varimax Rotation
Variable
100m
200m
400m
800m
1500m
5000m
10 i OOOm
Mara thon
Variance
% Var
Factor1 Factor2
Communality
-0.158
-0.406
-1.312
-0.031
-0.083
-0.399
-0.841
0.034
0.234
2.049
0.002
0.019
0.537
2.643
80.130
71.529 14.119
'0.832 0.lb4
85.649
0.996
0.097
0.262
0.573
0.033
0.110
0.615
1.392
~~J
0.023034
0.105833
0.229701
1.192564
0.578875
1. 262533
6.430489
236
The correlation matrix Rfor the men's track records follows.
Correlations: 100m, 200m, 400m, 800m, 1S00m, SOOOm, 10,OOOm, Marathon
200m
400m
800m
lS00m
SOOOm
10,000m
0.845
0.797
0.795
0.761
0.748
0.721
0.768
0.772
0.780
0.766
0.713
0.896
0.861
0.843
0.807
0.917
0.901
0.878
0.988
0.944
0.954
100m
0.915
0.804
0.712
0.766
0.740
0.715
0.676
200m
400m
800m
lS00m
SOOOm
10,000m
Marathon
The scree plot below suggests at most a m = 2 factor solution.
.. S
Fà.rNumbe
Principal Component Factor Analysis of R (m =2)
Unrotated Factor Loadings and Communalities
Variable Fa to
100m
200m
400m
800m
lS00m
SOOOm
10,000m
Mara thon
Variance
% Var
0.861
0.896
0.878
0.914
0.948
0.957
0.947
0.917
6.7033
0.838
Fa tor2
0.423
0.376
0.276
Communality
-0.123
-0.236
-0.267
-0.309.,
0.920
0.944
0.847
0.840
0.913
0.972
0.969
0.937
0.6384
0.080
7.3417
0.918
1
237
Rotated Factor Loadings and Communalities
Varimax Rotation
Communality
Variable
0.920
0.944
0.847
0.840
0.913
0.972
0.969
0.937
100m
200m
400m
800m
1500m
5000m
10.000m
Mara thon
Variance
% Var
7.3417
0.918
3.2249
0.403
4.1168
0.515
Factor Score Coefficients
Variable Factor1 Factor2
0.586
-0.335
100m
0.533
-0.283
200m
0.413
-0.183
400m
800m
150 Om
5000m
10.000m
Marathon
0.004
-0.053
-0.186
-0.224
-0.277
0.176
0.233
0.349
0.380
0.420
,Scareeløt~f'100ml .n, Marathoia:lPÇ,ltn=2)
. #-"
. it tj 10
.
..
..
... .0..
. .. . .
. .,
-.. ..
o
~
-3
o
-1
-2
3
1
Firs Fadr
Maximum Likelihood Factor Analysis of R (m = 2)
Unrotated Factor Loadings and Communalities
Variable Fac
100m
200m
400m
800m
1S00m
SOOOm
10.000m
Marathon
Variance
% Var
r
Communali ty
0.866
0.963
0.772
0.788
0.866
0.988
0.989
0.912
0.780
0.814
0.810
0.875
0.927
0.991
0.989
0.949
6.4134
0.802
0.7299
0.091
7.1432
0.893
238
Rotated Factor Loadings and Communalities
Varimax Rotation
Variable
Communal i ty
0.866
0.963
0.772
0.788
0.866
0.988
0.989
0.912
100m
200m
400m
800m
150 Om
5000m
10,000m
Marathon
7.1432
0.893
Variance 3.9446 3.1986
% Var 0.493 0.400
Factor Score Coefficients
Variable Factorl Factor2
0.256
-0.125
100m
0.994
-0.490
200m
'0.104
-0.044
400m
0.054
-0.011
800m
1500m
5000m
10,000m
Marathon
0.003
0.558
0.761
0.089
0.056
-0.209
-0.423
-0.051
,Seore9Jpt,of1.'ØOJDI"" Marathon (ML£,nì::l)
3
. iF II
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L
..,'ll,
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.
. ..
fi
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1.1
.
. .
..
-2
.
-3
-2
~1
o
1
2
3
Firs Factr
The results from the two solution methods are very similar. Using the unrotated
loadings, the first factor might be identified as a "running excellence" factor. ,All
the running events load highly on this factor. The second factor appears to
contrast the shorter running events with the longer events although the nature of the
contrast is a bit different for the two methods. For the principal component method,
the 100m, 200m and 400m events have positive loadings and the 800m, IS00m,
5000m, 1O,000m and marathon events have negative loadings. For the maximum
likelihood method, the 100m, 200m, 400m, 800m and 1 SOOm events are in one
group (positive loadings) and the 5000, 1O,OOOm and marathon are in the other
group (negative loadings). Nevertheless, this bipolar factor might be called a
239
"running speed-running endurance" factor. After rotation the overall excellence
factor disappears and the first factor appears to represent "running endurance" since
the running events 800m through the marathon load highly on this factor. Th~
second factor might be classified as a "running speed" factor. Note, for both
factors, the remaining running events in each case have moderately large loadings
on the factor. The two factor solution accounts for 89%-92% (depending on
solution method) of the total varance. The plots of the factor scores indicate that
observations #46 (Samoa) and #11 (Cook Islands) are outliers. The factor analysis
of the men's track records is very much the same as that for the women's track
records in Exercise 9.28.
9.31 The covariance matrix S for the running events measured in meters/second is
given below. Since all the running event variables are now on a commensurate
measurement scale, it is likely a factor analysis of S wil produce nearly the same
results as a factor analysis of the correlation matrix R. The results for a m = 2 factor
analysis of S using the principal component method are shown below. A factor
analysis of R follows.
Covariances: 100m/s, 200m/s, 400m/s, 800m/s, 1S00m/s, SOOOm/s, 10,OOOm/s,...
lOOmIs
2 OOml s
400m/s
800m/s
1500m/s
5000m/s
10, OOOml s
Mara thonr/ s
5000m/s
10. OOOml s
Mara thonrl s
lOOmIs
0.0434979
0.0482772
0.0434632
0.0314951
0.0425034
0.0469252
0.0448325
0.0431256
200m/s
400m/s
800m/s
1500m/s
0.0648452
0.0558678
0.0432334
0.0535265
0.0587731
0.0572512
0.0562945
0.0688217
0.0428221
0.0537207
0.0617664
0.0599354
0.0567342
0.0468840
0.0523058
0.05715£0
0.0553945
0.0541911
0.076'6388
5000ml s
10, OOOml s
Marathonr/s
0.0942894
0.0909952
0.0979276
0.0959398
0.0937357
0.0905819
Principal Component Factor Analysis of S (m = 2)
Unrotated Factor Loadings and Communalities
Variable
lOOmIs
2 OOml s
400m/s
800m/s
1500ml s
5000m/s
10. OOOml s
Marathonr/s
Variance
% Var
Fact
0.171
0.219
0.223
0.195 '
0.256
0.301
0.296
0.293
0.49405 0.04622
0.844 0.079
Communali ty
0.038
0.061
0.060
0.038
0.066
0.094
0.092
0.093
0.54027
0.923
0.0729140
0.0745719
0.0736518
240
Rotated Factor Loadings and Communalities
varimax Rotation
Factor1
Variable
100m/s
200m/s
400m/s
800m/ s
1S00m/ s
5 OOOm/ s
10,000m/s
Marathonr/s
Variance
% Var
Communality
0.038
0.061
0.060
0.038
0.066
0.094
0.092
0.093
0.080
0.105
0.116
0.151
. 12
0.273
0.275
0.283
0.32860 0.21168
0.562 0.362
o .54027
0.923
Factor Score Coefficients
Variable
100m/s
2 OOm/ s
400m/s
800m/s
lS00m/s
5000m/ s
10, OOOm/ s
Mara thonr/ s
Factor1 Factor2
-0.197 -0.377
-0.287 -0.561
-0.254 -0.526
-0.078
0.048
-0.022
0.159
0.379
0.415
0.489
0.184
0.240
0.334
Using the unrotated loadings, the first factor might be identified as a "running
excellence" factor. All the running events have roughly the same size loadings on
this factor. The second factor appears to contrast the shorter running events (100m,
200m, 400m, 800m) with the longer events (1500m, 5000m, 10,000, marathon).
This bipolar factor might be called a "running speed-running endurance" factor.
After rotation the overall excellence factor disappears and the first factor appears to
represent "running endurance" since the running events 1500m through the
marathon have higher loadings on this factor. The second factor might be classified
as a "running speed" factor. Note, the 800m run has about equal (in absolute value)
loadings on both factors and the remaining running events in each 'Case have
moderate and roughly equal loadings on the factor. The two factor solution
accounts for 92% of the variance.
The correlation matrix R is shown next along with the scree plot. A two factor
solution seems warranted.
241
Correlations: 100m/s, 200m/s, 400m/s, 800mls, 1S00m/s, SOOOm/s, 10,OOOm/s, on
20 Om/ s
400m/s
800m/s
1500m/ s
0.909
0.794
0.697
0.755
0.726
0.700
0.661
0.836
0.784
0.778
0.745
0.732
0.706
0.754
0.758
0.760
0.744
0.691
0.895
0.852
0.833
0.800
0.916
0.899
0.872
5000m/s
10 i OOOm/s
100m/s
200m/s
400m/s
800m/ s
1500m/ s
5000m/s
10,OOOm/s
Marathonm/s
0.986
0.935
10, OOOm/ s
Marathonm/s
0.947
ScteePlot of lOOmIs, .., Maråthonl11$ (C()rrêlatiol1f1atr~)
7
5
l 4
'ii-
i:
3. 3
¡¡
2
1
o
1
2
3
4 5
6
Factr Number
Principal Component Factor Analysis of R (m = 2)
Unrotated Factor Loadings and Communalities
Variable
Communali ty
0.913
0.939
0.841
0.834
0.914
0.968
0.965
0.929
10 Om/ s
20 Om/ s
40 Om/ s
80 Om/ s
1500m/ s
5000m/ s
10,000m/s
Marathonm/ s
Variance
% Var
6.6258
0.828
0.6765
0.085
7.3023
0.913
7
8
242
Rotated Factor Loadings and Communalities
Varima Rotation
Variable
Factorl
Communality
0.913
0.939
0.841
0.834
0.914
0.968
0.965
0.929
0.369
0.423
0.466
100m/ s
200m/s
400m/ s
0.74i
800m/s
l500m/s
5000m/s
0.805
0.882
0.895
0.896
Variance
4.1116
0.514
10,000m/s
Marathonm/s
% Var
3.1907
0.399
7.3023
0.913
Factor Score Coefficients
Factorl Factor2
Variable
-0.315
-0.270
-0.186
10 Om/ s
20 Om/ s
40 Om/ s
800m/s
1500m/s
5000m/s
10,000m/s
Marathonm/s
0.178
0.236
0.341
0.371
0.405
-0.566
-0.522
-0.418
-0.004
0.056
0.178
0.215
0.261
~PC,m=2J '
. ""
.#'1(.
..
..
.....-..
.
.
.
.-
-2
~1
0
Fiis Fact
..
.
... .
1
2
243
Maximum Likelihood Factor Analysis of R (m = 2)
Unrotated Factor Loadings and Communalities
Communality
0.859
0.957
0.758
0.777
0.865
0.985
0.986
0.899
Factor1
Variable
100m/s
200m/s
400m/s
800m/s
1500m/s
5000m/ s
10.000m/s
Mara thonm/ s
0:773
0.806
0.797
0.870
0.928
0.989
0.986
0.942
% Var
7.08ti5
0.886
0.7485
0.094
6.3380
0.792
Variance
Rotated Factor Loadings and Communalities
Varima Rotation
Variable
Communality
0.859
0.957
0.758
0.777
0.865
0.985
0.986
0.899
100m/ s
200m/s
400m/ s
800m/ s
1500m/s
5000m/ s
10,000m/s
Marathonm/s
% Var
7.0865
0.886
3.1540
0.394
3.9325
0.492
Variance
Factor Score Coefficients
Factor1 Factor2
0.268
-0.128
0.951
-0.457
0.111
-0.046
0.055
-0.008
Variable
lOOmIs
200m/s
400m/ s
800m/ s
1500m/ s
5000m/s
10, OOOm/ s
Marathonm/s
0.012
0.570
0.711
0.089
0.055
-0.219
-0.388
-0.047
Score P,lotof 100m's., ..., Marathonm/s (MLE, m=2)
3
.
2
.
.
..
.
l 1
,f
" D
J
..
.
.
.
..
..
.
,. . .
.
.
00
.. .
.
.
-.. .
.
..
.
. .
. #t(L
-2
.
.
I
.
.
.
*11
-3
-3
.
.
.
..
-2
-1
0
Firs factor
1
i
244
The results from the two solution methods are very similar and very similar to the
principal component factor analysis of the covariance matrix S. Using the unrotated
loadings, the first factor might be identified as a "running excellence" factor. All
the running events load highly on this factor. The second factor appears to contrast
the shorter running events with the longer events although there is some difference
in the groupings depending on the solution method. The 800m and 1500m runs are
in the longer group for the principal component method and in the shorter group for
the maximum likelihood method. Nevertheless, this bipolar factor might be called a
"running speed-running endurance" factor. After rotation the overall excellence
factor disappears and the first factor appears to represent "running endurance" since
the running events 800m through the marathon load highly on this factor. The
second factor might be classified as a "running speed" factor. Note, for both
factors, the remaining running events in each case have moderately large loadings
on the factor. The two factor solution accounts for 89%-91 % (depending on
solution method) of the total variance. The plots of the factor scores indicate that
observations #46 (Samoa) and #11 (Cook Islands) are outliers.
The results of the m = 2 factor analysis of men's track records when time is
measured in meters per second are very much the same as the results for the m = 2
factor analysis of R presented in Exercise 9.30. If the correlation matrix R is factor
analyzed, it makes little difference whether running event time is measured in
seconds (or minutes) as in Exercise 9.30 or in meters per second. It does make a
difference if the covariance matrix S is factor analyzed, since the measurement
scales in Exercise 9.30 are quite different from the meters/second scale.
245
9.32. Factor analysis of data on bulls
Factor analysis using sample covariance matrix S
Initial Factor Method: Principal Components
Difference
Proportion
20579.6126
15704.9378
o . 8082
a . 8082
Cumulati ve
4874.6748 5 . 4292
4869.2456 2. 1129
0.1914 o . 0002
o . 9998
o . 9996
5
6
7
3.3163 a .4688
O. 0741
o .~045
2 . 8475
O. 0001
a . 3948
a . 0695
a .0000
a .0000
o .0000
1 .0000
1 .0000
1 .0000
1. ()OO
4
3
2
1
Eigenvalue
Factor Pattern
FACTORl
X3 a . 48777
X4 0 . 75367
X5 0.37408
X6 0.48170
X7 0.11083
X8 0.66769
X9 a . 96506
F ACTOR2
F ACTOR3
a . 39033
a .38532
-0 . 00086
a . 64446
a . 33505
o . 65725
a . 62342
a . 36809
-0.38394 -0.49074
o . 29875
-0 . 26204
o . 33038
o . 00009
Varimax Rotated Factor Pattern
F ACTORl F ACTOR2
FACTOR3
o . 32637
X3 0.50195 0.42460
X4 0.25853 0.90600
X5 0.83816 0.45576
X6 0.44716 0.42166
X7 -0.60974 -0.06913
X8 0 . 40890 a .46689
X9 -0.13508 0.30219
YrHgt
FtFrBody
PrctFFB
Frame
BkFat
SaleHt
SaleWt
from a covarance matrx and then
rotates the scaled loadings.
YrHgt
a . 18354
FtFrBody
PrctFFB
0.31943
0.15478
BkFat
0.33514
o . 50894
o . 94363
Frame
SaleHt
SaleWt
Initial Factor Method: Maximum Likelihood
Factor Pattern
X3
X4
X5
X6
X7
X8
X9
F ACTORl
F ACTOR2
FACTOR3
o . 00000
1 . 00000
o . 62380
o . 00000
o . 39838
o . 00000
0.42819
0.85244 a . 52282
-0.01180 o . 94025
-0.36162 -0.34428
0.85951
o . 08393
o . 00598
a . 36843
0.03120
a . 39308
YrHgt
FtFrBody
PrctFFB
Frame
BkFat
o . 28992
SaleHt
a . 83599
'SaleWt
Varimax Rotated Factor Pattern
FACTOR1 FACTOR2
X3 0 .94438 a . 28442
X4 0.41219 0.50159
X5 0 . 23003 a . 94883
X6 0.88812 0.25026
X7 -0.25711 -0.51405
X~ 0 . 75340 0 . 26667
19 (). 25282 -0.05273
F ACTOR3
0.16509
0.55648
0.21635
YrHgt
FtF;rBody
O. 18382
Frame
BkFat
0.27102
o . 43720
o . 87'634
BAS scaws the loadings obtained
PrctFFB
SaleHt
SaleWt
The scaling is Î../ ç: .
!J V"ü
246
Factor analysis using sample correlation matrix R
1 2 3 4
Eigenvalue 4.12071.33710.74140.4214
Initial Factor Method: Principal Components
6
7
0.1465
o . 0471
Difference 2.7836 0.5957 0.3200 0.2356
Proportion 0.5887 0.1910 0.1059 0.0602
5
O. 1858
o . 0393
o . 0265
o . 0994
o . 0209
Cumulati ve 0.5887 0.7797 0.8856 0.9458
o .9723
a . 9933
o .0067
1 .0000
, Factor Pattern
F ACTOR1
X3 a .91334
X4 a . 83700
X5 0.72177
X6 0.88091
X7 -0 .37900
X8 0.91927
X9 0.54798
F ACTOR2
FACTOR3
-0 .04948
0.15014
-0.35794
-0 . 36484
a . 00894
o .38772
o .48930
-0.38949
-0.03335
0.11715 -0.15210
0.21811
o .69440
o .82646
YrHgt
FtFrBody
PrctFFB
Frame
BkFat
SaleHt
SaleWt
Varimax Rotated Factor Pattern
X3
X4
X5
X6
X7
X8
X9
F ACTOR1 FACTOR2
FACTOR3
0.94188 0.27085
-0.06532
o .44792 0 .78354
o . 24262
0.26505 0.87071
0.93812 0.21799
-0.25513
-0.01382
-0.23541 -0.37460
o .79502
Frame
BkFat
0.83365 0.41206
0.13094
0.74194
SaleWt
o . 34932 0 . 39692
YrHgt
FtFrBody
PrctFFB
SaleHt
Ini tial Factor Method: Maximum Likelihood
Factor Pattern
X3
X4
X5
X6
X7
X8
X9
FACTORl
o . 00000
F ACTOR2
F ACTOR3
1 . 00000
a . 00000
YrHgt
0.42819
o .62380
o . 85244
o . 52282
o . 94025
o . 39838
o . 00000
FtFrBody
Pn:tFFB
0.03120
Frame
BkFat
-0.01180
-0.36162 -0 . 34428
o . 08393
O. 00598
0.85951
o .36843
o . 39308
o . 28992
o . 83599
SaleHt
SaleWt
Varimax Rotated Factor Pattern
FACTORl FACTOR2 FACTOR3
X3 0.94438 0.28442 0.16509
X4 0.41219 0 . 50159 0.55648
X5 0.23003 0 .94883 0 . 21635
X6 0.88812 0.25026 0.18382
X7 -0.25711 -0.51405 0.27102
X8 0.75340 0.26667 0.43720
X9 O. 25282 -0.05273 0 . 87634
YrHgt
FtFrBody
PrctFFB
Frame
BkFat
SaleHt
SaleWt
The interpretation of factors from R is different of the interpr,etation of factiJl' from
S.
247
Factor scores for the first two factors using S
and varimax rotated PC estimates of factor loadings
51
(O50
N-
..
.. -
. ...
.
.
.~. .
....., ,.
. .". .. . .
o .................._.............,.......-..i...._..n.~-_.................................................................
.0
.. .
"; -
..
ci
I
I
-2
-1
I
I
i
1
2
Factor scores for the first two factors using R
and varimax rotated PC estimates of factor loadings
"1 -
51
(O-
N
...
o
.
. . ..:..:.. .
..
.
.
o
.
'1
.o
......_..........................a......................,. .....................................................__.........._......__....
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, ~ . ¡
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.
. 0
:.:
.
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ci-2
.
ii
i
I
i
i
.1
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1
2
248
9.33 The correlation matrix R and the scree plot follow. The correlations are relatively
modest. These correlations and the scree plot suggest m = 2 factors is probably too
few. An initial factor analysis with m = 2 confirms this conjecture. Consequently,
we give am =3 factor solution.
Correlations: indep, supp, benev, conform, leader
supp
benev
conform
leader
indep
-0.173
-0.561
-0.471
0.187
supp
benev conform
0.018
-0.327
-0.401
0.298
-0.492
-0.333
Scte Plot ofindep, ...;leader
(¡.s
0.0,
1
3
2
Factr Number
Principal Component Factor Analysis of R (m = 3)
,.
~
lU
Unrotated Factor Loadings and Communalities
Variable
indep
supp
benev
conform
leader
Variance 2.1966
% Var 0.439
Fàctor2
Fact~r3
l:9 . 5 8 Q)
-0.009
-0.422
cr5~m,
0.163
0.100
-0.256
1. 3682 0.7559
0.274 0.151
Communality
0.943
0.909
0.670
0.819
0.979
4.3207
0.864
249
Rotated Factor Loadings and Communalities
Varimax Rotation
Variable Fact~ Factor2 Factor3
indep
supp
benev
(=0.971 0.018 -0.003
leader
-0.155 ~ -0.111
Variance
1.6506
0.330
conform
% Var
Communali ty
0.943
0.909
0.670
0.819
0.979
0.136 -0.i12 CO:890J
~1~
(~0.41-a -0.081
O....U9" -0.379 (-OJ077
1. 3587
1. 3114
0.272
4.3207
0.864
0.262
Factor Score Coefficients
Variable Factorl Factor2 Factor3
indep
-0.752 -0.362 -0.147
supp
0.119
-0.129
0.690
benev
0.372
-0.127
-0.010
conform
0.073
-0.277
-0.545
leader
0.240
0.832
0.008
..
Score Plot of,indep, ...,Ieader (PC, m=3l
4
. ..
.
.
.
..
..
.. .
..
'\. . -....
"
-..3
.
. ...
. .:
..
I" .
..
.
..,
.. .
..
.
.
. . ..
. .
.
. ... .
..
. ..
,1
0
Fii'l'actr
-2
Maximum Likelihood Factor Analysis of R (m = 3)
* NOTE * Heywood
case
Unrotated Factor Loadings
Variable
Factor3 Communality
1.000
-0.790
1.000
-0.086
0.532
CD
indep
supp
benev
conform
leader
Variance
% Var
and Communalities
1. 5591
0.312
1. 5486
0.310
0.194
0.000
0.589
1.000
1. 0133
4.1211
0.824
0.203
250
Rotated Factor Loadings and Communalities
Varimax Rotation
Variable
indep
supp
benev
conform
~ Factor2
Factor3 Communality
IT
0.515 ~
-0.992 0.034
0.0 8 -O.~
~ Gb.454)
-0.980)
0.098
leader
-0.129 0.9681
cg.432)
.213
Variance
1. 5842
% Var
0.317
1.3199
0.264
1. 2170
0.243
1. 000
1.000
0.532
0.589
1. 000
4.1211
0.824
Factor Score Coefficients
Variable Factor1 Factor2 Factor3
indep
supp
benev
conform
leader
-0 .123
-0 .130
0.219
-0. 024
-1. 069
-0. 000
-0. 000
O. 000
O. 000
-0. 000
-1. 016
0.011
1. 081
0.000
-0.211
Using the unrotated loadings and including moderate loadings of magnitudes
.4-.5, the factors are all bipolar and appear to be difficult to interpret. Moreover,
the arangement of relatively large loadings on each factor is quite different for the
two solution methods. The rotated loadings are consistent with one another for the
two solution methods and, although all the factors ar bipolar, may be easier to
interpret. The first factor is a contrast between Independence and the pair
Benevolence and Conformity. Perhaps this factor could be classifed as a
"conforming-not conforming" factor. The second factor is essentially a
"leadership" factor although if moderate loadings are included, this factor is a
251
contrast between Leadership and Benevolence. Teenagers with above average
scores on Leadership tend to be above average on this factor, while those with
above average scores on Benevolence tend to be below average on this factor.
Perhaps we could label this factor a "lead-follow" factor. The third factor is
essentially a "support" factor although, again, if moderate loadings are used, this
factor is a contrast between Support and Conformity. To our minds however, the
latter is difficult to interpret. The factor scores for the first two factors are similar
for the two solutions methods. No outliers are immediately evident.
9.34 A factor analysis of the paper property variables with either S or R suggests a m = 1
factor solution is reasonable. All variables load highly on a single factor. The
covariance matrix S and correlation matrix R follow along with a scree plot using
R. For completeness, the results for a m = 2 factor solution using both solution
methods is also given. Plots of factor scores from the two factor model suggest that
observations 58, 59, 60 and 61 may be outliers.
Covariances: BL, EM, SF, BS
BL
BL 8.302871
EM SF
EM 1. 886636
0.513359
SF 4.147318
0.987585 2.140046
0.434307 0.987966
BS 1.972056
Correlations: BL, EM, SF, BS
BL EM SF
EM 0.914
SF 0.984 0.942
BS 0.988 0.875 0.975
BS
o .480272
252
Principal Component Factor Analysis of S (m = 1)
Unrotated Factor Loadings and Communalities
Variable Factorl Communality
BL
EM
SF
BS
Variance
% Var
2.878
0.664
1.449
0.684
8.285
0.441
2.101
0.468
11. 295
11. 295
0.988
0.988
Factor Score Coefficients
Variable Factor1
BL
EM
SF
BS
0.734
0.042
0.188
0.042
The first factor explains 99% of the total varance. All varables, given their
measurement scales, load highly on this factor. Note: There is no factor
rotation with one factor.
Principal Component Factor Analysis of R (m = 1)
Unrotated Factor Loadings and Communalities
Variable
Communality
0.984
0.905
0.991
0.960
BL
EM
SF
BS
Variance 3.8395
% Var 0.960
3.8395
0.960
Factor Score Coefficients
Variable Factor1
BL
EM
SF
BS
0.258
0.248
0.259
0.255
The first factor explains 96% of the variance. All varables load highly and about
equally on this factor. This factor might be called a "paper properties index."
253
Maximum Likelihood Factor Analysis ofR (m = 1)
* NOTE * Heywood case
Unrotated Factor Loadings and Communalities
Variable Fac
BL
EM
SF
BS
Variance
% Var
1.000
0.914
0.984
Communality
1.000
0.835
0.968
0.975
0.988,
3.7784
0.945
3.7784
0.945
Factor Score Coefficients
Variable Factor1
BL
EM
SF
BS
1.000
0.000
0.000
0.000
The results are similar to the results for the principal component method. The
first factor explains about 95% of the varance and all varables load highly and
about equally on this factor. Again, the factor might be called a "paper properties
index."
Principal Component Factor Analysis of R (m = 2)
Unrotated Factor Loadings and Communalities
Variable Factor1 Factor2 Communality
0.993
-0.098
0.9 2
BL
0.999
0.307
0.951
EM
0.991
-0.008
0.996
SF
0.996
-0.191
0.980
BS
Variance
% Var
3.8395
0.960
0.1403
0.035
3.9798
0.995
Rotated Factor Loadings and Communalities
Varimax Rotation
Variable Factor1 Factor2 Communality
BL
EM
SF
BS
Variance
% Var
0.817
0.522
0.761
0.868
0.571
0.852
0.642
0.493
0.993
0.999
0.991
0.996
2.271 7
1.7082
0.427
3.9798
0.995
0.568
Factor Score Coefficients
Variable Factor1 Factor2
-0.361
0.650
BL
1. 821
-1.235
EM
0.128
0.232
SF
-0.8£8
1 . 013 1
BS
254
. #"-0
..
. lr61
. #S9
.
..,.. .I .
".
. ..
.. .,
. e...
.
.#tõfJ
..
,
..
-1 0
Firs FaCtor
Using the unrotated loadings, the second factor explains very little of the variance
beyond that of the first factor and is not needed. Since the unrotated loadings
provide a clear interpretation of the first factor there is no need to consider the
rotated loadings. The potential outlers are evident in the plot of factor scores.
Maximum Likelihood Factor Analysis of R (m = 2)
* NOTE * Heywood
case
Unrotated Factor
Loadings
Variable Factor
BL
EM
SF
BS
Variance
% Var
0.988
0.875
0.975
1.000
3.6900
0.922
and Communalities
Factor2 Communality
0.103
0.986
0.485
0.185
0.000
1. 000
0.2800
0.070
3.9700
0.992
1. 000
0.984
Rotated Factor Loadings and Communalities
Varimax Rotation
Variable Factor1
BL
EM
SF
BS
Variance
% Var
0.809
0.523
0.757
0.870
2.2572
0.564
Factor2 Communality
0.986
0.576
0.853
0.641
0.492
1.7128
Ù .428
Factor Score Coefficients
Variable Factor1
BL
EM
SF
BS
-0. 000
-1. 016
-0.0,00
1.759
Factor2
-0.000
1. 795
-0.000
-1. 078
1. 000
0.984
1. 000
3.9700
0.992
25S
The results are similar to the results for the principal component method. Using the
unrotated loadings, the first factor explains 92% of the total variance and the second
factor explains very little of the remaining variance. Since the unrotated loadings
provide a clear interpretation of the first factor (paper properties index) there is no
need to consider the rotated loadings. The same potential outlers are evident in the
plot of factor scores.
9.35 A factor analysis of the pulp fiber characteristic varables with Sand R for m = 1
and m = 2 factors is summarized below. The covarance matrix S and correlation
matrix R follow along with a scree plot using R. Plots of factor scores from the two
factor model suggest that observations 60 and 61 and possibly observations 57, S8
and 59 may be outliers. A m = 1 factor solution using R appears to be the best
choice.
Covariances: AFL, LFF, FFF, ZST
AFL
LFF
FFF
ZST
AFL
LFF
FFF
ZST
-3.21404
0.00577
221.05161
-185.63707
0.34760
308.39989
-0.40633
0.00087
0.06227
3.35980
Correlations: AFL, LFF, FFF, ZST
AFL LFF FFF
LFF 0.906
FFF -0.733 -0.711
ZST 0.784 0.793 -0.785
256
Principal Component Factor Analysis of S (m = 1)
unrotated Factor Loadings and Communalities
variable
Communali ty
0.047
175.573
279.858
0.001
AFL
LFF
FFF
ZST
variance 455.48
% Var 0.860
455.48
0.860
Factor Score Coefficients
Variable Factor1
AFL
LFF
FFF
ZST
0.000
0.433
-0.645
0.000
The first factor explains 86% of the total varance and represents a contrast between
FF (with a negative loading) and the AFL, LFF and ZST group, all with positive
loadings. AFL (average fiber length), LFF (long fiber fraction) and ZST (zero span
tensile strength) may all have to do with paper strengt while FF (fine fiber
fraction) may have something to do with paper quality. Perhaps we could label this
factor a "strength--uality" factor.
257
Principal Component Factor Analysis of R (m = 1)
Unrotated Factor Loadings and çommunalities
Variable
Communality
0.877
0.870
0.770
0.841
AFL
LFF
FFF
ZST
Variance
% Var
3.3577
0.839
3.3577
0.839
Factor Score Coefficients
Variable Factor1
AFL
LFF
FFF
ZST
0.279
0.278
-0.261
0.273
The first factor explains 84% of the variance and the pattern of loadings is
consistent with that of the m = 1 factor analysis of the covarance matrix S. Again,
we might label this bi polar factor a "strength-quality" factor.
Maximum Likelihood Factor Analysis ofR (m = 1)
Unrotated Factor Loadings and Communalities
Variable
Communali ty
0.900
0.894
0.614
0.717
AFL
LFF
FFF
ZST
Variance' 3.1245
% Var 0.781
3.1245
0.781
Factor Score Coefficients
Variable Factor1
AFL
LFF
FFF
ZST
0.422
0.394
-0. 090
0.132
The first factor explains 78% of the variance and the pattern of loadings is
consistent with that of the m = 1 factor analysis of the covariance matrix R using
the principal component method. Again, we might label this bi polar factor a
"strength-quality" factor.
258
Because the different measurement scales make the factor loadings obtained from
the covariance matrix difficult to interpret, we continue with a factor analysis of the
correlation matrix R with m = 2.
Principal Component Factor Analysis of R (m = 2)
Unrotated Factor Loadings and communalities
Factor2 communality
0.942
0.256
variable
~
AFL
- . 50
0.953
0.949
0.863
0.3493
0.087
3.7070
0.927
0.288
LFF
FFF
ZST
3.3577
0.839
Variance
% Var
Rotated Factor Loadings and Communalities
Varimax Rotation
Communality
0.942
0.953
Variable
AFL
LFF
FFF
0.949
0.863
ZST
2.0176
0.504
Variance
% Var
3.7070
0.927
1. 6893
0.422
Factor Score Coefficients
Variable Factor1 Factor2
0.696
0.757
0.613
AFL
LFF
FFF
-0.082
ZST
0.359
0.429
1. 075
-0.501
. *' sf
.t1~'!
. .. ... ..
.
... ~: ~
. l. ..-.. .
.... ..
.#'"1
.*"'0
-4
-3
~2 -1
Factor
FirS
1
2
259
Maximum Likelihood Factor Analysis of R (m = 2)
Unrotated Factor Loadings and Communalities
Factor2
-0.205
-0.292
Variable
AFL
LFF
FFF
ZST
Variance
% Var
3.2351
0.809
Communality
(-0: 38ij
0.033
0.876
0.943
0.944
0.752
0.2796
0.070
3.5146
0.879
Rotated Factor Loadings and Communalities
Varimax Rotation
Variable
F5a~~,,~~'
Communal i ty
0.876
0.943
0.944
0.752
AFL
LFF
FFF
- ,.8
. 01
ZST
Variance
% Var
2.0124
0.503
3.5146
0.879
1. 5023
0.376
Factor Score Coefficients
Variable Factor1 Factor2
-0.101
0.336
AFL
LFF
FFF
ZST
0.922
0.534
0.049
-0.423
-1. 197
0.076
m=2)
"'''c.. .¡",
..
..
.. ..
.. ..- .. .
.-:-. .....
" ..,.:
. ~
..+$7
1l~"i.
-3
-2
.":;2
-1
o
Firs Factr
Examining the unrotated loadings for both solution methods, we see that the second
the remaining variane. Also, this factor has
factor explains little (about 7%-8%) of
moderate to very small loadings on all the variables with the possible exception of
260
variable FF. If retained, this factor might be called a "fine fiber" of "quality"
factor. Using the rotated loadings, the second factor looks much like the first factor
for both solution methods. That is, this factor appears to be a contrast between
variable FF and the group of variables AF, LFF and ZST. To summarize, there
seems to be no gain in understanding from adding a second factor to the modeL. A
one factor model appears be sufficient in this case. However, plots of the factor
scores for m = 2 suggest observations 60, 61 and, perhaps, observations 57, 58 and
59 may be outliers.
9.36 The correlation matrix R and the scree plot is shown below. After m = 2 there is no
shar elbow in the scree plot and the plot falls off almost linearly. Potential choices
for mare 2, 3, 4 and 5. We give the results for m = 4 but, to our minds, here is a
case where a factor model is not paricularly well defined.
Correlations: Family, DistRd, Cotton, Maze, Sorg, Milet, Bull, Cattle, Goats
Bull Cattle
Sorg Millet
Maze
Family DistRd Cotton
DistRd -0.084
0.028
0.724
Cot ton
0.730
0.679 -0.054
Maze
0.109
0.383
0.568 -0.071
Sorg
Millet 0.506 0.022 0.389 0.217 0.382
0.353
0.443
0.623
0.765
0.727 -0.088
Bull
Cattle 0.336 -0.063 0.175 0.197 0.404 0.081 0.520
0.560
0.357
0.305
0.424
0.136
0.031
0.399
0.484
Goats
ScreeP.lotofFamily" ..,Goats
i
2
3
4 5 6
Factor Number
7
8
9
261
Principal Component Factor Analysis of R (m = 4)
F~
unrotated Factor Loadings and Communalities
Variable
Factor3
0.903
Family
DistRd
Cotton
tt~~
-0. 0
-0.068
0.175
Maze
-0.070
-0.396
sorg
Millet
Bull
Cattle
0.125
0.286
-0.178
Goa ts
% Var
Rotated
1. 0581
4.1443
0.460
Variance
0.118
Family
DistRd
Cotton
Maze
Sorg
Millet
Bull
Cattle
Goa ts
Variance
% Var
F~
~
0.714'
-0.026
\I .951í
8J11
0.092
0.226
Factor2
-0. 7 .
0.320
-0.022
0.150
~ ~~~
0.006
-0.301
o 564
-0.863 i
-0.026
-0.210
0.148
0.180
0.535
0.879
0.629
EO.~6)
2.7840
0.309
1.8985
0.211
1.6476
0.183
(0'.
724J
~~
7.3593
0.818
0.9205
0.102
and Communalities
Factor Loadings
Varimax Rotation
Variable
~
Factor4 Communality
0.842
-0.118
0.974
0.851
. 28
0.907
0.158
0.706
0.798
-0.582
0.856
0.811
0:466
0.614
0.108
Factor4 communality
0.842
0.080
0.974
61.986ì
0.851
-0.076
0.907
0.047
0.706
0.112
0.798
-0.029
0.856
0.043
0.811
0.074
0.614
-0.145
7.3593
0.818
1. 0291
0.114
Factor Score Coefficients
Factor4
Variable Factor1 Factor2 Factor3
o .Oti3
-0.171
-0.013
0.197
Family
-0.963
0.030
0.042
0.014
DistRd
-0.090
-0.115 -0.024
0.344
Cotton
0.023
0.247
-0.165
0.494
Maze
O.HlO
-0.374
0.246
-0.199
Sorg
-0.001
-0.078 -0.260 -0.697
Millet
0.005
0.110
0.204
0.224
Bull
0.019
0.329
0.633
-0.063
Cattle
-0.164
-0.156
0.338
-0.114
Goats
SCOff: PlolõfFamily" mjGoats (PC, m=4,
. 'l3'!
.
. t:i.r
.
...
.
'..~":;..
,
. e. . ,
-1
o
..
· .ft :;7
-#.¡S'
1
Firs
'i .,t. -lll.~'
Factor
2
3
4
262
Maximum Likelihood Factor Analysis ofR (m = 4)
unrotated Factor Loadings and Communalities
Factor3 Factor4 Communality
0.837
-0.162 -0.374
0.009
-0.044 -0.003
0.782
-0.044
-0.307
0.990
0.025
0.649
-0.071 r=i0~5
Q112
21)
0.369
-0.361
-0.301
0.962
0.131
-0.096
0.869
-0.074
0.465
-0.109
-rr.151
64~ì
Variable F~~~~
FamilY
- .064
DistRd
Cotton
a1
~
0.980
0.211
Maze
Sorg
Millet
Bull
Cattle
0.746
0.290
0.249
Goa ts
2.9824
0.331
Variance
% Var
Rotated
1. 7047
0.189
~
Factor Loadings and
Varirnax Rotation
Variable
- .605
FamilY
0.017
DistRd
Cotton
-0.362
-0.034
Maze
Sorg
-0.558
fj~'
rff
Millet
Bull
Cattle
-0.324
-0.15tì
C=Ô.466
Goa ts
Variance
% Var
2.2098
0.246
1.7035
0.189
0.6610
0.073
5.9322
0.659
0.5841
0.065
communalities
i ty
Factor3 Factor4 Communal
0.837
-0.148
0.229
0.009
0.025
-0.081
0.782
-0.370
0.075
0.990
-0.016
0.166
0.649
-0.089
0.303
0.369
-0.028 -0.120
0.915
fO.4il
0.268
E§:m
~:$
0.962
0.869
0.4ti5
1. 2850
0.7340
0.082
5.9322
0.ti59
0.143
Factor Score Coefficients
Factor3 Factor4
Variable Factor1 Factor2
0.247
-0.078
-0.606
0.013
FamilY
-0.002
-0.009
-0.002
0.001
DistRd
-0.161 -0.162 -0.113
0.033
cotton
0.681
0.109
0.440
0.995
Maze
0.206
0.017
-0.404
-0.023
Sorg
0.052
-0.062
-0.185
0.003
Millet
-1. 426
0.103
0.215
-0.026
Bull
0.385
0.896
0.091
-0.141
Cattle
-0.023
-0.010
-0.093
-0.009
Goa ts
,
. "7("
.
'.
( " ....
....1.
'"
-;
.
.
.
.#t;7
. l.:~
.lf52.
-.
-1
o 1
Firs Factr
. ":Js
263
The two solution methods for m = 4 factors produce somewhat different results.
The patterns for unrotated loadings on the first two factors are similar but not
identicaL. The patterns of loadings for the two solution methods on the third and
fourth factors are quite different. Notice that DistRd does not load on any factor in
the maximum likelihood solution. The factor loading patterns are more alike for the
two solution methods using the rotated loadings, although factors 2 & 3 in the
principal component solution appear to be reversed in the maximum likelihood
solution. The rotated loadings on factor 4 for the two methods are quite different.
Again, DistRd does not load on any factor in the maximum likelihood solution, it
appears to define factor 4 in the principal component solution. (From R we see that
DistRd is not correlated with any of the other varables.) Variables Family, Cotton,
Maze, and Bullocks load highly on the first factor. The variables Family, Sorghum,
Milet and Goats load highly on the second factor (maximum likelihood solution) or
the third factor (principal component solution). Growing cotton and maze is labor
intensive and bullocks are helpfuL. The first factor might be called a
"family far-row crop" factor. Milet and sorghum are grasses and may provide
feed for goats. Consequently, the second (or third in the case of the principal
component solution) factor might be called a "family farm-grass crop" factor.
The third factor in the maximum likelihood solution (second factor in the principal
component solution) may have different interpretations depending on the solution
method but in both cases, Bullocks and Cattle load highly on this factor. Perhaps
this factor could be called a "livestock" factor. The rotated loadings are
considerably different on the fourth factor. This factor is clearly a "distance to the
road" factor in the principal component solution. The interpretation is not clear in
the maximum likelihood solution. The fact that the two solution methods produce
somewhat different results and explain quite different proportions of the total
variation (82% for principal components, 66% for maximum likelihood) reinforces
the notion that a linear factor model is not paricularly well defined for this
problem. Plots of factor scores for the first two factors indicated there are several
potential outlers. If these observations are removed, the results could change.
Chapter 10
lO.l.
t-l/2lo
..-1/2
11 ~i2..-It_
t22 ~l
tii _- (0
a
2()
( .:S)2J
which has eigenvalues ~2 = (,95)2 and p;2 = o.
Thus (1)
The normlized eigenvector. are ~1 · (:1 and ': · (~l.
'ui= el
.1t 1/2x(1)
11 -= (0
a IJ1(.1xO)(X1J
(l )= x(1)
2
2
Since !i t2~/2 = (1 OJ, VI = xf2).
Thus Ui = x~l) ,VI = xfl) and Pi = .95.
iO.Z
* *
a)
Pi = .55, P2 = .49
b)
Ui = .32XP) - .36X~1)
Vi = .36Xfl) - .iox~2)
U2 = .20XP) + .30X~1)
V2 =
.23XF)
+ .30X~1)
iO.S
a)
-1 -1 Q-1D 0-10 (.4S189
t11t12t22t21 =~ 11~~22~1 =
.45189-). .28919
.14633
.14633
.28919)
.17361
= ).2_.5461 )..0005
.17361- ).
= ( À-.5457) P.-.OO09)
equation is the same as that of
The characteristic
ii/2 12 2~ 21 ii/2 (see Example 10.1) and consequently the
eigenvalues are the same.
b) U2 = -.671ZP) +l.OSSzll)
( 2)
Vz = -.863Zi
( 2)
+ .106ZZ
Var(U2) = (_0.677)2+(1.OSS)2_2(.677i(I.05S)(.4) = 1.0
Var(V2) = 1.0
Corr(UZ' V 2) = (-.677) (- .863) (.5)+( -.863) (1.u5S) (.3)
+ (.706)(-.677)(.6)+(.70ti)(I.0S5)(.4)= ..03 = p~
10.7
a)
0(p(1
* =,!,p
lp
Pi
1
Ui =
f2(l + p)
VI =
10.8
c)
1
r2(1+p)
266
(X(1)+X(I)
1 2 )
i 2
(X (2) + X( 2) )
A*
Pi = .72
A 'i
VI = .20xi2)+.70x~2)
e = 45- = 4 radians
A*
d)
PI =
.57.
A
Ui = 1.03 cos 61 +
VI =
.49 cos
A*
10.9
a)
Pl= .39
.46 Sin
a1
Sin
a2
e2 + .78
P2 = .07
Û1 = i.~6zll)-1.03Zl1); U2 = .30zl1)+.79Zl1i
V2 = -.02zl2)+1.0IZl2)
VI = 1.10zl2)-.45Z~2)
,b) n = 140, p=2, q=2, n-l- ¥p+q+1l = 136.5
Value of
Null hypothesi s
Ho: t12 = E'12 = a
test sta ti stic
-136.5 R.(. 8444 i ( .9953)
Upper 51
-Degrees of point of f
Freedom distribution
4
9.48
= 23.74
H~l): pi *0, pî=O =-136.5R.(.9953)
1 3.84
.65
A A
Therefore, reject Ho but do not reject H~l). Reading ability (summarized by Ui) does correlate with arithmetic ability (summrize~ by Vi)
but the correlation (represented by PI = .39) is not parti~ularly
strong.
JO.10
a)
267
A* A*
Pi = .33, P2 = .17
b) Û1 = i.002Z~l)-.003Z~i)
V i = -.602Z12) -.977Z~2)
U1 .nonprimary
Zi( i) -- 1973
d(
omic. h'
es 1standardized)
Vi : i zl2) +Z~2) = a "pun; shment index"
Punishment appears to be correlated with nonprimary homicides
but not primary homi ci des.
10.11 Using the correlation matrix R and standardized variables, the canonical
correlations and canonical variables follow. The Z(l).s are the banks, the Z2).S
are the oil companies.
p; =.348, p; =.130
Ûi = -.539z:I)+ i.209z~l) + .079z~1)
Û2 = i.142z:1) -.410z~1) +.142z~1)
Vi = 1.1 60z:2) - .26 lz~2)
V2 = -.728z:2) + 1.345z~2)
Additional correlations:
vi. .1.
R ZCI) = (.266 .913 .498), R" Z(2) = (.982 .532)
RVi.Z(2) = (.342 .185), Rvi.z(l) = (.093 .318 .174)
Here H 0 : 1:12 c¡12) = 0 is rejected at the 5 % level and H cil) : Pi- *' 0, p; = 0 is not
rejected at the 5% leveL. The first canonical correlation, although relatively small,
is significant. The second canonical correlation is not significant.
Focusing attention on the first pair of canonical variables, Û i is dominated by
Citibank, Vi is dominated by Royal Dutch Shell. The canonical correlation (.348)
between Û i and ~ suggests there is not much co-movement between the rates of
return for the banks on one hand and the oil companies on the other. Moreover,
Û i is not highly correlated with any of the Z2).S (oil companies) and ~ is not
the Zl)'s (banks). The first canonical varables
highly correlated with any of
differentiate stocks in different industries with some, but not much, overlap.
A*
a)
ID.12-
Pl =
.69,
Reject Ho:
A*
P12 = 0 a t the 5: level but do
Ui =
not reject
*
H~I) = pi 4: 0,
b)
268
P2 :I .19
P2 = a a t the 5: level.
. 77zI i ) +. 27Z~ 1 )
A
VI = .oszI 2) +. 90Z~ 2) + .19Z~ 2)
c)
Sample Correla tions
Vari ables
xU)
Variables
-
Be tween Original Variables and Canonical
A
..
Ui
Vi
A
X(2)
. Variables
Ui
A
Vi
.1
i.
annua 1
frequency
.99 .68
1.
of restaurant dining
2.
annua 1 frequency
of a ttendi n9 movi es
age of head of
.29 .42
househol d
2.
.89 .61
3.
annual fami ly income
.68 .98
educa ti ona 1 1 eve 1
.35 .S1
of head of household
d)
-
U1 is a measure of family entertainment outside the home. VI
may be considered a measure of family MstatuS" which is domin-
ated by family income. Essentially, family entertainment outside the home is positively associated with family income.
a)
10.13
,.P1 =
.909,
"'
P2
=
. 636,
?3
=
.256,
Va 1 ue of
test statistic
Null hypothesi s
1.
Ho: L12=P12=0
2
Ho: Pi *0, P2=." = P4=0
3.
Ho: PI *0,
P2 *0,
P3=O,
~4 --
.094
Degrees
Conclusion
of freedom
at a level
309.98
20
Reject H
78.63
12
Reject H
16.81
6
0
00 not r.eject Ho.
P4=O
0
269
Z(1)
i
Z(1 )
2
A
.30J
i~~J' r:~ -::: -::: -:;:
.55
z(l
)
3
Z(1)
4
Z(1)
5
Z(2)
1
.46 .03)
G~J' G::: -:::
Z(2)
2
.98 -.18
Z(2)
3
Z(2)
4
A
b) U1 appears to measure qual i ty of wheat as a "contrast" between
"good" aspects (Zl1), zll) and z¡i)) and "bad" aspects
(Z3 (l! Z4 (1) ).
Vi ; s harder to interpret. It appears to measure the quality of
the flour as represented by z12), z~2) and z~2).
270
10.14
a) pi = 0.7520, pi = 0.5395. And the sample canonical variates are
U1 U2
Raw Canonical Coetticients tor the Accounting measures ot protitability
BRA -0.494697741 1.9655018549
RRE 0.2133051339 -0.794353012
HR 0.7228316516 -0.538822808
RRA 2.7749354333 -4.38346956
RR -1 .383659039 1.6471230054
RR -1.032933813 2.6190103052
V1 V2
Raw Canonical Coetticients tor the Market measures ot protitability
Q 1.3930601511 -2.500804367
REV -0.431692979 2.8298904995
U1 is most highly correlated with RRA and HRA and also HRS and RRS. Ví is highly
correlated with both of its components. The second pair does not correlate well with
their respective components..
b) Standardized Variance ot the Accounting measures ot protitability
Explained by
Their elm
The epposi te
Canonical Variables
Cumulative
Proportion
Proportion
1
0.6041
0.5041
2
O. 0906
o . 6946
Canonical Variables
Canonical
R-Squared
0.5655
0.2910
CWlulati ve
Proportion
Proportion
0.2851
0.0263
0.2851
0.3114
Standardized Variance ot the Market measures ot protitability
Explained by
Their eim The epposi te
Canonical Variables Canonical Variables
Cumulative Canonical Cumulative
Proportion Proportion R-Squared Proportion Proportion
1 0.8702 0.8702 0.5665 0.4921 0.4921
2 0.1298 1.0000 0.2910 0.0378 0.5299
Market measures can be well explained by its canonical variate 'C. However, accounting
meaures cannot be well explained. In fact, from the correlation between measures and
canonical variates, accounting measures on equity have weak correlation with Ûi.
Correlations Between the Accounting measures ot
protitabili ty and Their Canonical Variables
U1 U2
BRA 0.8110 0.2711
HR 0 . 4225 0.0968
BR 0.7184 0.5626
RRA 0 .S60S .. .OOag
271
RRE 0.6741 -0.09S9
RR 0.7761 0.3814
Correlations Betveen the Market measures ot
pro~itability and Their Canonical Variables
V1 V2
Q
0.9886 0.1508
REV 0.8736 0.4866
10.15
pi = 0.9129, p; = 0.0681. And the sample canonical variates are
U1 U2
V1 V2
Rav Canonical Codticients tor the dynamc measure
X1 0.0036016621 -0.006663216
12 -0.000696736 0.0077029513
Rav Canonical Coetticients tor the static measures
13 0.0013448038 0.008471036
14 0.0018933921 -0.007828962
Standardized Variance ot the dynamic measure
Explained by
Their Olm
Canonical Variables
Proportion
Cumulative Canonical
Proportion R-Squared
Proportion
0.8840 0.8334
0.7367
0.7367
1 .0000 0 .0046
o . 0006
o .7373
1 0.8840
2 0.1160
Standardized Variance ot
Their Olm
Cumative
2
Proportion
the static measures
Explained by
The Opposite
~anonical Variables
Canonical Variables
1
The Opposite
Canoni~al Variables
Cumulative
Proportion
Proportion
0.9601
0.0399
0.9601
1.0000
Canonical
R-Squared
0.8334
o . 0046
Cumulative
Propor'tion
Proportion
o . 8002
o . 0002
o . 8002
o . 8003
Static meaures can be well explained by its canonical variate ill' Also, dynamic
meaures can be well explained by its canonical variate Vi.
272
10.16 From the computer output below, the first two canonical correlations are ßi =
0.517345 and P'2 = 0.125508. The large sample tests
-en - 1 - ~(p + q - 1) ) In((1 - p*~)(1 - p*D) ~ X;q(.05)
or
1
-(46-1-2"(3+2-1) )In((1-(.517345)2)(1-(.125508)2) J - 13.50 ~ X~(.05) = 12.59
and
-en - 1 - ~(p + q - 1) ) In((1 - p*D) ~ XlP-lXq-il05)
or
1
-(46 - 1 - 2"(3 + 2 - 1) ) In¡(1 - (.125508)2) J = 0:ô67 ~ X~(.05) = 5.99
suggest that only the first pair of canonical variables are important. Even if the variables
means were given, we prefer to interpret the canonical variables obtained from S in terms
of coeffcients of standardized variables.
Ûi - .4357zPJ - .7047zl1) + i.0815z~i)
Vi = i.020z~2) - .1609z~2)
The two insulin responses dominate Ûi while Vi consists primarily of the relative weight
variable.
Canonical Correlation Analysis
Adjusted Appr~x
Canonical
Correlation
1
0.517345
2
O. 125508
Canonical Standard
Correlation Error
0.517145
o .007324
0.125158
o . "009843
Squared
Canonical
Correlation
0.267646
0.015752
Canonical Correlation Analysis
Raw Canonical Coefficients for the ~lucose and Insulin
GLUCOSE 0.0131006541 0.0247524811
INSULIN -0.014438254 -0.009317525
I
NSULRES 0 . 023399723 -0.0"08667216
Raw Canonical Coefficients for the Weight and Fasting
WEIGHT 8.0655750801 -0.375167814
FASTING -0.019159052 0 .12~675138
273
Standardized Canonical Coefficients for the Glucose
GLUCOSE
0.4357 0.8232
INSULIN
-0.7047 -0.4547
1.0815 -0.4006
I
NSULRES
Standardized Canonical
Coefficients
WEIGHT
FASTING
Correlations
Between the Glucose
SECONDA2
1 .0202
-0.0475
-0.1£09
1.0086
and Insulin
PRIMARY
1
Variables
PRIMARY2
o . 3397
o . 6838
INSULIN
-0 . 0502
-0 . 4565
-0 . 5729
0.7551
and Fasting
and Their Canonical
GLUCOSE
I NSULRES
Correlations
for the Weight
SECONDAl
and Insulin
Between the Weight and Fasting and Their Canonical
SECONDAl
SECONDA2
WEI GHT
o . 9875
O. 1576
FASTING
o . 0465
O. 9989
Variables
10.17 The computer output below suggests maybe two .canonical pairs of variables. the
canonical correlations are 0.521594, 0.375256, 0.242181 and 0.136568. Ûi ignores the
first smoking question and Û2 ignores the third. Vi depends heavily on the difference of
annoyance and tenseness.
Even the second pairs do not explain their own variances very well. R~(1)IU2 = .1249
and R~(1)iV2 = 0.0879
Canonical Correlation
Canonical
Adjusted
Canonical
Correlation
Correlation
2
3
0.521594
0.375256
0.242181
0.52t771
0.374364
0.241172
4
O. 136568
o . 135586
1
Analysis
Approx
Standard
Error
o . 007280
o .008592
0.009414
0.009814
Squared
Canonical
Correlation
o .272060
D .140817
D .058652
0.018651
Standardized Canonical Coefficients for the Smoking
SMOKING 1 SMOKING2 SMOKING3
SMOKING4
-0.0430 1.0898 1.1161
-1.0092
SMOK2
1.1622 0 .6988 -1.4170
-1.3753 0.2081 0.015£
o . 1732
SMOK3
SMOK4
o .8909 -1 .6506 Q. 8325
SMOKl
1.6899
-0.2630
Standar4ize Canonical CoeffÜ:ients f-or the Psych and Physical State
274
STATE
CONCEN
1
0.4733
ST A TE2
ST A TE3
-0.8141
-0.4510
o . 4946
o . 5909
ANNOY
-0 .7,806
SLEEP
TENSE
o .2567
-0 . ~052
o . 3800
ALERT
0.6919
-0.1451
-0 . 1840
0.6981
-0.4190
-1.5191
IRRIT AB
-0 . 0704
O. 6255
~O . 3343
TIRED
0.3127
o .5898
CONTENT
o .3364
o . 4869
o . 2276
o . 8334
STA TE4
-0 . 1 t5'04
-0.7193
0.624'6
o . 4376
-0.7253
0.87£0
U .1861
-0.6557
Canonical Structure
Correlations Between the Smoking and Their Canonical Variables
SMOKINGl SMOKING2 SMOKING3 ~MOKING4
SMOK3
0.4458 0.5278 0.6615 0.2917
0.7305 0.3822 0.1487 0.5461
0.2910 0.2664 0.4668 0.7915
SMOK4
o . 6403 -0.0620 0 . 5586 0 .5236
SMOKl
SMOK2
Correlations Between the Psychological and Physical
State and Their Canonical Variables
STATE
1 STATE2 STATE3
5TA TE4
CONCEN
0.7199 -0.3579 0.0125
ANNOY
o .3035 0 . 1365 0 . 3906
-0.3137
-0.4058
SLEEP
TENSE
0.5995 -0.3490 0 .3709
o .2586
0.7015 0.3305 0.0053
..0.18'61
ALERT
o .7290 -0. 1539 -0. 1459
-0.3681
IRRIT AB
0.4585 0.3342 0.1211
-0 .0805
0.0749
TIRED
CONTENT
o . 6905 -0.0267 0 . 2544
o . 5323 0 . 4350 0 . 3207
-0.5601
275
Canonical Redundancy Analysis
Raw Variance of the Smoking
Explained by
Their Own
The Opposite
Canonical Variables
Canonical Variabl€s
Cumulati ve
Proportion
Proportion
o . 3068
2
3
o . 3068
O. 1249
o . 2474
4
0.3210
1. 0000
1
0.4316
o . 6790
Canonical
R-Squared Proportion
0.2721
o . 0835
0.1408
0.0176
o . 0587
0.0145
0.0187
o . 0060
Cumulati ve
Proport ion
'0 .0835
0.1'011
0.1155
0.1215
Raw Variance of the Psychological and Physical State
Explained by
The ir Own
The Opposite
Canonical Variables
Cumulati ve
1
2
3
4
Proportion
Proportion
o . 3705
O. 0879
0.3705
o .4583
0.0617
0.1032
Canonical Variable s
Cumulative
Canonical
R-Squared Proportion
o . 2721
0.1008
0.1408
0.0124
Proportion
o . 1008
O. 1132
0.5201
o . 0587
o . 0036
0.11'68
o . 6233
0.0187
0.0019
O. 1187
10.18 The canonical correlation analysis expressed in terms of standardized variables
follows. The Z(1).s are the paper characteristic varables, the :t2).S are the pulp
fiber characteristic variables.
Canonical correlations:
p; = .917, p; = .817, p; = .265, p; = .092
First three canonical variate pairs:
Ûi =-1.505z:1) -.212z~l) +1.998z~1) +.676z~l)
Vi =-.159z:2) +.633z~2) +.325z~2) +.818z~2)
Û 2 = -3.496z:l) -1.543z~1) + 1.076z~l) + 3.768z~\)
V2 = .689z:2) + i.oo3z~2) + .OO5Z~2) -1.562z~2)
Û3 = -5.702z:1) +3.525z~1) -4.714z~1) +7.153z~1)
V3 = -.513z:2) +.077Z~2) -i.663z~2) -.779z~2)
276
Additional correlations:
Ru,.zo) =(.935 .887 .977 .952), Ry,.Z(2) =(.817 .906 -.650 .940)
RU1.Z(2l =(.749 .831 -.596 .862), Ryi.zo) =(.858 .814 .896 .873)
Here H 0 : L12 (P12) = 0 is rejected at the 5 % level and H ¿I) : pt '# 0, P; = 0 is
rejected at the 5% leveL. H¿2): Pi. '# O,p; '# O,p; = P; = 0 is not rejected at the
5% leveL. The first two canonical correlations are significantly different from O.
The last two canonical correlations are not significant.
The first canonical variable Û 1 explains 88% of the total standardized varance of
it's set, the Z(1),s. The first canonical variable Vi explains 70% of the total
standardized variance of it's set, the z,2).S. The first canonical varates are good
summar measures of their respective sets of varables. Moreover, the first
canonical variates, which might be labeled a "paper characteristic index" and "a
pulp fiber strength-quality index", are highly correlated. There is a strong
association between an index of pulp fiber characteristics and an index of the
characteristics of paper made from them.
The second canonical variable Û 2 appears to be a contrast between the first two
variables, breaking length and elastic modulus, and the last two variables, stress
burst strength. However, the only moderately large (in absolute
at failure and
value) correlation between the canonical variate and it's component varables is
the correlation (-.428) between Û 2 and Z~I) , elastic modulus. The remaining
correlations are small. This canonical variable might be a "paper stretch" measure.
The canonical variable "2 appears to be determined by all variables except Z~2) ,
fine fiber fraction. This canonical variable might be a "fiber length/strength"
measure. The second pair of canonical variates is also highly correlated.
10.19 The correlation matrix R and the canonical analysis for the standardized varables
follows. The z,1), s are the running speed events (100m, 400m, long jump), the
z,2).S are the arm strength events (discus, javelin, shot put).
1.0
.7926J
.4682
.4682
1.0
.4179
Rl1 = .5520 1.0 .4706
R22 = .4179
.4706.6386J
1.0
(.6386
1.0 .5520
.4752J
RI2 = R;i = .2100 .2116 .2539
.3998 .1821
.3102
(.3509
.4953
( .7926
1.0
277
Canonical correlations:
p; = .540, p; = .212, p; = .014
Canonical variables:
Ûi = .540z~1) -.120z~1) +.633z~l)
Û2 = i.277z~l) -.768z~1) -.773z~1)
Vi = -.057z~2) + .043z~2) + 1.024z~2)
V2 = -.422z~2) -1.0685z~2) + .859z~2)
Û 3 = .399z~1) + .940z~1) - .866z~1)
V3 = 1.590z~2) - .384z~2) -1.038z~2)
Additional correlations:
RUI'Z(1) = (.662 .160 .732), Rv"Z(2J = (.772 .498 .999)
Here H 0 : L12 (P12) = 0 is rejected at the 5 % level and H cill : p; "* 0, p; = p; = 0 is
rejected at the 5 % leveL. H ci2) : p; "* 0, p; "* 0, p; = 0 is not rejected at the 5 %
leveL. The first and second canonical correlations are significant. The third
canonical correlation is not significant.
We might identify Ûi as a "running speed" measure since the 100m run and the
long jump receive the greatest weight in this canonical variate and also are each
highly correlated with Ûi' We might call Vi a "strength" or "ar strength"
measure since the shot put has a large coeffcient in this canonical variate and the
discuss, javelin and shot put are each highly correlated with Vi'
278
Chapter 11
given in (11-19) is
11.1 (a) The linear discriminant function
A (_ - )'8-1X =AI
a X
Y = Xl - X2 pooled
where
S~moo = ( _: -: i
so the the linear discriminant function is
((: i - (: iH -: -: 1 z=¡-2 ~=-2Xi
(b)
2 2
A =l(A
m
- Yl +A)
Y2 =l(AI
- a Xl +AI)'
a X2 =-8
Assign x~ to '11 if
Yo = (2 7)xo ~ rñ = -8
and assign Xo to '12 otherwise.
Since (-2 O)xo = -4 is greater than rñ = -8, assign x~ to population '11-
279
11.2 (a) '11 = Riding-mower owners; 1T2 = Nonowners
Here are some summary statistics for the data in Example 11.1:
Z¡ - (I:,,::: 1 '
Z2 - 1:::: 1
5, - ( ~:::::: -I::::: 1 '
82 = ( 200.705 -2.589 1
-2;589 4.464
8 pooled - ,
8-1
pooled =
.00637 .24475
( .00378 AJ06371
-7.204 4.273
_(.276.675
-7.204 i
The linear classification 'function for the data in Example 11.1 using (11-19)
is
.006371
r J'
x = L .100 .785 :.
20.267 17.633 .00637
( (109.475 i -( 87.400 i) i ( ,00378
where
1 1
.24475
ri = 2"(Yl + Y2) = 2"(â'xi + â'X2) = 24.719
280
(b) Assign an observation x to '11 if
0.100x¡ +0.785xi ~ 24.72
Otherwise, assign x to '12
Here are the observations and their classifications:
Owners
Observation a'xo
Classification
1
nonowner
23.44
2
owner
24.738
26.436
3
owner
25.478
4
owner
30.2261
5
owner
29.082
6
owner
27.616
7
owner
28.864
8
owner
9
25.600
owner
28.628
10
owner
25.370
11
owner
26.800
12
owner
Nonowners
Observation a/xo
Classification
1
owner
25.886
2
nonowner
24.608
3
nonowner
22.982
4
nonowner
23.334
owner
25.216
5
6
21. 736
nonowner
21.500
7
nonowner
24.044
8
nonowner
9
nonowner
20.614
10
nonowner
21.058
11
nonowner
19.090
20.918
12
nonowner
From this, we can construct the confusion matrix:
Predicted
Membership
'11 '12
Actual
membership
:~ j
11 1
2 10
Total
12
12
(c) The apparent error rate is 1~~i2 = 0.125
(d) The assumptions are that the observations from 7íi and 7í2 are from multi-
variate normal distributions;with equal covariance matrices, Li = L2 = .L.
11.3 l,Ne ned.t-o 'Shuw that the regiuns Ri and R2 that minimize the ECM are defid
281
by the values x for which the following inequalities hold:
Ri : fi(x) ;: (C(lj2)) (P2)
h(x) - c(211) Pi
R2 : fiex) ~ (cC112)) (P2)
h(x) c(211) Pi
Substituting the expressions for P(211) and p(ij2) into (11-5) gives
J R2 J Ri
ECM = c(211)Pi r fi(~)dx + c(li2)p2 r h(x)dx
And since n = Ri U R2,
1 =Jr h(x)dx
r h(x)dx '
Ri J +R2
and thus,
ECM = c(211)Pi (1 - k.i fi(x)dx) + c(112)p2 ~i h(x)rix
Since both of the integrals above are over the same. region, we have
ECM = r (c(112)p2h(x)dx - c(21
JRi
l)pifi
(x)ldx + c(2~1)Pi
The minimum is obtained when Ri is chosen to be the regon where the term in
brackets is less than or equal to O. So choose Ri so that
c(211)pifi( x) ;: c(112)pd2(:i )'Ur
282
h(æ) )0
h(x) - (C(112)
c(2j1)) Pi
(P2)
11.4 (8) The minimum ECM rule is given by assigning an observation :i to '11 if
fi(æ) )0 (C(112)) .(pi) = (100) (~) = .5
h(x) - c(211) Pi 50.8
and assigning x to '12 if
fi(x) ~ (C(112))(!!) = .(100) (.2) = .5
f2(x) c(211) Pi 50.8
(b) Since fi(x) = .3 and f2(x) = .5,
fi(x) = 6;: 5
hex) . -'
and assign x to '11'
11.5
- ~ (~-~1)'t-1(~-~1) + ~ (~-~2)lt~1(:-~2) =
1 1 1 1 - 1 1+- 1 l +-1 1
- 2(~lr :-2:~r ~+~~r, :i-~'t :+2:2+ :-~2+ ~2
1 i - 1 l l- 1 1,,- 1 J
= - 2(-2(:1-:2) ~ ~+~l~ :1-:2~ :2
i -1 i ( ) i l- 1 ( )
= (:1-:2) t : -2 :'-:2 If :1+~2.
283
11.6 a) E(~'I~I7ii) -aa = .:!:l - m = ~l!:i - ~ ~l(~i + !!2J
= 1 ~I (~i - !!2) = ~ (!:i - !:2) i r i (~i -!!) ~ 0 s ; nee
r1 is positive definite.
b) E ( ~,1 ~ lir 2) - II = ~ 1!:2 - m = l ~l (~2 - ~1)
_ 1 ( ),..-1 (
- - '2 ~l - ~2'" ~l - ~2) ~ 0 .
11.7 (a.) Here are the densities:
--x
1.0
1.0
0.6
0.6
~
0.2
-0.2
0.2
R_1
1/4
R_2
-1.0 -0.5 0.0 0.5 1.0 1.5
x
-0.2
R_1 -1/3
R_2
-1.0 -0.5 0.0 0.5 1.0 1.5
x
284
(b) 'When Pi = P2 and c(112) = c(211), the classification regions are
!i(x)
Ri ..hex)
hex)~- 1
R2 : h (x) ~ 1
These regions are given by Ri : -1 ~ x ~ .25 and R2 : .25 ~ x ~ 1.5.
(c.) When Pi = .2, P2 = .8, and c(112) = c(211), the clasification regions are
R2 : fiex)
h (x) ~ .4
Ri : fi(x) ;: P2 = .4
hex) - Pi
These regions are given by Ri : -1 ~ x ~ -1/3 and R2 : -1/3 ~ x ~ 1.5.
11.8 (al Here are the densities:
i.
ci
-~
C'
ci
,.
ci
,.
cii
R_2
-1
-1/2 R_1 1/6
R_2
o
1
x
(b) When Pi = P2 and c(112) = c(2Il), the classification regions are
R1. .h(x)
h(x);:- 1
R2 : !i(x)
hex) .( 1
2
285
These regions are given by
Ri : -1/2 =: x ~ 1/6 and R2 = -1.5 ~ x ~ -1/2, 1/6 ~ x ~2.5
11.9
a'B ,ua
=
!'((~1-~)(~1-~)' + (~2-~)(~2-~),J~'
a/La
a1ta
-
whI,ere
+ ) Thus
~ = 2'
"_1~1
- u-_
~2.
= l(2.
~ ll_l - U_2) and 11_2 - ~ =
tt ~2 - ~l ) so
a'B ,ua =
a/La
! ~I (~1-~2)(~1-~2) I ~
ala
- I
,
28~
11.10 (a) Hotellng's two-sample T2-statistic is
T2 - (:Vi - X2)' f (~i + n~) Spooled J -i (Xi - X2)
- (-3 - 2j ((I~ + 112) l-::: -::: If L ~: I = 14.52
.. ..
Under Ho : l.i = 1J2,
T2", (ni + n2- 2)p F. . .
ni n2 - P -
+ 1 p,nl+n2-p-l
Since T2 = 14.52 ~ ~i~i~~-;~~ F2,2o(.1) = 5.44, we reject the null hypothesis
Ho : J.i = J.2 at the Q' = 0.1
level of significance.
(b) Fisher's linear discriminant function is
Yo = â'xo = -.49Xi - .53x2
.i
(c) Here, m, = -.25. Assign x~ to '1i if -A9xi - .53x2 + .25 ~ O. Otherwise
assign Xo to '12.
For x~ = (0 1), Yo = -.53(1) = -.53 and Yo - m = -.28 ~ o. Thus, assign
Xo to '12.
287
11.11 Assuming equal prior probabiliti€s Pi = P2 = l, and equal misclasification costs
c(2Il) = c(112) ~ $10:
c
9
10
11
12
!13
14
P(BlIA2) P(B2IAl) P(A2 and Bl)
.006
.023
.067
.159
.309
.500
.691
.500
.309
.159
.067
.023
.346
.250
.154
.079
.033
.011
peAl and B2)
.D03
.011
.033
.079
.154
.250
P( error)
Expected
cost
.349
.261
.188
.159
.188
.261
3.49
2.61
1.88
1.59
1.88
2.61
minimized for c = 12 and the minimum expected
Using (11- 5) ) the expected cost is
cost is $1.59.
1i.~2 Assuming equal prior probabiltiesPi = P2 = l, and misclassificationcosts c(2Il) =
$5 and c(112) = $10,
expected cost = $5P(A1 and B2) + $15P(A2 and B1).
c
9
10
11
12
13
14
P(BlIA2) P(B2/A1) P(A2 and Bl)
0.006
0.023
0.067
0.159
0.309
0.500
0.691
0.500
0.309
0.159
0.067
0.023
P(AI and B2)
P(error)
0.003
0.011
0.033
0.079
0.154
0.250
0.349
0.261
0.188
0.159
0.188
0.261
0.346
0.250
0.154
0.079
0.033
0.011
Expected
cost
1.78
1.42
1.27
1.59
2.48
3.81 .
Using (11- 5) , the expected cost is minimized for c = 10.90 and the minimum
expected cost is $1.27.
11.13 Assuming prior probabilties peAl) = 0.25 and P(A2) = 0.715, and misoassIÍca-
tion costs c(2Il) = $5 and c(lj2) = $10,
expecte cost = $5P~B2jAl)(.2'5) + $15P(BIIA2)(.75).
288
c
9
10
11
12
13
14
P(Bl/A2) P(B2/A1) P(A2 and Bl) P(A1 and B2) P(error)
0.006
0.023
0.067
0.159
0.309
0.500
0.691
0.500
0.309
0.159
0.067
0.023
0.173
0.125
0.077
0.040
0.017
0.006
0.005
0.017
0.050
0.119
0.231
0.375
Expected
cost
0.178
0.142
0.127
0.159
0.248
0.381
0.93
0.88
1.14
1.98
3.56
5.65
Using (11- 5) , the expected cost is minimized for c = 9.80 and the minimum
expected cost is $0.88.
11.14 Using (11-21),
79
A* - v'â'â
-.61 1
ai
-â -(.-and
m*i = -0.10
Since â~xo = -0.14 ~ rñi = -0.1, classify Xo as 7i2'
Using (11-22),
~1 and
m; = -0.12
-.77
aA 2* -- a~ --( 1.00 i
Since â;xo = -0.18 ~ m; = -0.12, classify Xo as '12.
These
'results are consistent with the classification obtained for the case of equal
prior probabilties in Example 11.3. These two clasification r.eults should be
identical to those of Example 11.3.
289
f1 (xl (C(lIZl P2J
11.15
fZ(~) l eT Pi defines the same region as
PzJ. For a multivariate
1n fi(~) -In f2(~) l rc(1IZ)
1n Le-pi
normal distribution
, - 1 - - , --1
1n f.(x) = _12 ln It.1 _.22 ln 2rr - 21(x-ii,.)'r'(x-ii.), i=1,2
so
1 n f1 (~) - , n f 2 (:) = - ~ (:-~1)' ~i 1 (:-~, )
1 ( ) ,+- , 1 ( I t i I)
+ 2' ~-!:2 +2 (~-~Z) - '2 1n M
_ 1 ( ,.,-1 '+ -1 , +-1
- - i : "'1 : - 2~rl'1 : + ~1 "'1 ~1
1 +- -1!:2+2
,- 1!:2)1- ('21n
U iW
i/ )
- ~ ,'2 t~ -+ ,2!:2'12~
1 1(+-1 +-1) (,+-1 ,+-1)
= - 2 ~ ~1 - '12 ~ + ~1+1 - ~2"'2 ~ - k
where
1
k='21n
(iii/)
1 i ~1
-1- ,
-1 ~2) .
iW + I'!!i+1
~2i2
290
11.16
Q = In ..
fi(x)
= - i lnl+il - i(:-~l) 'ti1 (~-~1)
(f ¡(X)J
1 l' -1
+ '2 In!t21 + 2'(~-~2) t, (~-~2)
1 , (..-1 t- 1 ) i +-, 1+- i
.. .. - 1..1
= - -2 x +i -+2 X + X t II - _X 1'2 ll_Z - k
where
When
k =12'(1n (I
t ii ) 1..-' 't-1 ' J
ii + ~, 1'1 ~i - ~2T2 ~2
.
ti = h = t,
Q= i~ -i1-:1+-1
1 (~i iT t-~1 1- !:21'
1+-1)
l' ~1
+~2 - 2'
~Z
It-'()'( 1+-1 '
= ~ l ~1 - LZ - 2' l:i - e2) l (~1 +!:Z)
11.17 Assuming equal prior probabilties and misclassification costs c~2Ii) = $W and
c(1/2) = $73.89. In the table below ,
Q-__
i ("(-i "(-i) (i "(-i i -i)
2 Xo LJi - ~2 Xo + J.i ~i - 112:E2 :to
1
-~l (IEil) _ ~( i~-l i -1 )
2 n 1~21 2 1-1 i 1-1 - 1-2~2 1-2
291
x
(10,
(12,
(14,
(16,
(18,
(20,
(22,
(24,
(26,
(28,
(30,
P('1ilx)
P
('12
I
Q
x)
15)'
1. 00000
0
17)'
19)'
21)'
23)'
25)'
27)'
0.99991
0.95254
0.36731
0.21947
0.69517
0.99678
291'
1. 00000
31)'
1. 00000
331'
1. 00000
35)'
1.00000
0.00009
0.04745
0.63269
0.78053
0.30483
0.00322
0.00000
0.00000
0.00000
0.00000
Clasification
18.54
9.36
3.00
-0.54
-1.27
'1i
0.87
1l2
5.74
13.46
24.01
37.38
53.56
'1i
'1i
'11
ii2
'12
'1i
'1i
'11
'1i
The quadratic discriminator was used to classify the observations in the above
table. An observation x is classified as '11 íf
Q ~ In r(C(112)) (P2)J = In (73.89) = 2.0
L c(211) Pi 10
Otherwise, classify x as '12.
For (a), (b), (c) and (d), see the following plot.
50
40
30
0
0
0
0
0
C\
x'
20
10
0
o
20
10
)L1
30
292
11.18
The vector: is an (unsealed) eigen'l.ector of ;-1B since
t-l t-l 1
B: = t c(~1-~2)(~1-~2)IC+- (~1-~2)
= c2t-l (~1-~2) (~1-~2) i t-1 (~1-~2)
= A t-1 (~1-~2) = A :
where
A = e2 (~1-~2) 't-l (!:1-~2) .
11.19 (a) The calculated values agree with those in Example 11.7.
(b) Fisher's linear discriminant function is
A AI 1 2
3 3
Yo = a Xo = --Xl + -X2
where
17 10 27
3 3 6
Yl = -; Y2 = -; rñ = - = 4.5
Assign x~ to '1i if -lxi + ~X2 - 4.5 ~ 0
Otherwise assign x~ to '12.
a"'i
Xo ..
-m
'1i
Observation
'12
Classification
Observation
'11
1
2
2.83
0.83
'1i
3
-0.17
'12
2
3
1
a-I
Xo -..
m
-1.50
0.50
-2.50
Classification
112
7(1
7í2
293
The results from this table verify the confusion matrix given in Example 11.7.
(c) This is the table of squared distances ÎJt( x) for the observations, where
D;(x) = (x - xd8~;oied(X - Xi)
'11
Obs. ,ÎJI(x)
i
ÎJ~ (x)
'12
Classification
Obs.
ÎJ~ (x)
ÎJH x )
Clasification
3
3
'1i
1
313
i3
7f2
2
i
J!
'1i
2
l3
i3
7fi
3
4
3
3
3
'12
3
19
3
4
1
3
21
3
3
7f2
The classification results are identical to those obtained in (b)
11.20 The result obtained from this matrix identity is identical to the result of Example
11.7.
11.23 (a) Here ar the normal probabiHty plot'S for each of the vaables Xi,X'2,Xa, X4,XS
294
-2
.1
o
2
295
-2
-1
0
2
....~a_~
300
~x
0
~
00/
0
ocP
250
200
0
.2
-1
0
2
0
.2
-1
0
2
0
80
60
II
x
40
20
,i.III.ID.ooO 0
0
.2
-1
0
1
2
Standard Normal Quantiles
Variables Xi, xa, and Xs appear to be nonnormaL. The transformations In\xi)
and In(xs + 1) appear to slightly improve normality.
(b) Using the original data, the linear discriminant function is:
y = â' x = 0.023xi - O.034x2 + O.2lx3 - 0.08X4 - 0.25xs
where
ri = -23.23
, In,(x3 + 1),
296
Thus, we allocate Xo to Í1i (NMS group) if
âxo - rñ = 0.023xi - 0.034x2 + 0.2lx3 - 0.08X4 - 0.25xs + 23.23 ;: 0
Otherwise, allocate Xo to '12 (MS group).
( c) Confusion matrix:
Predicted
Membership
'1i '12
Actual
membership
;~ j
66 3
7 22
Total
t ~~
APER= 6~~~9 = .102
This is the holdout confusion matrix:
Predicted
Membership
,'1i '12
Actual
membership
;~ j
64 5
8 21
Total
t ~:
Ê(AER) = 6~~~9 = .133
11.24 (a) Here are the scatterplots for the pairs of observations (xi, X2),tXi, X3), and
~Xl' X4):
297
0
0.1
+
++* +:
0.0
C\
+
Q.
++++
+it+
+
+
ce
0
)(
-0.2
-0.3
+lt
o 0
+
.0.1
+
0
bankrupt
nonbankrupt
0
0
0
0
-0.4
-0.6
-0.2
-0.4
0.6
0.4
0.2
0.0
+
5
+
C"
+
+
4
3
++0+;
++
+
)(
+
+
++
++
2
0+
0
0
-0.6
-0.4
+
000
oOi 8(3
~
0
1
0
+
+
-0.2
0.6
0.4
0.2
0.0
0.8
-a
+
)(
0
0.4
0.2
óJ
0
0
0
0.6
+
+
0
o Cò
+
0 0
0
0
-0.6 -0.4 -0.2
+
+
++
+
+
0
0
0
+
+
+
Ll
\
+
0.2
+
q.
+
0++
0.0
+
0.4
0.6
x1
The data in the above plot appear to form fairly ellptical shapes, so bivaate
norma1ìty -does not seem like an unreasonable asumption.
298
(b) '11 = bankrupt firms, '12 = nonbankrupt firms. For (Xi,X2):
Xi
-
8i
-
-0.0819 i '
( -0,0688
X2
-
0.02847 0.02092
( 0,0442 0.02847 J
82
-
0.0551
( 0.2354
i'
0.00837 0.00231
lO'M735
0.Oæ37 J
(c), (d), (e) See the tables of part (g)
(f)
0.01751 J
8 pooled =
0.01751 0.01077
( 0.04594
Fisher's linear discriminant function is
y = â'x = -4.67xi - 5.l2x2
where
rñ = -.32
Thus, we allocate Xo to '1i (Bankrupt group) if
âxo - rñ = -4.67xi - 5.12x2 + .32 ~ 0
Otherwise, allocate Xo to '12
APER= :6 = .196.
(Nonbankrupt group).
299
Since 8i and 82 look quite different, Fisher's linear discriminant function
For the
various classification rules and error rates for these variable pairs, see
the following tables.
This is the table of quadratic functions for the variable pairs .(Xb X2),~Xb X3),
and (Xb xs), both with Pi = 0.5 and Pi = 0.05. The classification rule for any
of thee functions is to classify a new observation into 1ii (bankrupt firms)
if
the quadratic function is ~ 0, and to classify the
new observation into
300
'12 (nonbankrupt firms) otherwise. Notice in the table below that only the
constant term changes when the prior probabilties ~hange.
Variables
Prior
Quadratic function
-61.77xi + 35.84xiX2 + 407.20x~ + .s.64xi - 30.60X2
Pi = 0.5
(Xi,X2)
Pi = 0.05
-i.55x~ + 3.S9xiXa - 3.08x3 - 10.69xi + 7.9ûxa
Pi = 0.5
(xi, Xa)
Pi = 0.05
-0.46xf. + 7.75xiX4 + 8.43x¡ - 10.05xi - 8.11x4
Pi = 0.5
(Xl, X4)
Pi = 0.05
+
-
0.17
3.11
3.14
6.08
2.23
0.71
Here is a table of the APER and Ê(AER) for the various variable pairs and
prior probabilties.
APER
Variables
(Xi, X2)
(Xi, xa)
(Xi, X4)
Ê(APR)
Pi = 0.5
Pi = 0.05
Pi = 0.5
Pi = 0.05
0.20
0.11
0.17
0.26
0.37
0.39
0.22
0.13
0.22
0.26
0.39
o ,4t)
For equal priors, it appears that the (Xl, Xa) vaiable pair is the best clasifer,
as it has the lowest APER. For unequal priors, Pi = 0.05 and P2 = 0.95, the
variable pair (xi, X2) has the lowet APER.
301
(h) When using all four variables (Xb X2l X3, X4),
0.04424 0.02847 0.03428 0.00431
-0.0688
Xi
-0.0819
-
,
-
8i
X2
iJ.0330u
1.3675
0.03428 0.02580 0.1'6455
0.4368
0.00431 0.00362 0.03300 0.04441
0.04735 0.00837 0.07543
-u.00662
0.00837 0.u023l 0.00873
D.0003l
2.5939
0.07543 0.00873 1 :04596
0.03177
0.4264
-0.00662 0.00031 0.03177
0.02618
0.2354
-
0.02847 0.02092 0.0258D () .00362
0.0551
,
82
-
Assign a new observation Xo to '1i if its quadratic function .given below is less
than 0:
Prior
Quadratic function
-49.232 -20.657 -2.623
Pi = 0.5
4.91
14.050
-52.493
-28.42
-20.657
526.336
-2.623
11.412
-3.748
1.4337
8.65
14.050 -52.493
1.434
11.974
-11.80
11.412
x'0
xo+
Xo
Pi = 0.05
For Pi = 0.5 : APER = ;6 = .07, Ê(AER) = ;6 = .11
For Pi = D.n5 : APER = :6 = .20, Ê(AER) = ¡~= .24
-
2.69
-
5.64
302
11.25 (a) Fisher's linear discriminant function is
Yo = a' Xo - rñ = -4.80xi - 1.48xg + 3.33
Classify Xo to '1i (bankrupt firms) if
a' Xo - rñ ;: 0
Otherwise classify Xo to '12 (nonbankrupt firms).
The APER is 2:l4 = .13.
, This is the scatterplot of the data in the (xi, Xg) coordinate system, along
with the discriminant line.
5
4
C'
3
x
2
1
-0.6
-0.4
-0.2
0.0
0.2
0.4
0.6
x1
(b) With data point 16 for the bankrupt firms delete, Fisher's linear discrimit
303
function is given by
Yo = a'a;O - m = -5.93xi - 1.46x3 + 3.31
Classify Xo to'1i (bankrupt firms) if
a'xo - m, 2: 0
Otherwise classify Xo to '12 (nonbankrupt firms).
The APER is 1;;4 = .11.
With data point 13 for the nonbankrupt firms deleted, Fisher's linear discriminant function is given by
Yo = a'xo - m = -4.35xi - i.97x3 + 4.36
Classify Xo to '1i (bankrupt firms) if
a/:.o - m ;: 0
Otherwise classify Xo to '12 (nonbankrupt firms).
The APER is 1;;3 = .089.
This is the scatterplot of the observations in the (Xl, X3), coordinate system
with the discriminant lines for the three linear discriminant functions given
abov.e. Als laheUed are observation 16 for bankrupt
firms and obrvtion
304
13 for nonbankrupt firms.
It appears that deleting these observations has changed the line signficantly.
11.26 (a) The least squares regression results for the X, Z data are:
Parameter Estimates
Variable
DF
INTERCEP
1
X3
1
Parameter
Estimate
Standard
Error
Paramet-er=O
Prob ;) ITI
-0.081412
0.307221
o . 13488497
o .05956685
-0.604
5.158
o .5492
T for HO:
0.0001
Here are the dot diagrams of the fitted values for the bankrupt fims and for
the nonbankrupt firms:
305
.. .. .. .... ...
+~--------+---------+---------+---------+---------+----- --Banupt
.
. . ..
.....
+---------+---------+--- ---- --+---------+---------+----- - - N onbanrupt
o . 00 0 . 30 O. 60 0 .90 1. 20 1.50
fitted values:
This table summarizes the classification results using the
OBS
GROUP
13
16
31
banrupt
banrupt
nonbankr
34
38
41
nonbanr
nonbanr
nonbanr
FITTED
CLASSIFICATION
--------------------------------------------o . 57896
0.53122
0.47076
O. 06025
o .48329
o . 30089
misclassify
misclassify
misclassify
misclassify
misclassify
misclassify
The confusion matrix is:
Predicted
Membership
'11 '12
Actual
membership
'11 =1 19 2
'12 J 4 21
Total
t ;;
Thus, the APER is 2:t4 = .13.
(b) The least squares regression results using all four variables Xi, X2, X3, X4 are:
306
Parameter Estimates
Standard
Error
Parameter=O
Pr.ob ;) ITI
1.122
0.335
1.268
3.214
-0 .944
O. 2ô83
o .7393
Variable
DF
Parameter
Estimate
INTERCEP
1
0.208915
Xl
1
o . 156317
0.18615284
0.46653100
X2
X3
X4
1
1. 149093
o . 90606395
1
o . 225972
1
-0.305175
0.07030479
0.32336357
T fo.r HO:
0.2119
o . 0026
O. 3508
Here are the dot diagrams of the fitted values for the bankrupt firms a:nd for
the nonbankrupt firms:
_+_________+_________+_________+_________+_________+__---Banrupt
_+_________+_________+_________+_________+_________+__---N onbankrupt
-0.35 0 . 00 0 .35 0 . 70 1 .05 1.40
This table summarizes the classification results using the fitted values:
OBS
GROUP
FITTD
CLASSIFICATION
o . 62997
o . 72676
misclassify
misclassify
misclassify
misclassify
---------------------------------------------15
16
20
banrupt
banrupt
banrupt
34
nonbanr
0.55719
0.21845
The confusion matrix is:
307
Predicted
Membership
Total
'1i '12
Actual
membership
18 3
1 24 F ;~
:~ j
Thus, the APER is 3::1 = .087. Here is a scatterplot of the residuals against
the fitted values, with points 16 of the bankrupt firms and 13 of the nonbankrupt firms labelled. It appears that point 16 of the bankrupt firms is an
outlier.
+
0
16
+
0.5
+.
"- ~
0
In
-e
:i
"C
'ëi
(I
0.0
bankrupt
nonbankrupt
"'~
°
Cò
+
0
c:
~
-0.5
0.0
,
+
.
+
°eo
+
°
°
13
0
0.5
Fitted Values
11.27 ~a) Plot'Üfthe4ata in the (Xi,X4) variabte space:
1.0
1.5
308
2.5
:;
:;
:;
2.0
:;
:;
~
,'§
-i:
1.5
Ii
-
+
Jo
+
Q)
'V
1.0
+
:;
:;
:;
:;
:;
:; :;
:;
:;
:;
:;
:;
:;
:; :;
:;
:;
:2
:;
:; :; :;
:; :; :; :; :; .
+
+
:;
+ + +
+
.++++
:;++++++
+ + + +
+ +
+ +
+
+++ ++
o
X
o
Setosa
Versiclor
Virginic
o 000
o 00
0 0 00
0000000000
0.5
o
o
2.0
+
~
00 0 0
2.5
3.0
3.5
4.0
X2 (sepal width)
'Shape. However,
The points from all three groups appear to form an ellptical
it appears that the points of '11 (Iris setosa) form an ellpæ with a different
orientation than those of '12 (Iris versicolor) and 113 (Iri virginica). This
indicates that the observations from '1i may have a different covariance matrix
from the observations from '12 and '13.
(b) Here are the results of a test of the null hypothesis Ho : Pi = 1L2 = ¡.3 vel'US
others at the a = 0.05 level
Hi : at least one of the ¡.¡'s is different from the
of significance:
Statistic
WiJ.x'S' Lambda
Value F
o .~2343B63 199.145
Num DF
Den DF Pr)J F
8
288 Q.0001
309
Thus, the null hypothesis Ho : J11 = J12 = J13 is æjected at the Q = 0.05
level of significance. As discussed earlier, the plots give us reason to doubt
the assumption of equal covariance matrices for the three
groups.
(c) '11= Iris setosa; '12 = Iris versicolor '13 = Iris virginica
The quadratic discriminant scores d~(x) given by (11-47) with Pi = P2 =
P3 = l are:
population
~(x) = _1 In ISil- l(x - Xi)' Sii(x - Xi)
'11
-3.68X2 + 6.16x2x4 - 47.60x4 + 23;71x2 + 2.30X4 - 37.67
'12
-9.09x~ + 19.57x2x4 - 22.87x~ + 24.94x2 + 7..ß3x4 - 36.53
'13
-6. 76x~ + 8.54x2X4 - 9.32x~ + 22.92x2 + 12.38x4 - 44.04
To classify the observation x~ - 13.5 1.75), compute Jftxo) for i = 1,2,,3,
and classify Xo to the population for which ~(xo) is the la¡;g.et.
AQ
di (xo) = -103.77
AQ
d2 (xo) = 0.043
cf(xo) = -1.23
So classify Xo to '12 (Iris versicolor).
(d) The linear discriminant scores di(x) are:
population I di(x) = ~SpooledX - l~Spooledæi J dïÍ2;O)
1li . 36.02x2 - 22.26x4 - 59.00 .28.12
'12 i9.3lx2 + 1£.58x4 - 37.73 '58..6
'13 15A9X2 + 3'6.28x4 -59.78 57.92
310
Since d¡(xo) is the largest for í = 2, we classify the new observation x~ =
i3.5 1.75) to'1i according to (11-52). The results are the same for (c) and
(d).
(e) To use rule (11-56), construct dki(X) = dk(x) - di(æ) for all i "It. Then
classify x to'1k if dki(X) ;: 0 for all i = 1,2,3. Here is a table of dn(%o) for
i, k = 1,2,3:
i
1
2
3
0
-30.74
30.74
29.80
0
-29.80
0.94
-0.94
0
1
J 2
i
Since dki(XO) .;: 0 for all i =l 2, we allocate Xo to '12, using (11-52)
Here is the scatterplot of the data in the (X2' X4) variable space, with the
classification regions Ri, R2, and Rg delineated.
2.5
:;
:;
-.i-
U
.~
2.0
:;
;.
:;
:;
.
1.5
eØ
-
+
+
+
ã5
Co
'"
X
1.0
:;
;.
;.
:;
:;
:;
;. ;.
:;
:;
;.
:;
:;
:; ;. :;
;.
:;
;.
;. :;
;. :; ;. ;. ;. ;i
;.
;i++++
;.++++++
+ + + +
+
+ + +
+ +
+
+
::
0.5
0
0
000
00
0
0000000000
00
0
0
2.0
2.5
3.0
0
3.5
X2 ~sepal width)
0
0
0
4.0
0
311
CHAPTER 11. DISCRIMINATION A.ND CLASSIFIC.4.TIOH
36
(f) The APER = ii~ = .033. Ê(AER) = itg = .04
11.28 (a) This is the plot of the data in the (lOgYi, 10gY2) variable space:
0
2.5
-~
-
Cl
.Q
o Setosa
+ Versiclor
~ Virginic
0
0
Oll 00
0 0
OCDai 0
0 0
0
00 0
0 o 0
2.0
0
0
!b
1.5
0
1.0
0
0
0
0
0
00 0
0
0
o
0
0
0
*
+++;.
+;. +
+ t +.t;t + + :P
+ J: +;.
V + + t ;. + ;.
++:t++~
+;.~ ;.'l~~
;.;.
;.;.;.;.
;.;.;. ;.;')l
0.4
0.6 ,
0.8
1.0
log(Y1 )
The points of all three groups appear to follow roughly an eliipse-like pattern. However, the orientation of the ellpse appears to be different for the
observations from '11 (Iris setósa), from the observations from '12 and '13. In
'1i, there also appears to be an outlier, labelled with a "*".
(b), (c) Assuming equal covariance matri.ces and ivariate normal populations,
these are the linear discriminant -scores dit x) for i = 1, 2, 3.
For both variables log Yi, and log 1':
population J df(X) = ä;SpooledX - lä;SpooledZi
'11 . 26.81
7r2 75.10 log Yí + 13.82
log Yi + 28.90 log 1' - 31.97
log 1' - 36.83
7r3 79.94 log Yi + 10.80 IQg Y2 - 37.30
312
For variable log Yi only:
population
¿¡(x) = ~SpooledX - læ~Spooleåæi
'1i
40.90 log Yi - 7.82
'12
81.84 log Yi - 31.30
85.20 log Yi - 33.93
'13
For variable 10gY2 only:
population ¿¡(x) = ~SpooiedX - l~Spooledæi
'11 30.93 log Y2 - 28.73
'12 19.52 log Y2 - 11.44
'13 16.87
Variables
log Yl, log Y2
log Yl
log Y2,
log Y2 + 8.54
APER
E(AER)
26 - 17
27 - 18
150 - .
150 - .
49 - 33
iš -.
49 - 33
150 - .
34 - 23
34 - 23
i50 - .
i50 - .
The preceeding misclassification rates are not nearly as good as those in Ex:-
ample 11.12. Using "shape" is effective in discriminating'1i (iris versicolor)
from '12 and '13. It is not as good at discriminating 7í2 from 1i3, because of
the overlap of '11 and '12 in both shape variables. Therefore, shape is not an
effective discriminator of all three species of iris.
(d) Given the bivarate normal-like scatter and the relatively large
samples, we do not expect the error rates in pars (b) and,(c) to differ.
much.
313
11.29 (a) The calculated values of Xl, Xi, X3, X, and Spooled agree with the results for
these quantities given in Example 11.11
(b)
1518.74 J
,B-
w-i _
1518.74 258471.12
.000003
( 0.000193
0.348899
0.000193) _
( 12.'50
The eigenvalues and scaled eigenvectors of W-l Bare
).i
-
5.646,
A'
ai
0.009
( 5.009 J
).2
-
0.191,
A i
a2
-0.014
( 0'2071
To classify x~ = (3.21 497), use (11-67) and compute
EJ=i(âj(x - Xi))2
i = 1,2,3
Allocate x~ to '1k if
EJ=i(âj(x - Xk))2 ::E;=i (âj(æ - Xi))2
for alli i= Ie
For :.o,
k L~_l(â'.(X - Xk)J2
1 2.63
2 16.99
3 2.43
Thus, classify Xo to '13 This result agrees with thedasifiation given in
Example 11.11. Any time there are three populations with only two discrim-
314
inants, classification results using Fisher's Discriminants wil be identical to
those using the sample distance method of Example 11.11.
11.30 (a) Assuming normality and equal covariance matrices for the three populations
'1i, '12, and '13, the minimum TPM rule is given by:
Allocate xto '1k if the linear discriminant score dk (x) = the largest of di (:.), d2 \ æ ), d3\~
where di(x) is given in the following table for i = 1,2,3.
population
di(x) = ~SpooledX - lX~SpooledXi
'11
0.70xi + 0.58x2 - l3.52x3 + 6.93x4 + 1.44xs - 44.78
'12
1.85xi + 0.32x2 - 12.78x3 + 8.33x4 - 0.14xs - 35.20
'13
2.64xi + 0.20X2 - 2.l6x3 + 5.39x4 - 0.08xs - 23.61
(b) Confusion matrix is:
Predicted
Membership
'1i '12 '13
Actual '11
membership '12
7
7í3
0
1
0
10
3
0
0
35
Total
7
11
38
And the APER O+5~+3 = .071
The holdout confusion matrix is:
Predicted
Membership
'1i '12 '13 Total
me~~~~hiP :: J ~ I ~ 1 :5 ( ~
E(AER)= 2+5~+3 = .125
315
(c) One choice of transformations, Xl, log X2, y', log X4,.. appears to improve the
normality of the data but the classification rule from these data has slightly higher
error rates than the rule derived from the original data. The error rates (APER,
Ê(AER)) for the linear discriminants in Example 11.14 are also slightly higher
than those for the original data.
11.31 (a) The data look fairly normaL.
00
500
0
0
::
cø
0 Q)
0 0
450
Q)
i:
"t
0
00
0
400
0
0
0
0
c6 0
0
0
0
0 00 õJ°
0
0
0000 000 °õJ
+
C\
+
0+
0
+
Gl
X
+0
0
+ +
+
+
0+
+ ~it +
+
a
Alaskan
+
Canadian
60
80
t
120
+
+
+ +++
+
+ +
+
100
+
++
.¡+
+
+
+ 't
+
1+
350
300
+
+
+
+
+
140
160
180
X1 (Freshwater)
Although the covariances have different signs for the two groups, the corr.ela-
tions are smalL. Thus the assumption of bivariate normal distributions with
.equal -covariance matrioes does not seem unreasnable.
316
(b) The linear discriminant function is
â'x - rñ = -0.13xi + 0.052x2 - 5.54
Classify an observation Xo to'1i (Alaskan salmon) if â'xo-m ;: 0 and clasify
Xo to
'12 (Canadian salmon) otherwise.
Dot diagrams of the discriminant scores:
. .. .
... I... .
-------+---------+---------+---------+---------+--------- Alaskan
.. .... ...
. ..
..
.... ". . ".. .......
.. . . . . .
-------+---------+---------+---------+---------+---------Canadian
-8.0
-4.0
0.0
4.0
8.,Q
12.0
It does appear that growth ring diameters separate the two groups reasonably
well, as APER= ~t~ = .07 and E(AER)= ~t~ = .07
( c) Here are the bivariate plots of the data for male and female salmon separately.
317
eo
100 12.0
80
160 180
140
i
mae ¡~:~:%~~;.~"E~"
%
500
0
0
-
45
0
0
CI
i:
.¡:
0
Cb
0
ct
e
o
0
0
0
0
o 0
400
C\
o 0
X
350
o il
0
0
o 0
o
+
o 000+
o
+
+
+ ++
+o +
+
++++
+ 0+
o
00
o
+
óJ
000
+
o
o
++
+ ++
o+0
+
+
o
+
++
+ :.+ +
+
+ +
+
+
+
+
300
140 160 180
X1 (Freshwater)
For the male salmon, these are some summary statistics
. xi
436.1667
( 100.3333 i, Si
-197.71015 1702.31884
( 181.97101 -ì97.71015 J
( ::::::: l' S2
141.64312 760.65036
( 370.17210 141:643121
X2
The linear discriminant function for the male 'Salmon only Is
â'x - m= -0.12xi + 0.D56x2 - 8.12
Classify an observation Xo to 1ii (Alaskan salmon) if â'xo-m;: 0 and clasify
:c to '12 (Canadian -salmon) oth.erwIse.
+
+
+
318
Using this classification rule, APER= 3tal = .08 and E(AER)= 3:ä2 = .w.
For the female salmon, these are some summary statistics
Z¡ - (4::::::: J' s, -210.23231 1097.91539
( 336.33385 -210.23231 i
Z2 - (:::::::: J' S2 120.64000 1038.72ûOO
( 289.21846 120.64000 J
The linear discriminant function for the female salmon only is
â' X - rñ = -O.13xi + O.05X2 - 2.66
Classify an observation xo to'1i (Alaskan salmon) if â'xo-m ~ 0 and classify
xo to '12 (Canadian salmon) otherwise.
Using this classification
rule, APER= 3i;0 = .06 and E(AER)= 3;;0 = .06.
It is unlikely that gender is a useful discriminatory varable, as splitting the
data into female and male salmon did not improve the classification results
greatly.
319
the data for the two groups:
11.32 (a) Here is the bivarate plot of
+
+
0.2
+
+
+
++
++
+
+
0.0
+
+
C\
+
X
+
+
++
+
+
+
+ ++
+0
0
+0 + ã'
+ %+ooo'
+ +
ll 0
+
++õ
+
-0.2
+
+
0
+
+
+
0
0
0
0
0
0
0
0
e
+
+0
+
-0.4
o Noncrrer
+ Ob. airrier
o
-0.6
-0.4
-0.2
0.0
X1
Because the points for both groups form fairly ellptical shapes, the bivariate
normal assumption appears to be a reasonable one. Normal -score plot-s fDr
each group confirm this.
(b) Assuming equal prior probabilties, the sample linear discriminant function is
â'x - ri = i9.32xi - l7.l2x2 + 3.56
Classify an observation Xo to '1i (Noncarriers) if â'xo - rñ ;: .0 and classify
Xo to '12 (Obligatory carriers) otherwise.
The holdout confusion matrix is
320
Predicted
Membership
'1i '12
Actual
membership
'11 j
'12
26 4
8 37
Total
t ~~
Ê(AER)= 4is8 = .16
(c) The classification results for the 10 new cases using the discriminant function
in part (b):
Case
Xl
X2
1
-0.112
2
3
-0.0'59
-0.279
-0.068
0.012
-0.052
-0.098
-0.113
-0.143
-0.037
-0.090
-0.019
4
5
6
7
8
9
10
0.064
-0.043
-0.050
-0.094
-0.123
-0.011
-0.210
-0.126
â' x - rñ Classification
6.17 '1i
3.58 '1i
4.59 111
3.62 lii
4.27 '11
3.68 '1i
3.63 lii
3.98 '11
1.04 7íi
1.45 '11
(d) Assuming that the prior probabilty of obligatory carriers is ~ and that of
, noncarriers is i, the sample linear discriminant function is
â':. - rñ = 19.32xi - 17.12x2 + 4.66
Classify an observation Xo to lii (Noncarriers) if â':.o - rñ :; 0 and classify
::o to '12 (Obligatory carriers) otherwise.
The hold.ut confusion matrix is
321
Predicted
Membership
'11 '12
Actual
membership
:: j ~~ I 2°7
Total
t ~~
Ê(AER)= l~tO = 0.24
The classification results for the 10 new cases using the discriminant function
in part (b):
Case
1
2
3
4
5
6
7
8
9
10
Xi
X2
â'x - ri
-0.112
-0.059
0.064
-0.043
-0.050
-0.094
-0.123
-0.011
-0.210
-0.126
-0.279
-0.068
0.012
-0.052
-0.098
-0.113
-0.143
-0.037
-0.090
-0.019
7.27
4.68
5.69
4.72
5.37
4.78
4.73
5.08
2.14
2.55
Classification
7ri
'1i
'11
'11
7ri
'1i
'11
'1i
'11
'11
11.33 Let X3 = YrHgt, X4 = FtFrBody, X6 = Frame, X7 = BkFat, Xa = SaleHt, and Xg =
SaleWt.
(a) For
'11 = Angus, '12 = Hereford, and '13 = Simental, here are Fisher's linear
discriminants
di
d2
cÎi
-
-3737 + l26.88X3 - 0.48X4 + 19.08x5 - 205.22x6
+275.84x7 + 28.l5xa - 0.03xg
-3686 + l27.70x3 - 0.47X4 + l8.65x5 - 206.18x6
+265.33x7 + 26.80xa - 0.03xg
-3881 + l28.08x3 - 0.48x4 + 19.59xs - 206.36x6
+245.50X7 + 29.47xa - 0:03xg
322
When x~ = (50,1000,73,7, .17,54, 1525J we obtain di = 3596.31, d2 = 3593.32,
and d3 = 3594.13, so assign the new observation to '12, Hereford.
This is the plot of the discriminant scores in the two-dimensional discriminant
space:
2
0
0
0
0
0
~
0
0
.rct
i
C\
;:
0
8000
~
:.
00
0
+
+
0
0+ .p
+
-1
0
+ 0 +
00
:.~
i.
:.
:.
"b
+
+
~
~
0
+
+
:.
+
-2
0
:.
:.
:.
.2
:.
:.:.
~
~
0
:.
?:.
:.
+ + % 0 eO
+
+
~
+
0
2
Angus
Hereford
Simental
4
y1-hat
(b) Here is the APER and Ê(AER) for different subsets of the variable:
Subset I APER Ê(AER)
X3, X4, XS, X6, X7, Xai Xg
X4, Xs, X7, Xa
XS, X7, Xa
X4,XS
X4,X7
X4,Xa
X7,XS
XS,X7
Xs,XS
11.34 For
.13
.14
.21
.43
.36
.32
.22
.25
.28
.25
.20
.24
.46
.39
.36
.22
.29
.32
'11 = General Mils, '12 = Kellogg, and '13 = Quaker and assuming multivariate
flmai data with a 'Cmmon covariance matdx,eaual costs, and equal pri,thes
323
are Fisher's linear discriminant functions:
di
d2
d3
.23x3 + 3.79x4 - 1.69xs - .Olx65.53x7
-
1.90XB + 1.36xg - O.12xio - 33.14
.32x3 + 4.l5x4 - 3.62xs - .02X69.20X7
2.07xB + 1.50xg - 0.20xio - 43.07
.29x3 + 2.64x4 - 1.20xs - .02x65.43x7
1.22xB + .65xg - ü.13xio
The Kellogg cereals appear to have high protein, fiber, and carbohydrates, and
low fat. However, they also have high sugar. The Quaker cereals appear to have
low sugar, but also have low protein and carbohydrates.
Here is a plot of the cereal data in two-dimension discriminant space:
2
0
o ar
0
1
.c
0
C\
;:
;:
;:
0
0
0
0
0
+
0
00+
+
+
+
~
+
+
o~++
-1
.t +
+
;:
;:
-2
+
+
-3
-4
+
;:
+
lU
i
0
;:
-2
0
y1-hat
0
Gen. Mils
+
;:
auar
Kellog
2
324
11.35 (a) Scatter plot of tail length and snout to vent length follows. It appears as if
these variables wil effectively discriminate gender but wil be less successful
in discriminating the age of the snakes.
,:':___::.....-.-' _ _." ..-...-----...----...--...-.. _ _.d:-'..d..d."--"
. Sêatterplotof SntoVnLength.vs Ta.
..
..
..
..
..
~
~
..
il
. .
.
.
.
~
.
~
.. ..
..
..
~
~
~
~
~
..
..
.
..
..
. .
..
.
.. . . ...
.a
... ...
.
.
140160 180
liàjlLength
OD) Linear Discriminant Function for Groups
Female
-36.429
0.039
SntoVnLength
0.310
TailLength
Constant
Male
-41.501
0.163
-0.046
sumary of Classification with Cross-validation
Put into Group
Female
Male
Total N
N correct
True Group
Male
Female
34
3
37
34
i
27
29
27
0.931
Proportion
0.919
N = 66
N Correct = 61
E(AER) = 1 - .924 = .076 ~ 7.6%
Proportion Correct
0.924
325
(e) Linear Discriminant Function for Groups
4
3
2
-112.44 -145.76 -193.14
0.45
0.38
0.33
SntoVnLength
0.65
0.60
0.53
Tai lLength
Constant
sumary of Classification with Cross-validation
True Group
Put into Group
2
3
4
Total N
N correct
proportion
N = 66
2
3
13
4
2
4
0
2
21
0
3
17
13
26
21
21
23
21
0.765
0.808
0.913
Proportion Correct
N Correct = 55
0.833
E(AER)= 1-.833= .167 ~ 16.7%
(d) Linear Discriminant Function for Groups
2
3
4
-141. 94
SntoVnLength 0.36
-102.76
0.41
Constant -79.11
0.48
sumry of Classification with Cross-validation
Put into Group
2
3
4
Total N
N correct
Proportion
N = 66
True Group
2
3
4
14
1
3
21
0
4
0
4
17
14
26
21
0.824
0.808
19
23
19
0.826
N Correct = 54
Proportion Correct
o. a18
E(AER) = 1-.818 = .182 ~ 18.2%
Using only snout to vent length to discriminate the ages of the snakes is
about as effective as using both tail length and snout to vent length.
Although in both cases, there is a reasonably high proportion of
misclassifications.
326
11.36 Logistic Regression Table
95% CI
Odds
Predictor
Constant
Freshwater
Marine
Coef
SE Coef
Z
P
3.92484
0.126051
-0.0485441
6.31500
0.0358536
0.0145240
0.62
3.52
0.534
0.000
0.001
-3.34
Ratio Lower Upper
1.13
0.95
1. 06
0.93
1.22
0.98
Log-Likelihood = -19.394
Test that all slopes are zero: G = 99.841, OF = 2, P-Value = 0.000
The regression is significant (p-value = 0.000) and retaining the constant term
the fitted function is
In( p~z) ) = 3.925+.126(freshwater growth)-.049(marinegrowth)
1- p(z)
Consequently:
Assign z to population 2 (Canadian) if in( p~z) ). ~ 0 ; otherwise assign
1- p(z)
z to population 1 (Alaskan).
The confusion matrix follows.
Predicted
1 2 Total
1 46 4 50
Actual
2
3
47
50
7
APER = - = .07 ~ 7% This is the same APER produced by the linear
100
classification function in Example 11.8.
327
Cha,pter 12
12.1
Democrat
Y~s
1 -+ South
a) Codes:
Yes
Repub 1 icanNo
o -+ non-South No
e.g. Reagan - Cart~r:
i 0
1
1
o
o
2
2
t
P.
a+d = 3/5 = 60
Pair
Coefficient (a+d)lp
R-C
.6
.4
.6
R-F
R-N
R-J
R-K
o
.6
C-F
o
C-N
.2
.4
.6
C-J
C-K
N-J
.8
.6
.4
.4
N-K
;6
F-N
F-J
F-K
J-K
.4
Y.es
No
328
12.1
b)
RankOr¿~r
Coeffi ci ent
1
2
3
1
2
3
.75
4.'5
4.5
10
10
.75
.429
.25
.429
4.5
R-N
.6
.4
.6
R-J
0
0
0
.6
.75
.429
Pair
R-C
R-F
R-K
C-F
C-N
C-J
C-K
F-N
F-J
F-K
N-J
H-K
J-K
.571
0
.2
.4
.6
.8
.6
.4
.4
.6
.4
12.2
0
0
4.5
14.5
4.5
14.5
.333
.111
13 .
.571
.25
.429
.667
.429
.25
.25
.429
.25
10
.75
.889
.75
.571
.571
.75
.571
R-C
R-F
R-N
R-J
R-K
C-F
C-N
C-J
C-K
F-N
F-J
F-K
N-J
N-K
J-K
13
10
10
4.5
14.5
4.5
14.5
13
10
4.5
4.5
4:5
1
1
1
4.5
4.5
4.5
10
10
10
10
10
10
4.5
4.5
4.5
10
10
10
Rank Order
Coeffi c i ent
Pair
4.5
14.5
4.5
14.5
5
6
7
5
6
.333
.5
.2
9
14
9
9
14
14
0
.333
0
.333
0
.5
0
.5
0
.2
9
9
9
0
14
14
14
.2
9
9
9
14
14
12
6
12
6
0
0
0
.2
.333
.111
14
12
.4
.5
.571
.25
.333
6
3
.667
.5
.25
.4
.5
.4
.667
7
3
3
1
1
.8
.5
'1
.667
.333
.143
.25
.333
.25
3
11
6
3
11
11
1i
6
3
3
3
6
'6
6
.4
.571
.667
.571
3
i = (a+b)/p¡ Y = (a+c)/p
1
p
, P
r(x.-xP = (a+b)(1-(a+b)/p)2 + (c+d)(O-(a+b)/pF = (e+d)(a+b)
r(y._y)2 = (a+c)(1-(a+cl/p)2 + (b+d)(O-(a+el/p)2 = (a+C)(b+d)
l' 1 1 1 1
r(x.-x)(y.-y) = r(x.y.-y.i-x.y+xy)
p p p1
= a _ (a+c)(a+b) _ (a+b)(a+c) + p (a+b)(a+c)
p P
= a(a+b+c+d)-~a+eHa+b) = ad-be
Therefore
(ad-bc) lp
r = ((c+dHa+b~~a+C)(b+dl )',
=
ad-be
((a+b He+d) (a+c)(b+d))~
330
12.4
Let c,
=-,
a+d
a+d
c3 =(a+d)+2( b+c)
c =
2
p
1
e3
so
then c3 = 1 +2( c; 1_1)
increases as c,
2
cz
so
A 1 so t c2
increases as
c,
i ncrea s.es
increases
= c; 1 + ,
4
Finally. Cz = c-1+3 so Cz increases as c3 increases
3
12.5 a) Single linkage
2
1
3
4
(12) 3
1 0
(123)
(12) 0
3 11
z
o
4
3
4
5
4 3
o
Dendogram
~
J
:i
1.
-+
-+ 3 (D 0
Z (j 0
~
ri. 2. '3 'l
(123) 4
4
4
o
4 (: oJ
331
12.5 b)
c.) Average Linkage
Compl ete Li nkage
Dendoaram
Dendoaram
10
S
e
4
~
"3
2-
Ll
1-
:i
:: :3 4
.1
~ ;l
12.6
"3
4
Dendograms
Complete Linkage
10
Average Li nkage
,Single Linkage
e
(¿
4-
.2
1. 4 :i 5'3.
1. 4 :z !;-:3 .
1. 4 ;2 S '5.
All three methods produee the same hierarchical arrangements. Item 3 is
somewhat different from the other items.
12.7
Treating correlations as similarity coefficients. we have:
i
Single linkage
'"
A
3
s
l.
I
i
S45 =.68
S(45)1 = max (S4!. S51) = .16
. ,g.w~
I',-'
~
.S1
Jrr-
.;r
5.(45)2 = .32, S'(45)3 = .18, and so forth.
i
1
I
I
i
i
.. i-i -- 3 ~t
j
332
i2~4S
Complete linkage
S45 = .68
. c,g_,,1
.'3
..Sl--S:
S(45)1 = min (S41, S5i) = .12
S(45)2 = .21, S(45)3 = .15, and so forth.
e3
., ~-- 1
Both methods arrive at nearly the same clust.eri ng.
12.8
1
2
3
4
1
0
2
9
0
3
3
7
0
4
6
5
9
a
5
11
10
CD
8
5
1
-+
2
(35)
1
0
2
9
0
(35 )
7
8.5
a
4
6
CD
8.5
a
1. :3 s- :: tt
Average linkage pr~uc.es r~sults si~;l¡r to single linkage.
4
..
0
333
12.9 Dendograms
Singl e Linkage
COl'pl ete Linkage
1.0 t
.S I
."
~
n
..
· 4.
· :i
_i-
5'
LL
.3
:L
i
i.::3 4 S'
'1 .i :3 cf s-
Average L; nkage
a
A1 though the vertical s~a les
are differ~ntt all three linkage
methods produce the same groupings. (Note different vertical
2
scales.)
::
1
1. .: .2 '4 S"
334
12.10 (a) ESSi = (2 - 2)2 = 0, ESS2 = (1 - 1)2 = 0, ESS3 = -(5 - 5)2 = 0, and
ESS4 =(8 - 8)2 = o.
(b) At step 2
Increase
in ESS
Clusters
t 13)-
(3)(2)-
( 14)-
(2)
t 12)-
( 1)
( 1)
( 1)-
(4)- .5
t 4)- 4.5
(3)- 18.0
(4)- 8.0
(23)(24)(2)-
(3)-24.5
~34)- 4.5
(c) At step 3
Increas
Clusters
in ESS
t 12)-
(34)-
( 123)-
(4)
5.0
8.7
Finally all four together have
ESS = (2 - 4)2 + (1 - 4)2 +(5 - 4)2 + (8 - 4)2 = 30
12.11 K = 2 initial clusters (AB) and (CD)
Xl xi
(AB) 3 1
(CD) 1 1
Final clusters (AD) and (Be)
Xi
(AD) 4
(BC) 0
xi
2.5
-.5
Squa red
di stance
~ntr.oids
C1 us ter
(AD)
(BC)
A
8
3.25 29.25
45.25
3.25
to
C
9 ro up
0
27.25
3.25
3.25
11.25
335
12.12 K = 2 initial clusters (AC) and (BD)
Xl x2
(Ae) 3 .5
(BD) -2 -.5
Squared di stance, to group
centroi ds
Final clusters (A) and (BCD)
C1 uster I
Xi x2
(A) 5 3
(A)
(BCD)
A
B
0
52
C
0
40
41
89
4
5
5
(BCD) -1 -1
As expected, this result is the same as the result in Example 12.11. A graph of the
items supports the (A) and (BCD) groupings.
12.13 K = 2 initial clusters (AB) and (CD)
Xi x2
(AB) 2 2
(CD) -1 -2
Final clusters (A) and (BCD)
(A)
(BCD)
Xi
x2
5
3
-1
-1
Squared distan~e
to group
cen troi ds
Cl uster
A
(BCD)
B
C
0
01
40
41
89
52
41
51
A
51
The final clusters (A) and (BCD) are the same as they are in Example 12.11. In
this case we start with the same initial groups and the first, and only, reassignment
is the same. It makes no difference if you star at the top or bottom of the list of
items.
336
12.14. (a) The Euclidean distances between pairs of cereal brands
CL C2 C3 C4 C5 C6 C7 C8 C9 CL0 ~11 C12
CL 0.0
C2116.0 0.0
C3 15.5 121.7 0.0
C4 6 . 4 117. 9 10 . 0 0 .0
C5 103.2 61.6 100.6 102.1 0.0
C6 72.844.178.474.454.3 0.0
C7 86.4 71 .9 82.5 84.9 22.3 52.4 0 . 0
C8 15.3 121.5 1.4 10.1 100.6 78.3 82.4 0.0
C9 46 . 2 72 . 6 54 . 7 48 . 9 75 . 8 32 . 1 65 .2 54 . 5 0 .0
CL0 54.9 123.0 68.9 59.5134.7 87.8 122.5 68.8 65.7 O.~
CL1 81.3 154.7 94.7 85.8169.6 121.3 157.0 94.6 94.5 47.1 0.0
C12 42.3 114.2 31.3 38.5 81.1 75.3 60.2 31.0 59.8 92.9 121.9 0.0
C13 163.2 163.4 177.9 168.1 208.0 155.4205.1 177.9 148.9 112.4 110.7 198.0
C14 46.7 90.8 60.4 51.5 103.8 55.4 92.9 60.3 28.5 44.3 67.5 75.9
C15 60.3 170.5 50.0 56.6 141.5 127.8 121.5 50.0 103.8 101.7 115.6 62;0
C16 46.9 90.8 60.5 51.6 103.8 55.5 92.9 60.3 28.5 44.3 67.6 75.8
C17 23.1 101.0 21.6 21.6 81.4 58.5 63.6 21.4 37.5 70.1 100.7 26.0
C18 265.7 221.1 280.0270.6278.9 233.9 283.3 280.0 235.6 227.7 218.b 294.5
C19 68.2 181.9 60.5 65.2 155.9 138.7 136.2 60.5 113.2 102.7 111.7 76.6
C20116.6 71.0 113.2115.3 19.7 69.9 32.1113.1 89.3 150.5 183.5 90.6
C21103.0 217.7 96.6100.6191.7 174.7171.6 96.6148.1129.7130.5 111.7
C22 98.6 160.1 112.6 103.4 181.3 130.5 170.2 112.6 106.9 54.1 22.5 139.2
C23 58.0 102.8 49.1 54.9 62.4 68.1 41.3 48.9 61.2 105.4 136.9. 20.7
C24 68.1 181.8 60.4 65.2 155.8 138.7 136.1 60.4 113.1 1'02.7 111.6 76.5
C25 49.4 121.0 36.2 44.8 82.5 82.1 62.8 36.2 68.9 101.7 130.2 14.7
C26 182.8 290.3 186.0 183.8 285.6 250.4 267.2 185.9 220.2 173.8 145.7 210.7
C27134.7 99.9 148.2 139.1150.9 1'01.1 152.2 148.2 1-04.2 99.6 113.7 160.9
C28 16.1128.3 14.2 14.2111.1 85.7 92.3 13.7 59.2 63.5 86.3 39.4
C29 107.5 159.0 120.3 111.6 180.7 132.1 170.7 120.3 116.0 54.1 64.6 144.1
C30 33.5 120.1 21.2 29.2 90.7 78.8 71.2 21.0 61.7 83.1 113.7 17.2
C31 78.9 80.5 90.9 82.8 108.5 59.2 103.1 90.8 56.9 52.6 90.6 101.7
C32 32.1 122.6 43.5 36.0 120.8 83.1 105.0 43.3 51.3 50.9 60.0 65.9
C33 143.1 68.0 141.3 142.4 42.0 84.5 61.1 141.2 109.8 170.6 203.8 120.8
C34 173.0 157.7 187.8 177.9 207.5 155.6206.8 187.8 151.8 127.0 123.8 205.9
C3S 116.2 70.4 112.7 114.9 16.9 69.2 30.4 112.6 89.9 148.8 183.8 90.0
C36 114.1 230.0 111.1 112.9 210.2 186.9 190.8 111.1 158.8 129.8 122.7 131.2
C37 53.1 78.2 51.4 52.4 51.6 41.3 34.2 51.1 38.1 91.1124.5 36.6
C38 54.2 100.4 45.8 51.0 61.8 63.5 43.5 45.8 59.0 99.2 133.'6 25.8
C39 48.3 93.5 42.5 45.9 61.0 ~5.1 43.3 42.5 49.6 90.7 125.9 27.3
C40 40.6140.9 51.6 44.3139.8 100.7123.8 51.4 70.3 44.1 46.2 79.4
C41 197.8 309.6 194.3 196.6 288.1 268.0 268.1 194.3 237.8 215.5 194.4 209.9
C42 191.1 301.3 190.3 190.8 286.6 260.4267.3 190.2229.3 200.8 174.~ 209.7
C43 185.2 290.7 189.2 186.6 288.1 251.4 270.2 189.2 221.4 173.6 143.7 214.8
C13 C14 C15 C16 C17 C18 C19 C20 C21 ~22 C23 C24
C13 0 . 0
C14 127.4 0.0
C15 213.2 105.0 0.0
C16127.4 1.0 105.0 0.0
C17 173.1 51.3 69.7 51.3 0.0
C18 134.4220.7 321.2 220.8 270.1 0.0
C19 212.5 11'0.8 16.2 110.9 81.2 322.6 0.0
337
C20 223.2 117.3 151.2 117.3 94.3288.6166.1 0.0
C21 234.6 142.8 50.3142.8117.2347.4 36.5201.2 0.0
C22 91.5 79.1 135.2 79.2 116.8 204.1 131.1 195.9 148.8 0.0
C23 204.9 83.3 81.1 83.2 36.8 295.9 96.2 70.9 130.9 153.2 0 .0
C24 212.5 110.7 16.0 110.8 81.1 322.6 1.4166.036.5 131.1 96.1
0.0
C25207.5 86.0 60.0 86.1 35.2303.9 75.3 91.8 110.1 147.9 23.2 75.3
C26 233.8 200.3 159.3 200.3 '204.2 342.0 143.8 297.3 121.0 152.7 231.2 143.8
C27 67.1 92.1 193.3 92.2 136.5 141.1 197.4 164.6 227.0 105.1 162.0 197.4
C28174.0 59.3 46.7 59.3 30.1278.3 55.0 123.1 89.7 104.7 58.5 54.9
C29 83.1 93.3144.4 93.3122.6214.5141.7 197.4 160.4 51.8156.3 141.7
C30 191.2 73.8 53.3 73.8 24.6 293.2 66.8 102.5 102.5 130.6 34.3 66.8
C31104.8 49.4 135.7 49.3 78.9207.0141.7 124.7 173.2 91.2104.5 141.7
C32 150.5 37.5 75.3 37.5 47.4 248.1 78.9 132.4 108.8 79.4 80.7 78.7
C33 230.0 136.6 181.8 136.5 121.5 283.5 196.3 31.7231.9214.1101.6
196 . 3
C35 221.6 117.8 150.9 117.7 93.7289.9 165.8 10.1 201.0 195.7 70.2
C36 226.8 148.7 71.8 148.7 131.9 341.0 56.0 221.0 28.8 139.2 151.3
165.7
56.0
C34 30.1 132.2 226.4 132.3 180.7 107.3 226.8 221.3 250.8 107.0 210.8 226.8
C37182.4 63.6 95.5' 63.6 31.1 270.0 108.7 64.4 144.7 138.6 27.7
108 .6
95.7
C39188.6 71.5 83.1 71.6 27.4 282.6 96.8 74.6 132.8 140.6 21.8 96.7
C40146.6 52.5 71.8 52.6 62.1252.4 70.9 152.7 96.8 66.6 96.6 70.8
C38198.4 80.8 81.3 80.9 34.1292.4 95.7 74.1131.3 148.9 17.1
C41 301.1 227.1 153.1 227.1 213.8 401.5 140.2 295.1 108.9 210.5 228.7 140 .1
C42 277.2 214.8 154.9 214.9 209.3 375.5 140.8 294.9 112.9 188.1 229.2 140.7
C43 229.1 200.6 165.0200.7207.1 335.7 149.7 300.2 128.8 149.4235.2 149.6
C25 C26 C27 C28 C29' C30 C31 C32 C33 C34C35 C36
C25 0 . 0
C26
C27
C28
C29
213.9 0.0
170.1 257.2 0.0
46.5 175.0 148.2 0.0
152.5 172.5 103.0 113.8 0.0
C30 20.8 200.3 158.2 30.2- 132.8 0.0
C31 111.4 225.7 66~9 91.2 79.1 97.2 0.0
C32 75.0 170.7 126.2 36.4 101.6 62.2 81.5 0.0
C33 122.5 324.8 167.2 151.1 214~1 131.9 137.3 157.0 O.~
C34 215.5 253.2 58.3 184.8 107.8 201.1112.6 158.5 225.1 0.0
C35 91.3297.5 163.7 122.7 194.6 101.0 121.9 133.6 33.3220.7 0.0
C36131.0 93.2 227.1 102.7 152.9 120.7 178.1 114.7 250.8 244.4220.8 0.0
C37 43.5234.6136.1 60.4141.6 44.5 81.7 72.4 91.2 186.663.7 161.4
C38 24.7 230.4 156.4 57.3 148.9 30.7 97.7 81.1 103.2 205.3 72.0150.5
C39 30.1227.7 146.5 53.6 140.6 30.7 87.9 74.5 102.6 195.3 72.6 150.5
C40 86.9 150.1132.6 41.9 88.9 71.1 88.4 24.1177.4158.4153.0 98.1
C41 209.3 98.9 305.4 186.0 236.3 204.2 264.3 190.2 325.4 315.9 297.0 96.8
C42 210.6 71.2 286.8 180.8 216.6 203.0 251.2 179.4 324.1 292.0 296.8 ~4.0
C43218.2 17.7254.4 178.3 170.3204.2225.5 172.3327.1 248.4300.5 100.9
C37 C38 C39 C40 C41 C42 C43
C37 0 . 0
C38 27 .0 0 .0
C39 20.2 10.1 0.0
C40 90.2 94.6 88.5 0.0
C41 241.1 232.1 233.1 177.4 0.0'
C42 237.9 231.7 231.2 164.5 35.2 0.0
C43 237.2233.9230.8 151.2 108.278.7 0
338
(b) Complete linkage produces results similar to single linkage.
Single linkage
~
""
ì3
g
g
~
o
N
CO..
N..
00
o
"'..
_N
00
..
Oõ
MI
ot.
Complete linkage
8..
8..
8
'"
""
ì3
~
o
..""
õú
~"
::~
0l 00
339
12.15.
In K-means method, we use the means of the clusters identified by
average linkae as
the initial cluster centers.
Final cluster centers
1
2
3
4
1 110.0 2.1 0.9 215.0
2 114.4 3.1 1.7 171.1
3 86 . 7 2. 3 o. 5 26.7
4 112.5 3.2 0.8 225.0
for K = 4
5
6
7
8
0.7 15.3 7.9 50.0
2.8 15.0 6.6 123.9
1.4 10.0 5.8 55.8
5.8 12.5 10.8 245.0
K-means
K = 2
1
CL
1
2
C2
C3
C4
C5
C6
C7
C8
C9
1
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
1
1
1
1
1
1
1
C10
C12
C14
C15
C16
C17
C19
C20
C21
C23
C24
C25
C26
C28
C30
C32
C33,
C35
C36
C37
1
1
1
1
C3B
1
C39
C40
C41
C42
C43
C11
C13
C18
C22
C27
C29
C31
~34
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
2
2
2
2
2
2
2
2
C10
C12
C14
C15
C16
C17
C19
. C20
C23
C24
C25
C28
C30
C31
C32
C33
C35
C37
C38
C39
C40
C21
C26
C36
C41
C42
C43
C11
C13
C18
C22
C27
C29
C34
K = 4
1
CL
1
1
C2
C3
C4
C5
C6
1
1
C4
C5
C6
C7
C8
C9
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
C7
C8
C9
C12
C15
C17
C19
C20
C23
C24
C25
C28
C30
C33
C35
C37
1
1
1
C4
C5
1
1
C6
C7
C8
C9
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
C3B
1
C39
CL0
C11
1
2
C14
C16
C22
C29
2
2
2
2
1
1
1
1
2
2
2
2
2
2
3
3
3
3
3
3
3
2
C31
C32
C40
C21
C2q
C36
C41
C42
C43
C13
C18
C27
3
3
3
4
4
4
(;34
4
Single
C1
C2
C3
1
1
1
0.0
1
2 86.1
0.0
3 190.0 162.2
0.0
4 195.4 132.7 275.4
4 clusters
K = 3
C1
C2
C3
Distances between centers
1
2
3
4
2
2
2
3
3
3
1
1
1
1
1
1
1
C10
1
C11
1
C12
1
C13
1
C14
1
C15
1
C16
1
C17
1
C19
1
C20
1
C21
1
C22
1
C23
1
C24
1
C25
1
C27
1
C28
1
C29
1
C30
1
C31
1
C32
1
C33
1
C34
1
C3S
1
C36
1
C37
1
C38
1
C39
1
C40
1
C18 18
C26 26
C43 26
.c41 41
C42 41
Complete
C1
C2
C3
C4
C5
C6
C7
C8
C9
C10
C12
C14
C16
C17
C20
C23
C25
C28
C30
C31
C32
C33
C35
C37
C38
C39
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
~40
1
CL1
11
11
11
11
11
11
15
15
15
15
15
15
.c13
C22
C27
C29
C34
C15
.c19
C21
C24
C26
C36
C41
C42
.c43
Ci8
1S
15
15
18
0.0
340
12.16 (a), (b) Dendrograms for single linkage and complete linkage follow. The
dendrograms are similar; as examples, in both procedures, countries 11, 40 and 46
form a group at a relatively high level of distance, and countries 4, 27, 37, 43, 25
and 44 form a group at a relatively small distance. The clusters are more apparent
in the complete linkage dendrogram and, depending on the distance level, might
have as few as 3 or 4 clusters or as many as 6 or 7 clusters.
341
(c) The results for K = 4 and K = 6 clusters are displayed below. The results seem
reasonable and are consistent with the results for the linkage procedures.
Depending on use, K = 4 may be an adequate number of clusters.
Data Display
Countr ClustMemK=6
ClustMemK=4
1
2
6
2
2
4
3
4
1
4
4
5
3
1
6
7
6
6
2
2
2
8
1
9
4
1
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
5
3
2
6
3
6
6
2
2
2
3
1
4
2
1
2
2
1
2
1
1
4
1
4
4
4
6
6
6
3
3
2
1
Numer of
Cluster1
Cluster2
VCluster3
Cluster4
Within
Average
Maximum
sum of
from
from
298.660
318.294
490.251
182.870
4.494
3.613
11.895
2.681
9.049
6.800
16.915
7.024
Wi thin
Average
Maximum
sum of
from
from
490.251
128.783
11.895
2.669
5.521
cluster distance distance
observations squares centroid "Centroid
11
20
3
20
4
4
1
4
4
4
3
6
Numer of clusters:
2
2
1
Numer of clusters:
4
2
4
4
4
2
2
2
1
1
4
1
4
4
6
4
5
3
2
4
2
4
2
2
5
1
4
3
1
4
4
4
3
2
6
6
4
6
1
4
3
6
2
1
2
1
2
4
6
Numer of
cluster distance distance
observations squares centroid centroid
4.008
2.884
90.154
10
Cluster1
2.428
1.
ti3
22.813
8
Cluster2
6."651
3.346
116. S18
8
Cluster3
5.977
2.513
78.508
10
Cluster4
16.915
¡.lusterS
Cluster6
vi IdcMl.c.a,\
3
15
342
12.17 (a), (b) Dendrograms for single linkage and complete linkage follow. The
dendrograms are similar; as examples, in both procedures, countries 11 and 46
form a group at a relatively high level of distance, and countries 2, 19,35,4,48
and 27 form a group at a relatively small distance. The clusters are more apparent
in the complete linkage dendrogram and, depending on the distance level, might
have as few as 3 or 4 clusters or as many as 6 or 7 clusters.
cOoø . ... , .. ,'~.. .,., , . ' , " . '..' ,
, . ,'~~~~~~~"~"1'\~~~'~'
, Countries
343
(c) The results for K = 4 and K = 6 clusters are displayed below. The results seem
reasonable and are consistent with the results for the linkage procedures.
Depending on use, K = 4 may be an adequate number of clusters. The results
for the men are similar to the results for the women.
Data Display
Country Cl us tMern=4
1
2
3
2
4
5
6
7
2
ClustMem=6
2
2
4
4
4
1
4
1
3
4
6
2
1
8
2
9
4
2
10
11
2
3
2
5
12
13
2
1
2
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
2
1
2
3
1
2
2
4
4
4
6
4
1
2
1
1
2
1
3
2
1
4
2
4
4
6
4
4
6
4
2
30
31
32
2
2
1
33
1
3
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
1
4
4
1
3
4
4
4
2
2
3
49
50
51
52
53
54
2
4
3
1
4
Wi thin
Average
Maximum
cluster distance distance
Numer of sum of
from
from
observations squares centroid centroid
Clusterl
10 169 .042
3.910
5.950
Cluster2
21
73 .281
1. 684
3.041
VCluster3
2
49 .174
4.959
4.959
Cluster4
21
56 .295
1. 481
3.249
Numer of clusters:
4
1
2
5
3
2
4
2
4
2
2
1
2
1
3
2
4
6
Average
Maximum
cluster distance distance
Numer of sum of
from
from
observations squares centroid centroid
Clusterl
12
26.806
1.418
2.413
Cluster2
15
18 _ 764
1.048
1. 844
Cluster3
10 169 _ 042
3.910
5.950
Cluster4
10
10.137
0.935
1.559
vkluster5
2
49.174
4.959
4.959
Cluster6
5
6 _ 451
1.092
1.£06
3
3
2
6
wi thin
1
4
"4
4
4
4
2
2
2
1
1
Numer of clusters:
/.U-t~\'CA
12.18.
344
St~s
.1
(.
'4 ì
io
.1
(.0''7)
i
h
~
.2
. Superior
North
r
.
The multidimensional scaling
configuration is consistent
with the locations of these cities
on a map.
St. Paul t MN
Marshfield
.
,
Wausau
· Appleton
Dubuque, IA
'.
Mad i son
.
Monroe
,.
· Ft. Atki nson
.'
.
Be 1 0; t
· -ch
i1
Mi lwaukee
ago , It
345
12.19.
The stress of final configuration for q=5 is 0.000. The sites in 5 dimensions and the
plot of the sites in two dimensions are
COORDINATES IN 5 DIMNSIONS
v~ABLE
--------
PLO
DIMENSION
--------1
P1980918
A
.51
B
C
D
E
-1. 32
P1SS0960
P1S30987
P1361024
Pl3S100S
P1340945
G
P193ll31
Pl3ll137
P1301062
DIMENSION
F
H
I
.47
.39
.23
.47
.58
-1.12
-.22
2
-.28
3
4
5
-.68
.12
-.05 -.02
.69
.30
.06
-.07
.34
.09
.10
.05
.30 -.32
.12
.14 -.22 -.14 -.28
-,35 .46 .18 -.10
-.31 .~S - .01
-1. 12
,61 -.70 -.06
.01
.24
.62
.19
.05
2I
+
+
I
I
1I
+
+
I
B
I
E
DF
I
oI
+
C
+
I
AG
I
I
I
-1
+
H
+
I
I
I
I
-2
+
+
-2 -1 0 1 2
2
-+------------ --+--------------+--------------+--------------+-
-+--------------+ --------------+ --------------+--------------+-
DIMNSION 1
The results show a definite time pattern (where time of site is frequently determined
by C-14 and tree ring (lumber in great houses) dating).
346
12.20~ A correspondence analysis of the mental health-socioeconomic .data
A correspondence analysis plot of the mental health-socioeconomic-data
Ex
;\12 = 0.026
It
C\
Ò
a Impaired
..It
ò
Ox
It
0
ò
U
...~..~~.~.~.~~t:........................................... L..............:...... ......._.Ç..~...
1.2-0.0014 8- I · MUd
~
9
It
..
9
Ax
It
C\
9
a Well
-a.07
-a.05
-a.03
-a.01
0.01
0.03
c2
u
v
-0.6922 0.1539 0.5588 0.4300
-0.1100 0.3665 -0.7007 0.6022
-0.6266 -0.2313 0.0843 -0.3341
-0.1521 -0.2516 -0.5109 -0.6407
0.0411 -0.8809 -0.0659 0.4670
o . 0265 0 . 5490 0 . 5869 -0. ~756
0.7121 0.2570 0.4388 0.4841
0.4097 0.4668 -0.5519 -0.2297
o . 6448 -0. ~032 0 . 2879 -~. 3062
lambda
0.1613 0.0371 0.0082 0.0000
Cumulative inertia
0.0260 0.0274 0.0275
Cumulative proportion
o . 9475 0.9976 1.0000
The lowest economic class is located between moderate and impaired. The next lowest
das is closetto impairro.
347
12.21. .A correspondence analysis of the income and job satisfaction data
A correspondence analysis plot of the income and jOb satisfaction data
~ $50,000 c
..II
ò
..0
ò
VS
x
II
$25.000 . $50,000 c
0q
q0
......j..i.;;.ö:öööï..................................r.........................................
u II0
9
so
l(
MS
x
c: $25.000 c
~
9
II
N
9 vp
-o.Q5
-0.025
-0.005
0.005
0.015
c2
u
V
-0.6272 -0.2392 0.7412
0.2956 0.8073 0.5107
-0.6503 -0.6661 -0.3561
-0.1944 0.5933 -0.7758
o . 7206 -0.5394 0.4356
-0.3400 0.3159 0.2253
0.6510 -0.3233 -0.4696
lambda
0.1069 0.0106 0.0000
Cumati ve inertia
0.0114 0.0116
Cumulative proportion
0.9902 1.0000
Very satisfied is closest to the highest income group, and v€ry dissatisfid is b€low the
lowest income group. Satisfaction appears to in'Cl'ease with income.
348
12.22. A correspondence analysis of the Wisconsin forest data
A correspondence analysis plot of the Wisconsin for.est data
¡ ì.12 = 0.537
C!
Ironwood 59 D Sugai;Maple
x
S10 D ¡àasswood
x
CD
d
co
d
SSe
"#
d
S7e
C\
d
..u 0d
RedOak
x
'.n ........ .......... n ......... ..................1'.................. ..............Uï;Ö..Ö96
AmericànElm
x.
WhiteOak
x
"#
SSe
9
S4e
BurOak.
CD
9
S2~eS1
'I
BlackOak
x
..C'
I
-0.6
-0.4
-0.2
0.0 0.1 0.2 0.3 0.4 0.5 0.6
c2
349
U
-0.3877 -0.2108 -0.0616 0.4029 -0.0582 0.326S 0.4247 -0.1590
-0.3856 -0.2428 -0.0106 0.4345 -0.1950 -0.1968 -0.2635 -0.3835
-0.3495 -0.1821 0.4079 -0.5718 0.2343 -0.1167 0.3294 -0.1272
-0.3006 0.1355 0.0540 -0.2646 0 .0006 -0.0826 -0.6644 -0.3192
-0.1108 0.5817 -0.4856 -0.1598 -0.2333 0.1607 0.0772 -0.0518
o . 2022 0 . 5400 0 . 4626 0 . 2687 -0.0978 -0.3943 0 . 2668 -0.3606
0.1852 -0.0756 -0.5090 -0.0291 0.6026 -0.1955 0.1520 -0.5154
0.3140 0.0644 0.3394 0.1567 0.3366 0.6573 -0.2507 -0.2267
0.4200 -0.3484 -0.0394 0.1165 -0.0625 -0.3772 -0.1456 0.1381
o . 3549 -0.2897 -0.0345 -0.3393 -0.5994 0 . 20020 . 1262 -0.4907
V
-0.3904 -0.0831 -0.4781 0.4562 -0.0377 0.3369 0.4071 -0.3511
-0.5327 -0.4985 0 .4080 0.0925 -0.0738 -0.3420 -0.2464 -0.3310
-0.1999 0.3889 0.4089 -0.3622 0.4391 0.3217 0.1808 -0.4260
0.0698 0.5382 -0.1726 0.3181 -0.0544 -0.1596 -0.6122 -0.4138
-0.0820 -0.0151 -0.4271 -0.7086 -0.4160 -0.1685 0.0307 -0.3258
0.4005 0.0831 0.1478 0.1866 -0.0042 -0.5895 0.5587 -0.3412
0.3634 -0.4850 -0.3232 -0.0937 0.6298 0.0164 -0.2172 -0.2745
0.4689 -0.2476 0.3150 0.0726 -0.4771 0.5142 -0.0763 -0.3412
lambda
0.7326 0.3101 0.2685 0.2134 0.1052 0.0674 0.0623 0.0000
Cumulati ve inertia
0.5367 0.6329 0.7050 0.7506 0.7616 0.7662 0.7700
Cumulati ve proportion
0.6970 0.8219 0.9155 0.9747 0.9891 0.9950 1.0000
350
12.23' We construct biplot of the pottery type-site data, with row proportions as variables.
Eigenvectors of S
S
0.0511 -0.0059 -0.0390 -0.0061
-0.0059 0.0084 -0.0051 0.0025
-0.0390 -0.0051 0.0628 -0.0187
-0.0061 0.0025 -0.0187 0.0223
0.6233 0.5853 0.1374 -0.5
0.0064 -0.2385 -0.8325 -0.5
-0.7694 0.3464 0.1951 -0.5
O. 1396 -0.6932 0 . 5000 -~. 5
Eigenvalues of S
0.0978 0.0376 0.0091 0.0000
pel pc2 pe3 pe4
St. Dev. 0.3128 0.1940 0.0952 0
Prop. of Vax. 0.6769 0.2604 0.0627 0
Cumulati ve Prop. 0.6769 0.9373 1.0000 1
As in the ~or~esondence analysis.
351
12.24. vVe construct biplot of the mental health-socioeconomic data, with column proportions
as variables.
A bipJot of the mental health-socioeconomic data
-0.15
-0.10
-0.05
0.0
0.05
0.15
0.10
0co
oo
0
c:
c:
oo
0
c:
D
C\
0
C
c:
0C\
Mild
C\
ci
E
0
()
c:
mpaired
0
0
Well
c:
c:
A
C\
0
C\
0
c:.
c:i
Moderate
E
oo
0
c:.
co
0
c:i
-0.10
-0.05
0.0
0.05
0.10
Compo 1
S
Eigenvectors of S
0.003089 0.000809 -0.000413 -0.003485
o . 000809 0 . 000329 -0.000284 -0.000853
-0.000413 -0.000284 0.000379 0.000318
-0.003485 -0.000853 0.000318 0.004021
-0.ô487 0.0837 -0.5676 0.5
'~0.1685 0.4764 0.7033 0.5
0.0794 -0.8320 0.2270 0.5
0.7379 0.2719 -0.3628 0.5
Eigenvalues of S
0.007314 0.000480 0.000024 0.000000
pc1 pc2 pe3 pc4
St. Dev. 0.0855 0.0219 0.0049 0
Prop. of Vax. 0.9355 0.0614 0.0031 0
Cumulati ve Prop. 0.9355 0.9969 1.0000 1
The biplot gives similar locations for health and socioeconomic status. A i"eflction
about the
45 degi-ee line would make them appear more alike.
352
12.25. A Procrustes analysis of archaeological data
A two-dimensional representation of archaeological sites
produced by metric multidimensional scaling
C! _
.-
P1931131
P1301062
0
ia _
c0
iic
ai
E
Pl361024
P~7
C! _
0
PI55096
Õ
pP198il18
1340 5
"C
c0
u
ai
en
~C!
.-f
P1311137
ia
..
-1.0
i
I
i
i
i
-0.5
0.0
0.5
1.0
1.5
2.0
First Dimension
A two-dimensional representation of archaeological sites
produced by nonmetric multidimensional scaling
C! _
.-
P130106
P1931131
o
ia _
oc
iic
Q)
P1351005 P1361024
o
c) P1530987
P155090
E
Õ
"C
C
ou
ai
Il Pl340945
c)I -
en
P19B018
o
P1311137
-7 -
~I
I
-1.0
-0.5
0.0
i
i
0.5
1.0
First Dimension
-T
1.5
2.0
353
Site
P1980918
P1931131
P1550960
P1530987
P1361024
P1351005
P1340945
P1311137
P1301062
Metric MDS
-0.512 -0.278
Nonmetric MDS
-0.276 -0.829
1.318 0.692
1.469 0.703
-0.470 -0.071
-0.545 -0.156
-0.338 -0.048
-0.387 0.088
-0.234 0.296
-0.469 0.137
-0.642 0.387
-0.581 -0.349
-0.889 -0.409
1. 118 -1.122
1. 262 -0. 989
0.216 0.608
0.096 0.963
-0. 137 0 . 379
-0.1459 0 .9893
v
-0 . 9977 -0. 0679
-0 . 0679 O. 9977
Q
Lambda
u
-0.9893 -0. 1459
0.9969 0.0784
-0.0784 0.9969
4.7819 0.000
0.0000 2.715
To better align the metric and nonmetric solutions, we multiply the nonmetrk scaling
solution by the orthogonal matrix Q. This corresponds to clockwise rotation of the
nonmetric solution by 4.5 degrees. After rotation, ,the sum of squared distanc.e, 0.803,
is reduced to the Procrustes measure of fit P R2 = 0.756.
354
12.26 The dendrograms for clustering Mali Family Fars are given below for
average linkage and Ward's method. The dendrograms are similar but a moderate
number of distinct clusters is more apparent in the Ward's method dengrogram
than the average linkage dendrogram. Both dendrograms suggest there may be as
few as 4 clusters (indicated by the checkmarks in the figures) or perhaps as many
either dendrogram
would depend on the use and require some subject matter knowledge.
....:..'...:.......:...
..:..:.'._':.....
.. ....:' -......
.,....: .........,..-....:.:..,
.' '"..-',,....
.... '."...".':.-'"
.. ,-,,',
- .. ':',:-
..,..."..,.....'..,....::..
....', .:..... ," " -.
as 7 or 8 clusters. Reading the "right" number of clusters from
. Average Linkage, Euclidean Distance; Malilf=aI1i1Yi,Fatms
79.43
O.OO"f~W~"~~~~~'\~
Fars
..Waíf
.' . ..kage,
EUdit:eanOisteince;M,.
__ ,.....:.... -:. .c......
c." _:' .....'.. .",......-...., ..: "p"-. _ _ .. .... .. ...... .'
643.37
428.91
Fars
355
12.27 If average linkage and Ward's method clustering is used with the standardized
Mali Family Farm observations, the results are somewhat different from those
using the original observations and different from one another. The dendrograms
follow. There could be as few as 4 clusters
(indicated by the checkmarks in the
figures) or there could be as many as 8 or 9 clusters or more. The distinct clusters
we focus
are more clearly delineated in the Ward's method dendrogram and if
attention on the 4 marked clusters, we see the two procedures produce quite
different results.
'-..,----_._- _.,-- ._-.- _.-_.-- ,,-,',,' ".."." ,-:,:;,--,,:-:::,-,,-,
_..,'::-',.;:-::-_.,--,-.',-.,'.. .---:.--..'"',',,.,-:---.,:-.-'-,,-,",'.",--..i.,.,_.',-':.--,.--.--._:-.:..__.',,",.,-,-'-.,--,,'--.'.','.,'--.',,'-',-,'.-,',',','.--__:,":-::.-',':-.-_--:,_::--X_,d,-i'.-
~ërilge t.jnk~lIe,Eu(¡ndean;lIsll. Mali:fiamil,;f~rm$h('5tandat4jlCc-:
8.03
uCD
i:
5.36'
Û
Q
2.68
....
,'~oall~~
,....~~'\~-:..-~1~¥?TFars
W~td..Llnk.figtjifi)J~lidean Di$t; MållFamilyFafm!i
44.51 .
I 29.68
'e
,8
M
¡
356
12.28 The results for K = 5 and K = 6 clusters follow. The results seem reasonable and
are similar to the results for Ward's method considered in Exercise 12.26. Note as
the number of clusters increases from 5 to 6, cluster 1 in the K = 5 solution is
paritioned into two clusters, 1 and 6, in the K = 6 solution, there is no change in
the other clusters. Although not shown, K = 4 is a reasonable solution as welL.
D.ata Display
Farm
ClustMem=5
ClustMem=6
1
1
2
2
1
2
3
3
3
3
3
4
~
5
5
1
7
4
8
9
4
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
cE
2
4
3
3
3
3
::
5
4
4
2
4
3
3
3
3
3
2
3
4
4
3
3
5
5
3
3
3
3
5
2
3
3
5
5
3
3
3
3
3
2
3
3
3
3
3
3
3
3
4
4
5
3
4
4
5
3
3
3
3
4
5
4
3
3
3
3
2
3
2
3
3
3
2
2
3
3
3
2
2
3
5
3
5
!
18.498
19.511
8.878
9.072
15.030
33.076
24.647
21.053
16.024
19.619
Wi thin
Average
Maximum
696.609
4440.330
3298.539
1129.083
1943.156
1005.125
13.005
19.511
8.878
9.072
15.030
22.418
15.474
24.647
Numer of clusters: 6
Numer of
Cluster1
v-luster2
i.luster3
vCuster4
L.uster5
Cluster6
observations
4
11
35
12
8
2
cluster distance distance
from
from
sum of
squares centroid centroid
3
2
3
2
3
3
3
2
2
3
3
3
2
2
3
3
1
2431.094
4440.330
3298.539
1129.083
1943.156
3
3
4
1
1
8
Maximum
3
3
4
4
2
veuster4
\/luster5
6
11
35
12
Average
5
4
4
4
4
Clusterl
vèluster2
veluster3
2
3
5
5
Numer of
observa tions
Wi thin
cluster distance distance
from
from
sum of
squares centroid centroid
5
3
3
Numer of clusters: 5
4
4
4
2
1
1
1
s-
./ rd~lGL1\ for t-wo CttOl(;è S ö~ K
21. 053
16.024
19.619
22.418
357
follow. The results seem reasonable and
12.29 The results for K = 5 and K = 6 clusters
are similar to the results for Ward's method considered in Exercise 12.27. Note as
the number of clusters increases from 5 to 6, clusters 3 and 4 in the K = 5 solution
lose 1 and 2 farms respectively to form cluster 6 in the K = 6 solution, there is no
change in the other clusters. These results using standardized observations are
somewhat different from the corresponding results using the original data. It
makes a difference whether standardized or un-standardized observations are used.
Data Display
Farm
SdC1usMem~5
SdC1usMe=6
1
2
1
5
1
3
3
3
3
5
6
5
5
1
7
3
8
9
3
1
3
3
4
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
5
3
Numer of clusters:
5
3
3
3
3
3
5
4
4
4
4
3
4
3
3
2
3
3
2
4
4
4
4
4
3
3
3
6
3
3
3
4
2
3
4
4
3
3
3
3
3
3
3
vfuster1
4
3
3
6
3
3
3
42
43
44
45
46
4
4
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
3
3
3
3
3
'68
'69
70
71
72
3
2
5
4
4
4
4
5
3
3
7
Numer of clusters:
2
3
3
4
35
20
vcuster5
4
3
2
3
Average
Maximum
14.050
56.727
55.318
84.099
63.071
1. 568
2.703
4.259
1. 211
1.954
2.970
1. 993
3.172
3.482
Within
Average
Maximum
14.050
56.727
1.568
3.288
2.703
4.259
3.288
3
3
4
3
4
5
3
3
39
40
41
5
C1uster4
4
3
5
Cluster3
3
4
3
within
cluster distance distance
from
from
sum of
Numer 0 f
observations squares centroid centroid
L.1 us ter2
3
5
6
3
l/lusterl
i. us ter2
6
cluster distance distance
from
from
sum of
Numer 0 f
observations squares centroid centroid
5
5
Cluster3
Cluster4
34
18
Cluster6
7
3
~uster5
51. 228
65.501
63.071
7.960
1. 183
1. 806
2.970
1.604
4
5
3
3
2
3
4
3
2
5
4
4
4
4
5
3
3
4
4
4
3
4
3
3
4
2
4
1
1
1
1
1
1
5
5
3
2
/ IdetthcOv\ for f£.uc c.ÛDtc.S o( K
1. 951
3.195
3.482
1. 954
358
12.30 The cumulative lift (gains) chart is shown below. The y-axis shows the per-centage
of positive responses. This is
the percentage of the total possible positive
responses (20,000). The x-axis shows the percentage of customers contacted,
which is a fraction of the 100,000 total customers. With no model, if we -contact
10% of the customers we would expect 10%, or 2,000 = .1 x20,000, of the
positive
responses. Our response model predicts 6,000 or 30% of the positive responses if
we contact the top to,OOO customers. Consequently, the y-values at x = 10%
shown in the char are 10% for baseline (no model) and 30% for the gain (lift)
provided by the modeL. Continuing this argument for other choices of x
(% customers contacted) and cumulating the results produces the lift (gains) chart
shown. We see, for example, if we contact the top 40% of the customers
determined by the model, we expect to get 80% of the positive responses.
Cumulative Gains Chart
100
Ul
CD
Ul
c:
&.
Ul
90
BO
70
60
-+ Lift Curve
CD
-- Baseline
ix 50
CD
E~Ul 40
30
o
0. 20
~ 10 ~
o
o 10 20 30 40 SO 60 70 80 90 100
% Customers Contacted
359
12.31 (a) The Mclust function, which selects the best overall model according to
the BIC criterion, selects a mixture with four multivariate normal components. The four estimated centers are:
..Pi =
3.3188
6.7044
0.3526
0.1418
11.9742
5.1806
5.2871
0.5910
0.1794
5.5369
íÆ2
,. =
7.2454
4.8099
0.3290
0.2431
3.2834
, ¡¿3 =
,
-P4 =
8.6893
4.1730
0.5158
0.2445
7.4846
and the estimated covariance matrices turn out to be restricted to be of the
form 1JkD where D is a diagonal matrix.
The estimated
D = diag(l1.2598, 2.7647,0.3355,0.0053,18.0295)
and the estimated scale factors are 17i = 0.0319, 172 = 0.3732, 173 = 0,0909,
174 = 0.1073.
Theestimatedproportionsarepi = 0.1059, P2 = 0.4986, P3 = 0.1322,P4 =
0.2633.
This minimum BIC model has BIG = -547.1408.
(b) The model chosen above has 4 multivariate normal components.
These four components are shown in the matrix scatter plot where the observations have been classified into one of the four populations.
The matrix scatter plot of the true classification, is given in the next
figure.
Comparing the matrix scatter plot of the four group classification with
the matrix scatter plot of the true classification, we see how the oil samples
from the Upper sandstone are essentially split into two groups. This is clear
from comparing the two scatter plots for (Xb X2).
We also repeat the analysis using the me function to select mixture distribution with K = 3 components. We further restrict the covariance matrices
to satisfy ~k = 1JkD. The K = 3 groups selected by this function have
estimated centers
..Pi =
5.3395
5.2467
0.5485
0.1862
5.2465
,
..P2 =
8.5343
4.2762
0.4988
0.2453
6.6993
,
P3 =
3.3228
6.7093
0.3511
0.1418
11.9780
360
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Figure 1: Classification into four groups using Mclust
..
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10
12
N
361
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classification into sandstone strata
II
Cl
Upper
0
Wilhelm
~
T
i
10
12
N
362
the estimated diagonal matrix
D = diag(1O.1535, 2.6295,0.2969,0.0052,24.0955)
with estimated scale parameters rii = 0.3702, rì2 = 0.1315, rì3 = 0.0314, with
resulting BIG = -534.0949.
The estimated proportions are Pi = 0.5651, P2 = 0.3296, P3 = 0.1052.
If we use this method to classify the oil samples, the following samples
are misclassified:
7 19 22 25 26 27 28 29 30 31
32 33 34 35 39 44 45 46 49
and the misclassification error rate is 33.93%.
Source Exif Data:
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