Sleuth3 Manual

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Package ‘Sleuth3’
January 24, 2019
Title Data Sets from Ramsey and Schafer's ``Statistical Sleuth (3rd Ed)''
Version 1.0-3
Date 2019-01-24
Author Original by F.L. Ramsey and D.W. Schafer;
modifications by Daniel W. Schafer, Jeannie Sifneos and Berwin
A. Turlach; vignettes contributed by Nicholas Horton, Linda Loi,
Kate Aloisio and Ruobing Zhang, with corrections by Randall Pruim
Description Data sets from Ramsey, F.L. and Schafer, D.W. (2013), ``The
Statistical Sleuth: A Course in Methods of Data Analysis (3rd
ed)'', Cengage Learning.
Maintainer Berwin A Turlach 
LazyData yes
Depends R (>= 3.5.0)
Suggests CCA, Hmisc, MASS, agricolae, car, gmodels, knitr, lattice, leaps, mosaic, multcomp
VignetteBuilder knitr
License GPL (>= 2)
URL http://r-forge.r-project.org/projects/sleuth2/

R topics documented:
Sleuth3-package .
case0101 . . . .
case0102 . . . .
case0201 . . . .
case0202 . . . .
case0301 . . . .
case0302 . . . .
case0401 . . . .
case0402 . . . .
case0501 . . . .
case0502 . . . .
case0601 . . . .
case0602 . . . .
case0701 . . . .
case0702 . . . .
case0801 . . . .

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R topics documented:

2
case0802
case0901
case0902
case1001
case1002
case1101
case1102
case1201
case1202
case1301
case1302
case1401
case1402
case1501
case1502
case1601
case1602
case1701
case1702
case1801
case1802
case1803
case1901
case1902
case2001
case2002
case2101
case2102
case2201
case2202
ex0112 . .
ex0116 . .
ex0125 . .
ex0126 . .
ex0127 . .
ex0211 . .
ex0218 . .
ex0221 . .
ex0222 . .
ex0223 . .
ex0321 . .
ex0323 . .
ex0327 . .
ex0330 . .
ex0331 . .
ex0332 . .
ex0333 . .
ex0428 . .
ex0429 . .
ex0430 . .
ex0431 . .
ex0432 . .

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24
25
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91

R topics documented:
ex0518 .
ex0523 .
ex0524 .
ex0525 .
ex0623 .
ex0624 .
ex0721 .
ex0722 .
ex0724 .
ex0725 .
ex0726 .
ex0727 .
ex0728 .
ex0729 .
ex0730 .
ex0816 .
ex0817 .
ex0820 .
ex0822 .
ex0823 .
ex0824 .
ex0825 .
ex0826 .
ex0828 .
ex0829 .
ex0914 .
ex0915 .
ex0918 .
ex0920 .
ex0921 .
ex0923 .
ex1014 .
ex1026 .
ex1027 .
ex1028 .
ex1029 .
ex1030 .
ex1031 .
ex1033 .
ex1111 .
ex1120 .
ex1122 .
ex1123 .
ex1124 .
ex1125 .
ex1217 .
ex1220 .
ex1221 .
ex1222 .
ex1223 .
ex1225 .
ex1317 .

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91
92
93
94
95
95
96
97
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102
103
104
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106
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116
117
118
119
120
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122
123
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124
125
126
126
127
129
130
131
132
133
134

R topics documented:

4
ex1319 .
ex1320 .
ex1321 .
ex1416 .
ex1417 .
ex1419 .
ex1420 .
ex1507 .
ex1509 .
ex1514 .
ex1515 .
ex1516 .
ex1517 .
ex1518 .
ex1519 .
ex1605 .
ex1611 .
ex1612 .
ex1613 .
ex1614 .
ex1615 .
ex1620 .
ex1708 .
ex1715 .
ex1716 .
ex1914 .
ex1916 .
ex1917 .
ex1918 .
ex1919 .
ex1921 .
ex1922 .
ex1923 .
ex2011 .
ex2012 .
ex2015 .
ex2016 .
ex2017 .
ex2018 .
ex2019 .
ex2113 .
ex2115 .
ex2116 .
ex2117 .
ex2118 .
ex2119 .
ex2120 .
ex2216 .
ex2220 .
ex2222 .
ex2223 .
ex2224 .

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135
136
137
138
139
140
141
142
142
143
144
144
145
146
146
147
148
149
149
150
151
152
152
153
154
155
156
156
157
158
159
159
160
161
162
163
164
165
166
167
167
168
170
170
171
172
173
174
175
175
176
177

Sleuth3-package
ex2225 . . . . .
ex2226 . . . . .
ex2414 . . . . .
Sleuth3Manual

5
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Index

Sleuth3-package

.
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.
.

178
178
179
180
181

The R Sleuth3 package

Description
Data sets from Ramsey and Schafer’s "Statistical Sleuth (3rd ed)"
Details
This package contains a variety of datasets. For a complete list, use library(help="Sleuth3") or
Sleuth3Manual().
Author(s)
Original by F.L. Ramsey and D.W. Schafer
Modifications by Daniel W Schafer, Jeannie Sifneos and Berwin A Turlach
Maintainer: Berwin A Turlach 

case0101

Motivation and Creativity

Description
Data from an experiment concerning the effects of intrinsic and extrinsic motivation on creativity.
Subjects with considerable experience in creative writing were randomly assigned to on of two
treatment groups.
Usage
case0101
Format
A data frame with 47 observations on the following 2 variables.
Score creativity score
Treatment factor denoting the treatment group, with levels "Extrinsic" and "Intrinsic"
Source
Ramsey, F.L. and Schafer, D.W. (2013). The Statistical Sleuth: A Course in Methods of Data
Analysis (3rd ed), Cengage Learning.

6

case0102

References
Amabile, T. (1985). Motivation and Creativity: Effects of Motivational Orientation on Creative
Writers, Journal of Personality and Social Psychology 48(2): 393–399.
Examples
attach(case0101)
str(case0101)
boxplot(Score ~ Treatment)

# Basic boxplots for each level of Treatment

boxplot(Score ~ Treatment, # Boxplots with labels
ylab= "Average Creativity Score From 11 Judges (on a 40-point scale)",
names=c("23 'Extrinsic' Group Students","24 'Intrinsic' Group Students"),
main= "Haiku Creativity Scores for 47 Creative Writing Students")
detach(case0101)

case0102

Sex Discrimination in Employment

Description
The data are the beginning salaries for all 32 male and all 61 female skilled, entry–level clerical
employees hired by a bank between 1969 and 1977.
Usage
case0102
Format
A data frame with 93 observations on the following 2 variables.
Salary starting salaries (in US$)
Sex sex of the clerical employee, with levels "Female" and "Male"
Source
Ramsey, F.L. and Schafer, D.W. (2013). The Statistical Sleuth: A Course in Methods of Data
Analysis (3rd ed), Cengage Learning.
References
Roberts, H.V. (1979). Harris Trust and Savings Bank: An Analysis of Employee Compensation,
Report 7946, Center for Mathematical Studies in Business and Economics, University of Chicago
Graduate School of Business.
See Also
case1202

case0201

7

Examples
attach(case0102)
str(case0102)
boxplot(Salary ~ Sex,
ylab= "Starting Salary (U.S. Dollars)",
names=c("61 Females","32 Males"),
main= "Harris Bank Entry Level Clerical Workers, 1969-1971")
hist(Salary[Sex=="Female"])
dev.new()
hist(Salary[Sex=="Male"])
t.test(Salary ~ Sex, var.equal=TRUE) # Equal var. version; 2-sided by default
t.test(Salary ~ Sex, var.equal=TRUE,
alternative = "less") # 1-sided; that group 1 (females) mean is less
detach(case0102)

case0201

Peter and Rosemary Grant’s Finch Beak Data

Description
In the 1980s, biologists Peter and Rosemary Grant caught and measured all the birds from more
than 20 generations of finches on the Galapagos island of Daphne Major. In one of those years,
1977, a severe drought caused vegetation to wither, and the only remaining food source was a
large, tough seed, which the finches ordinarily ignored. Were the birds with larger and stronger
beaks for opening these tough seeds more likely to survive that year, and did they tend to pass this
characteristic to their offspring? The data are beak depths (height of the beak at its base) of 89
finches caught the year before the drought (1976) and 89 finches captured the year after the drought
(1978).
Usage
case0201
Format
A data frame with 178 observations on the following 2 variables.
Year Year the finch was caught, 1976 or 1978
Depth Beak depth of the finch (mm)
Source
Ramsey, F.L. and Schafer, D.W. (2013). The Statistical Sleuth: A Course in Methods of Data
Analysis (3rd ed), Cengage Learning.
References
Grant, P. (1986). Ecology and Evolution of Darwin’s Finches, Princeton University Press, Princeton, N.J.

8

case0202

See Also
ex0218
Examples
attach(case0201)
str(case0201)
mean(Depth[Year==1978]) - mean(Depth[Year==1976])
yearFactor <- factor(Year) # Convert the numerical variable Year into a factor
# with 2 levels. 1976 is "group 1" (it comes first alphanumerically)
t.test(Depth ~ yearFactor, var.equal=TRUE) # 2-sample t-test; 2-sided by default
t.test(Depth ~ yearFactor, var.equal=TRUE,
alternative = "less") # 1-sided; alternative: group 1 mean is less
boxplot(Depth
ylab= "Beak
names=c("89
main= "Beak

~ Year,
Depth (mm)",
Finches in 1976","89 Finches in 1978"),
Depths of Darwin Finches in 1976 and 1978")

## BOXPLOTS FOR PRESENTATION
boxplot(Depth ~ Year,
ylab="Beak Depth (mm)", names=c("89 Finches in 1976","89 Finches in 1978"),
main="Beak Depths of Darwin Finches in 1976 and 1978", col="green",
boxlwd=2, medlwd=2, whisklty=1, whisklwd=2, staplewex=.2, staplelwd=2,
outlwd=2, outpch=21, outbg="green", outcex=1.5)
detach(case0201)

case0202

Anatomical Abnormalities Associated with Schizophrenia

Description
Are any physiological indicators associated with schizophrenia? In a 1990 article, researchers reported the results of a study that controlled for genetic and socioeconomic differences by examining
15 pairs of monozygotic twins, where one of the twins was schizophrenic and the other was not. The
researchers used magnetic resonance imaging to measure the volumes (in cm3 ) of several regions
and subregions of the twins’ brains.
Usage
case0202
Format
A data frame with 15 observations on the following 2 variables.
Unaffected volume of left hippocampus of unaffected twin (in cm3 )
Affected volume of left hippocampus of affected twin (in cm3 )

case0301

9

Source
Ramsey, F.L. and Schafer, D.W. (2013). The Statistical Sleuth: A Course in Methods of Data
Analysis (3rd ed), Cengage Learning.
References
Suddath, R.L., Christison, G.W., Torrey, E.F., Casanova, M.F. and Weinberger, D.R. (1990). Anatomical Abnormalities in the Brains of Monozygotic Twins Discordant for Schizophrenia, New England
Journal of Medicine 322(12): 789–794.
Examples
attach(case0202)
str(case0202)
diff <- Unaffected-Affected
summary(diff)
t.test(diff) # Paired t-test is a one-sample t-test on differences
t.test(Unaffected,Affected,pair=TRUE) # Alternative coding for the same test
boxplot(diff,
ylab="Difference in Hippocampus Volume (cubic cm)",
xlab="15 Sets of Twins, One Affected with Schizophrenia",
main="Hippocampus Difference: Unaffected Twin Minus Affected Twin")
abline(h=0,lty=2)
# Draw a dashed (lty=2) horizontal line at 0
## BOXPLOT FOR PRESENTATION:
boxplot(diff,
ylab="Difference in Hippocampus Volume (cubic cm)",
xlab="15 Sets of Twins, One Affected with Schizophrenia",
main="Hippocampus Difference: Unaffected Minus Affected Twin",
col="green", boxlwd=2, medlwd=2, whisklty=1, whisklwd=2,
staplewex=.2, staplelwd=2, outlwd=2, outpch=21, outbg="green",
outcex=1.5)
abline(h=0,lty=2)
detach(case0202)

case0301

Cloud Seeding

Description
Does dropping silver iodide onto clouds increase the amount of rainfall they produce? In a randomized experiment, researchers measured the volume of rainfall in a target area (in acre-feet) on 26
suitable days in which the clouds were seeded and on 26 suitble days in which the clouds were not
seeded.
Usage
case0301

10

case0301

Format
A data frame with 52 observations on the following 2 variables.
Rainfall the volume of rainfall in the target area (in acre-feet)
Treatment a factor with levels "Unseeded" and "Seeded" indicating whether the clouds were
unseeded or seeded.
Source
Ramsey, F.L. and Schafer, D.W. (2013). The Statistical Sleuth: A Course in Methods of Data
Analysis (3rd ed), Cengage Learning.
References
Simpson, J., Olsen, A., and Eden, J. (1975). A Bayesian Analysis of a Multiplicative Treatment
Effect in Weather Modification. Technometrics 17: 161–166.
Examples
attach(case0301)
str(case0301) #Seeded is level 1 of Treatment (it's first alphabetically)
boxplot(Rainfall ~ Treatment)
boxplot(log(Rainfall) ~ Treatment) # Boxplots of natural logs of Rainfall
t.test(log(Rainfall) ~ Treatment, var.equal=TRUE,
alternative="greater") # 1-sided t-test; alternative: level 1 mean is greater
myTest <- t.test(log(Rainfall) ~ Treatment, var.equal=TRUE,
alternative="two.sided") # 2-sided alternative to get confidence interval
exp(myTest$est[1] - myTest$est[2]) # Back-transform estimate on log scale
exp(myTest$conf) # Back transform endpoints of confidence interval
boxplot(log(Rainfall) ~ Treatment,
ylab="Log of Rainfall Volume in Target Area (Acre Feet)",
names=c("On 26 Seeded Days", "On 26 Unseeded Days"),
main="Distributions of Rainfalls from Cloud Seeding Experiment")
## POLISHED BOXPLOTS FOR PRESENTATION:
opar <- par(no.readonly=TRUE) # Store device graphics parameters
par(mar=c(4,4,4,4))
# Change margins to allow more space on right
boxplot(log(Rainfall) ~ Treatment, ylab="Log Rainfall (Acre-Feet)",
names=c("on 26 seeded days","on 26 unseeded days"),
main="Boxplots of Rainfall on Log Scale", col="green", boxlwd=2,
medlwd=2, whisklty=1, whisklwd=2, staplewex=.2, staplelwd=2,
outlwd=2, outpch=21, outbg="green", outcex=1.5
)
myTicks <- c(1,5,10,100,500,1000,2000,3000) # some tick marks for original scale
axis(4, at=log(myTicks), label=myTicks)
# Add original-scale axis on right
mtext("Rainfall (Acre Feet)", side=4, line=2.5) # Add right-side axis label
par(opar) # Restore previous graphics parameter settings
detach(case0301)

case0302

case0302

11

Agent Orange

Description
In 1987, researchers measured the TCDD concentration in blood samples from 646 U.S. veterans of
the Vietnam War and from 97 U.S. veterans who did not serve in Vietnam. TCDD is a carcinogenic
dioxin in the herbicide called Agent Orange, which was used to clear jungle hiding areas by the
U.S. military in the Vietnam War between 1962 and 1970.
Usage
data(case0302)
Format
A data frame with 743 observations on the following 2 variables.
Dioxin the concentration of TCDD, in parts per trillion
Veteran factor variable with two levels, "Vietnam" and "Other", to indicate the type of veteran
Source
Ramsey, F.L. and Schafer, D.W. (2013). The Statistical Sleuth: A Course in Methods of Data
Analysis (3rd ed), Cengage Learning.
References
Centers for Disease Control Veterans Health Studies: Serum 2,3,7,8-Tetraclorodibenzo-p-dioxin
Levels in U.S. Army Vietnam-era Veterans. Journal of the American Medical Association 260:
1249–1254.
Examples
attach(case0302)
str(case0302)
# Note: Level 1 of Veteran is "Other" (first alphabeticall)
boxplot(Dioxin ~ Veteran)
t.test(Dioxin ~ Veteran, var.equal=TRUE,
alternative="less") # 1-sided t-test; alternative: group 1 mean is less
t.test(Dioxin ~ Veteran, alternative="less", var.equal=TRUE,
subset=(Dioxin < 40)) # t-test on subset for which Dioxin < 40
t.test(Dioxin ~ Veteran, alternative="less", var.equal=TRUE,
subset=(Dioxin < 20))
t.test(Dioxin ~ Veteran, var.equal=TRUE) # 2-sided--to get confidence interval
## HISTOGRAMS FOR PRESENTATION
opar <- par(no.readonly=TRUE) # Store device graphics parameter settings
par(mfrow=c(2,1), mar=c(3,3,1,1)) # 2 by 1 layout of plots; change margins
myBreaks <- (0:46) - .5
# Make breaks for histogram bins
hist(Dioxin[Veteran=="Other"], breaks=myBreaks, xlim=range(Dioxin),
col="green", xlab="", ylab="", main="")

12

case0401
text(10,25,
"Dioxin in 97 'Other' Veterans; Estimated mean = 4.19 ppt (95% CI: 3.72 to 4.65 ppt)",
pos=4, cex=.75) # CI from 1-sample t-test & subset=(Veteran="Other")
hist(Dioxin[Veteran=="Vietnam"],breaks=myBreaks,xlim=range(Dioxin),
col="green", xlab="", ylab="", main="")
text(10,160,
"Dioxin in 646 Vietnam Veterans; Estimated mean = 4.26 ppt (95% CI: 4.06 to 4.64 ppt)",
pos=4, cex=.75)
text(13,145,"[Estimated Difference in Means: 0.07 ppt (95% CI: -0.63 to 0.48 ppt)]",
pos=4, cex=.75)
par(opar) # Restore previous graphics parameter settings
detach(case0302)

case0401

Space Shuttle

Description
The number of space shuttle O-ring incidents for 4 space shuttle launches when the air temperatures
was below 65 degrees F and for 20 space shuttle launches when the air temperature was above 65
degrees F.
Usage
case0401
Format
A data frame with 24 observations on the following 2 variables.
Incidents the number of O-ring incidents
Launch factor variable with two levels—"Cool" and "Warm"
Source
Ramsey, F.L. and Schafer, D.W. (2013). The Statistical Sleuth: A Course in Methods of Data
Analysis (3rd ed), Cengage Learning.
References
Feynman, R.P. (1988). What do You Care What Other People Think? W. W. Norton.
See Also
ex2011, ex2223

case0402

13

Examples
str(case0401)
attach(case0401)
mCool <- mean(Incidents[Launch=="Cool"])
mWarm <- mean(Incidents[Launch=="Warm"])
mDiff <- mCool - mWarm
c(mCool,mWarm,mDiff) # Show the values of these variables
## PERMUTATION TEST , VIA REPEATED RANDOM RE-GROUPING (ADVANCED)
numRep <- 50 # Number of random groupings. CHANGE TO LARGER NUMBER; eg 50,000.
rDiff
<- rep(0,numRep) # Initialize this variable to contain numRep 0s.
for (rep in 1:numRep) { # Repeat the following commands numRep times:
randomGroup <- rep("rWarm",24) # Set randomGroup to have 24 values "rWarm"
randomGroup[sample(1:24,4)] <- "rCool" # Replace 4 at random with "rCool"
mW <- mean(Incidents[randomGroup=="rWarm"]) # average of random "rWarm" group
mC <- mean(Incidents[randomGroup=="rCool"]) # average of random "rCool" group
rDiff[rep] <- mC-mW # Store difference in averages in 'rep' cell of rDiff
} # End of loop
hist(rDiff, # Histogram of difference in averages from numRep random groupings
main="Approximate Permutation Distribution",
xlab="Possible Values of Difference in Averages",
ylab="Frequency of Occurrence")
abline(v=mDiff) # Draw a vertical line at the actually observed difference
pValue <- sum(rDiff >= 1.3)/numRep # 1-sided p-value
pValue
text(mDiff,75000, paste(" -->",round(pValue,4)), adj=-0.1)
detach(case0401)

case0402

Cognitive Load

Description
Educational researchers randomly assigned 28 ninth-year students in Australia to receive coordinate
geometry training in one of two ways: a conventional way and a modified way. After the training,
the students were asked to solve a coordinate geometry problem. The time to complete the problem
was recorded, but five students in the “conventional” group did not complete the solution in the five
minute alloted time.
Usage
case0402
Format
A data frame with 28 observations on the following 3 variables.
Time the time (in seconds) that the student worked on the problem
Treatment factor variable with two levels—"Modified" and "Conventional"
Censored 1 if the individual did not complete the problem in 5 minutes, 0 if they did

14

case0501

Source
Ramsey, F.L. and Schafer, D.W. (2013). The Statistical Sleuth: A Course in Methods of Data
Analysis (3rd ed), Cengage Learning.
References
Sweller, J., Chandler, P., Tierney, P. and Cooper, M. (1990). Cognitive Load as a Factor in the
Structuring of Technical Material, Journal of Experimental Psychology General 119(2): 176–192.
Examples
str(case0402) # level 1 of Treatment is "Conventional" (1st alphabetically)
attach(case0402)
boxplot(Time ~ Treatment)
median(Time[Treatment=="Conventional"])-median(Time[Treatment=="Modified"])
wilcox.test(Time ~ Treatment, exact=FALSE,
alternative="greater") # Rank-sum test;
wilcox.test(Time ~ Treatment, exact=FALSE,
alternative="two.sided", conf.int=TRUE)

correct=TRUE,
alternative: group 1 is greater
correct=TRUE,
# Use 2-sided to get confidence int.

## DOT PLOTS FOR PRESENTATION
xTreatment
<- ifelse(Treatment=="Conventional",1,2) # Make numerical values
myPointCode
<- ifelse(Censored==0,21,24)
plot(Time ~ jitter(xTreatment,.2),
# Jitter the 1's and 2's for visibility
ylab="Completion Time (Sec.)", xlab="Training Method (jittered)",
main="Test Completion Times from Cognitive Load Experiment",
axes=FALSE, pch=myPointCode, bg="green", cex=2, xlim=c(.5,2.5) )
axis(2) # Draw y-axis as usual
axis(1, tick=FALSE, at=c(1,2), # Draw x-axis without ticks
labels=c("Conventional (n=14 Students)","Modified (n=14 Students)") )
legend(1.5,300, legend=c("Did not Complete in 300 sec","Completed in 300 sec."),
pch=c(24,21), pt.cex=2, pt.bg="green")
detach(case0402)

case0501

Diet Restriction and Longevity

Description
Female mice were randomly assigned to six treatment groups to investigate whether restricting
dietary intake increases life expectancy. Diet treatments were:
1. "NP"—mice ate unlimited amount of nonpurified, standard diet
2. "N/N85"—mice fed normally before and after weaning. After weaning, ration was controlled
at 85 kcal/wk
3. "N/R50"—normal diet before weaning and reduced calorie diet (50 kcal/wk) after weaning
4. "R/R50"—reduced calorie diet of 50 kcal/wk both before and after weaning
5. "N/R50 lopro"—normal diet before weaning, restricted diet (50 kcal/wk) after weaning and
dietary protein content decreased with advancing age
6. "N/R40"—normal diet before weaning and reduced diet (40 Kcal/wk) after weaning.

case0501

15

Usage
case0501
Format
A data frame with 349 observations on the following 2 variables.
Lifetime the lifetime of the mice (in months)
Diet factor variable with six levels—"NP", "N/N85", "lopro", "N/R50", "R/R50" and "N/R40"
Source
Ramsey, F.L. and Schafer, D.W. (2013). The Statistical Sleuth: A Course in Methods of Data
Analysis (3rd ed), Cengage Learning.
References
Weindruch, R., Walford, R.L., Fligiel, S. and Guthrie D. (1986). The Retardation of Aging in
Mice by Dietary Restriction: Longevity, Cancer, Immunity and Lifetime Energy Intake, Journal of
Nutrition 116(4):641–54.
Examples
str(case0501)
attach(case0501)
# Re-order levels for better boxplot organization:
myDiet <- factor(Diet, levels=c("NP","N/N85","N/R50","R/R50","lopro","N/R40") )
myNames <- c("NP(49)","N/N85(57)","N/R50(71)","R/R50(56)","lopro(56)",
"N/R40(60)")
# Make these for boxplot labeling.
boxplot(Lifetime ~ myDiet, ylab= "Lifetime (months)", names=myNames,
xlab="Treatment (and sample size)")
myAov1
<- aov(Lifetime ~ Diet) # One-way analysis of variance
plot(myAov1, which=1) # Plot residuals versus estimated means.
summary(myAov1)
pairwise.t.test(Lifetime,Diet, pool.SD=TRUE, p.adj="none") # All t-tests
## p-VALUES AND CONFIDENCE INTERVALS FOR SPECIFIED COMPARISONS OF MEANS
if(require(multcomp)){
diet
<- factor(Diet,labels=c("NN85", "NR40", "NR50", "NP", "RR50", "lopro"))
myAov2 <- aov(Lifetime ~ diet - 1)
myComparisons <- glht(myAov2,
linfct=c("dietNR50 - dietNN85 = 0",
"dietRR50 - dietNR50 = 0",
"dietNR40 - dietNR50 = 0",
"dietlopro - dietNR50 = 0",
"dietNN85 - dietNP
= 0") )
summary(myComparisons,test=adjusted("none")) # No multiple comparison adjust.
confint(myComparisons, calpha = univariate_calpha()) # No adjustment
}
## EXAMPLE 5: BOXPLOTS FOR PRESENTATION
boxplot(Lifetime ~ myDiet, ylab= "Lifetime (months)", names=myNames,
main= "Lifetimes of Mice on 6 Diet Regimens",
xlab="Diet (and sample size)", col="green", boxlwd=2, medlwd=2, whisklty=1,

16

case0502
whisklwd=2, staplewex=.2, staplelwd=2, outlwd=2, outpch=21, outbg="green",
outcex=1.5)
detach(case0501)

case0502

The Spock Conspiracy Trial

Description
In 1968, Dr. Benjamin Spock was tried in Boston on charges of conspiring to violate the Selective
Service Act by encouraging young men to resist being drafted into military service for Vietnam.
The defence in the case challenged the method of jury selection claiming that women were underrepresented. Boston juries are selected in three stages. First 300 names are selected at random
from the City Directory, then a venire of 30 or more jurors is selected from the initial list of 300
and finally, an actual jury is selected from the venire in a nonrandom process allowing each side to
exclude certain jurors. There was one woman on the venire and no women on the final list. The defence argued that the judge in the trial had a history of venires in which women were systematically
underrepresented and compared the judge’s recent venires with the venires of six other Boston area
district judges.
Usage
case0502
Format
A data frame with 46 observations on the following 2 variables.
Percent is the percent of women on the venire’s of the Spock trial judge and 6 other Boston area
judges
Judge is a factor with levels "Spock's", "A", "B", "C", "D", "E" and "F"
Source
Ramsey, F.L. and Schafer, D.W. (2013). The Statistical Sleuth: A Course in Methods of Data
Analysis (3rd ed), Cengage Learning.
References
Zeisel, H. and Kalven, H. Jr. (1972). Parking Tickets and Missing Women: Statistics and the Law
in Tanur, J.M. et al. (eds.) Statistics: A Guide to the Unknown, Holden-Day.
Examples
str(case0502)
attach(case0502)
# Make new factor level names (with sample sizes) for boxplots
myNames <- c("A (5)", "B (6)", "C (9)", "D (2)", "E (6)", "F (9)", "Spock's (9)")
boxplot(Percent ~ Judge, ylab = "Percent of Women on Judges' Venires",

case0601

17

names = myNames, xlab = "Judge (and number of venires)",
main = "Percent Women on Venires of 7 Massachusetts Judges")
myAov1 <- aov(Percent ~ Judge)
plot(myAov1, which=1)
# Residual plot
summary(myAov1) # Initial screening. Any evidence of judge differences? (yes)
## ANALYSIS 1. TWO-SAMPLE t-TEST (ASSUMING NON-SPOCK JUDGES HAVE A COMMON MEAN)
SpockOrOther <- factor(ifelse(Judge=="Spock's","Spock","Other"))
aovFull
<- aov(Percent ~ Judge)
aovReduced
<- aov(Percent ~ SpockOrOther)
anova(aovReduced,aovFull) #Any evidence that 7 mean fits better than the 2 mean?
t.test(Percent ~ SpockOrOther, var.equal=TRUE) # Evidence that 2 means differ?
## ANALYSIS 2. COMPARE SPOCK MEAN TO AVERAGE OF OTHER MEANS
myAov3
<- aov(Percent ~ Judge - 1)
myContrast
<- rbind(c(1/6, 1/6, 1/6, 1/6, 1/6, 1/6, - 1))
if(require(multcomp)){ # use multcomp library
myComparison <- glht(myAov3, linfct=myContrast)
summary(myComparison, test=adjusted("none"))
confint(myComparison)
}
## BOXPLOTS FOR PRESENTATION
boxplot(Percent ~ Judge, ylab= "Percent of Women on Judges' Venires",
names=myNames, xlab="Judge (and number of venires)",
main= "Percent Women on Venires of 7 Massachusetts Judges",
col="green", boxlwd=2, medlwd=2, whisklty=1, whisklwd=2,
staplewex=.2, staplelwd=2, outlwd=2, outpch=21, outbg="green",
outcex=1.5)
detach(case0502)

case0601

Discrimination Against the Handicapped

Description
Study explores how physical handicaps affect people’s perception of employment qualifications.
Researchers prepared 5 videotaped job interviews using actors with a script designed to reflect an
interview with an applicant of average qualifications. The 5 tapes differed only in that the applicant
appeared with a different handicap in each one. Seventy undergraduate students were randomly
assigned to view the tapes and rate the qualification of the applicant on a 0-10 point scale.
Usage
case0601
Format
A data frame with 70 observations on the following 2 variables.
Score is the score each student gave to the applicant
Handicap is a factor variable with 5 levels—"None", "Amputee", "Crutches", "Hearing" and
"Wheelchair"

18

case0601

Source
Ramsey, F.L. and Schafer, D.W. (2013). The Statistical Sleuth: A Course in Methods of Data
Analysis (3rd ed), Cengage Learning.
References
Cesare, S.J., Tannenbaum, R.J. and Dalessio, A. (1990). Interviewers’ Decisions Related to Applicant Handicap Type and Rater Empathy, Human Performance 3(3): 157–171.
Examples
str(case0601)
attach(case0601)
## EXPLORATION
myHandicap <- factor(Handicap,
levels=c("None","Amputee","Crutches","Hearing","Wheelchair"))
boxplot(Score ~ myHandicap,
ylab= "Qualification Score Assigned by Student to Interviewee",
xlab= "Treatment Group--Handicap Portrayed (14 Students in each Group)",
main= "Handicap Discrimination Experiment on 70 Undergraduate Students")
myAov <- aov(Score ~ myHandicap)
plot(myAov, which=1) # Plot residuals versus estimated means
summary(myAov)
## COMPARE MEAN QUALIFICATION SCORE OF EVERY HANDICAP GROUP TO "NONE"
if(require(multcomp)){
# Use the multcomp library
myDunnett <- glht(myAov, linfct = mcp(myHandicap = "Dunnett"))
summary(myDunnett)
confint(myDunnett,level=.95)
opar <- par(no.readonly=TRUE) # Save current graphics parameter settings
par(mar=c(4.1,8.1,4.1,1.1)) # Change margins
plot(myDunnett,
xlab="Difference in Mean Qualification Score (and Dunnet-adjusted CIs)")
par(opar) # Restore original graphics parameter settings
}
## COMPARE EVERY MEAN TO EVERY OTHER MEAN
if(require(multcomp)){
# Use the multcomp library
myTukey
<- glht(myAov, linfct = mcp(myHandicap = "Tukey"))
summary(myTukey)
}
## TEST THE CONTRAST OF DISPLAY 6.4
myAov2
<- aov(Score ~ myHandicap - 1)
myContrast
<- rbind(c(0, -1/2, 1/2, -1/2, 1/2))
if(require(multcomp)){
# Use the multcomp library
myComparison <- glht(myAov2, linfct=myContrast)
summary(myComparison, test=adjusted("none"))
confint(myComparison)
}
# BOXPLOTS FOR PRESENTATION
boxplot(Score ~ myHandicap,
ylab= "Qualification Score Assigned by Student to Video Job Applicant",

case0602

19

xlab="Handicap Portrayed by Job Applicant in Video (14 Students in each Group)",
main= "Handicap Discrimination Experiment on 70 Undergraduate Students",
col="green", boxlwd=2, medlwd=2, whisklty=1, whisklwd=2, staplewex=.2,
staplelwd=2, outlwd=2, outpch=21, outbg="green", outcex=1.5)
detach(case0601)

case0602

Mate Preference of Platyfish

Description
Do female Platyfish prefer male Platyfish with yellow swordtails? A.L. Basolo proposed and tested
a selection model in which females have a pre-existing bias for a male trait even before the males
possess it. Six pairs of males were surgically given artificial, plastic swordtails—one pair received
a bright yellow sword, the other a transparent sword. Females were given the opportunity to engage
in courtship activity with either of the males. Of the total time spent by each female engaged in
courtship during a 20 minute observation period, the percentages of time spent with the yellowsword male were recorded.
Usage
case0602
Format
A data frame with 84 observations on the following 3 variables.
Percentage The percentage of courtship time spent by 84 females with the yellow-sword males
Pair Factor variable with 6 levels—"Pair1", "Pair2", "Pair3", "Pair4", "Pair5" and "Pair6"
Length Body size of the males
Source
Ramsey, F.L. and Schafer, D.W. (2013). The Statistical Sleuth: A Course in Methods of Data
Analysis (3rd ed), Cengage Learning.
References
Basolo, A.L. (1990). Female Preference Predates the Evolution of the Sword in Swordtail Fish,
Science 250: 808–810.
Examples
str(case0602)
attach(case0602)
## EXPLORATION
plot(Percentage ~ Length,
xlab="Length of the Two Males",
ylab="Percentage of Time Female Spent with Yellow-Sword Male",
main="Percentage of Time Spent with Yellow Rather than Transparent Sword Male")
abline(h=50)
# Draw a horizontal line at 50% (i.e. the "no preference" line)

20

case0701
myAov <- aov(Percentage ~ Pair)
plot(myAov, which=1) # Resdiual plot
summary(myAov)
# Explore possibility of linear effect, as in Display 6.5
myAov2
<- aov(Percentage ~ Pair - 1) # Show the estimated means.
myContrast
<- rbind(c(5, -3, 1, 3, -9, 3))
if(require(multcomp)){
# Use the multcomp library
myComparison <- glht(myAov2, linfct=myContrast)
summary(myComparison, test=adjusted("none"))
}
# Simpler exploration of linear effect, via regression (Ch. 7)
myLm
<- lm(Percentage ~ Length)
summary(myLm)
# ONE-SAMPLE t-TEST THAT MEAN PERCENTAGE = 50%, IGNORING MALE PAIR EFFECT
t.test(Percentage, mu=50, alternative="greater") # Get 1-sided p-value
t.test(Percentage, alternative="two.sided") # Get C.I.
## SCATTERPLOT FOR PRESENTATION
plot(Percentage ~ Length,
xlab="Length of the Two Males (mm)",
ylab="Percentage of Time Female Spent with Yellow-Sword Male",
main="Female Preference for Yellow Rather than Transparent Sword Male",
pch=21, lwd=2, bg="green", cex=1.5 )
abline(h=50,lty=2,col="blue",lwd=2)
text(29.5,52,"50% (no preference)", col="blue")
detach(case0602)

case0701

The Big Bang

Description
Hubble’s initial data on 24 nebulae outside the Milky Way.
Usage
case0701
Format
A data frame with 24 observations on the following 2 variables.
Velocity recession velocity (in kilometres per second)
Distance distance from earth (in magaparsec)
Source
Ramsey, F.L. and Schafer, D.W. (2013). The Statistical Sleuth: A Course in Methods of Data
Analysis (3rd ed), Cengage Learning.

case0702

21

References
Hubble, E. (1929). A Relation Between Distance and Radial Velocity Among Extragalactic Nebulae, Proceedings of the National Academy of Science 15: 168–173.
See Also
ex0725
Examples
str(case0701)
attach(case0701)
## EXPLORATION
plot(Distance ~ Velocity)
myLm <- lm(Distance ~ Velocity)
abline(myLm)
myResiduals <- myLm$res
myFits <- myLm$fit
plot(myResiduals ~ myFits)
# Plot residuals versus estimated means.
abline(h=0) # Draw a horizontal line at 0.
# OR, use this shortcut...
plot(myLm, which=1) # Residual plot (red curve is a scatterplot smooother)
## INFERENCE
summary(myLm)
confint(myLm,level=.95)
myLm2 <- lm(Distance ~ Velocity - 1)
summary(myLm2)
confint(myLm2)

# Drop the intercept.

## DISPLAY FOR PRESENTATION
plot(Distance ~ Velocity, xlab="Recession Velocity (km/sec)",
ylab="Distance from Earth (megaparsecs)",
main="Measured Distance Versus Velocity for 24 Extra-Galactic Nebulae",
pch=21, lwd=2, bg="green", cex=1.5 )
abline(myLm, lty=2, col="blue", lwd=2)
abline(myLm2, lty=3, col="red", lwd=2)
legend(-250,2.05,
c("unrestricted regression line","regression through the origin"),
lty=c(2,3), lwd=c(2,2), col=c("blue","red"))
detach(case0701)

case0702

Meat Processing and pH

Description
A certain kind of meat processing may begin once the pH in postmortem muscle of a steer carcass
has decreased sufficiently. To estimate the timepoint at which pH has dropped sufficiently, 10 steer
carcasses were assigned to be measured for pH at one of five times after slaughter.

22

case0702

Usage
case0702
Format
A data frame with 10 observations on the following 2 variables.
Time time after slaughter (hours)
pH pH level in postmortem muscle
Source
Ramsey, F.L. and Schafer, D.W. (2013). The Statistical Sleuth: A Course in Methods of Data
Analysis (3rd ed), Cengage Learning.
References
Schwenke, J.R. and Milliken, G.A. (1991). On the Calibration Problem Extended to Nonlinear
Models, Biometrics 47(2): 563–574.
See Also
ex0816
Examples
str(case0702)
attach(case0702)
# EXPLORATION
plot(pH ~ Time)
myLm <- lm(pH ~ Time)
abline(myLm, col="blue", lwd=2)
lines(lowess(Time,pH), col="red", lty=2, lwd=2) # Add scatterplot smoother
plot(myLm, which=1) # Residual plot
logTime <- log(Time)
plot(pH ~ logTime)
myLm2
<- lm(pH ~ logTime)
abline(myLm2)
plot(myLm2, which=1)
## PREDICTION BAND ABOUT REGRESSION LINE
xToPredict
<- seq(1,8,length=100) # sequence from 1 to 8 of length 100
logXToPredict <- log(xToPredict)
newData
<- data.frame(logTime = logXToPredict)
myPredict
<- predict(myLm2,newData,
interval="prediction", level=.90)
plot(pH ~ logTime)
abline(myLm2)
lines(myPredict[,3]~ logXToPredict, lty=2)
lines(myPredict[,2] ~ logXToPredict, lty=2)
# Find smallest time at which the upper endpoint of a 90% prediction
# interval is less than or equal to 6:
minTime <- min(xToPredict[myPredict[,3] <= 6.0])
minTime

case0801

23

abline(v=log(minTime),col="red")
# DISPLAY FOR PRESENTATION
plot(pH ~ Time, xlab="Time After Slaughter (Hours); log scale",
ylab="pH in Muscle", main="pH and Time after Slaughter for 10 Steers",
log="x", pch=21, lwd=2, bg="green", cex=2 )
lines(xToPredict,myPredict[,1], col="blue", lwd=2)
lines(xToPredict, myPredict[,3], lty=2, col="blue", lwd=2)
lines(xToPredict, myPredict[,2], lty=2, col="blue", lwd=2)
legend(3,7, c("Estimated Regression Line","90% Prediction Band"),
lty=c(1,2), col="blue", lwd=c(2,2))
abline(h=6, lty=3, col="purple", lwd=2)
text(1.5,6.05,"Desired pH", col="purple")
lines(c(minTime,minTime),c(5,6.15), col="purple", lwd=2)
text(minTime,6.2,"4.9 hours",col="purple",cex=1.25)
detach(case0702)

case0801

Island Area and Number of Species

Description
The data are the numbers of reptile and amphibian species and the island areas for seven islands in
the West Indies.
Usage
case0801
Format
A data frame with 7 observations on the following 2 variables.
Area area of island (in square miles)
Species number of reptile and amphibian species on island
Source
Ramsey, F.L. and Schafer, D.W. (2013). The Statistical Sleuth: A Course in Methods of Data
Analysis (3rd ed), Cengage Learning.
References
Wilson, E.O., 1992, The Diversity of Life, W. W. Norton, N.Y.
Examples
str(case0801)
attach(case0801)
## EXPLORATION
logSpecies <- log(Species)
logArea <- log(Area)

24

case0802
plot(logSpecies ~ logArea, xlab="Log of Island Area",
ylab="Log of Number of Species",
main="Number of Reptile and Amphibian Species on 7 Islands")
myLm <- lm(logSpecies ~ logArea)
abline(myLm)
## INFERENCE AND INTERPRETATION
summary(myLm)
slope
<- myLm$coef[2]
slopeConf <- confint(myLm,2)
100*(2^(slope)-1)
# Back-transform estimated slope
100*(2^(slopeConf)-1) # Back-transform confidence interval
# Interpretation: Associated with each doubling of island area is a 19% increase
# in the median number of bird species (95% CI: 16% to 21% increase).
## DISPLAY FOR PRESENTATION
plot(Species ~ Area, xlab="Island Area (Square Miles); Log Scale",
ylab="Number of Species; Log Scale",
main="Number of Reptile and Amphibian Species on 7 Islands",
log="xy", pch=21, lwd=2, bg="green",cex=2 )
dummyArea <- c(min(Area),max(Area))
beta <- myLm$coef
meanLogSpecies <- beta[1] + beta[2]*log(dummyArea)
medianSpecies <- exp(meanLogSpecies)
lines(medianSpecies ~ dummyArea,lwd=2,col="blue")
island <- c(" Cuba"," Hispaniola"," Jamaica", " Puerto Rico",
" Montserrat"," Saba"," Redonda")
for (i in 1:7) {
offset <- ifelse(Area[i] < 10000, -.2, 1.5)
text(Area[i],Species[i],island[i],col="dark green",adj=offset,cex=.75) }
detach(case0801)

case0802

Breakdown Times for Insulating Fluid under different Voltage

Description
In an industrial laboratory, under uniform conditions, batches of electrical insulating fluid were
subjected to constant voltages until the insulating property of the fluids broke down. Seven different
voltage levels were studied and the measured reponses were the times until breakdown.
Usage
case0802
Format
A data frame with 76 observations on the following 3 variables.
Time times until breakdown (in minutes)
Voltage voltage applied (in kV)
Group factor variable (group number)

case0901

25

Source
Ramsey, F.L. and Schafer, D.W. (2013). The Statistical Sleuth: A Course in Methods of Data
Analysis (3rd ed), Cengage Learning.
References
Nelson, W.B., 1970, G.E. Co. Technical Report 71-C-011, Schenectady, N.Y.
Examples
str(case0802)
attach(case0802)
## EXPLORATION
plot(Time ~ Voltage)
myLm <- lm(Time ~ Voltage)
plot(myLm, which=1)
# Residual plot
logTime <- log(Time)
plot(logTime ~ Voltage)
myLm <- lm(logTime ~ Voltage)
abline(myLm)
plot(myLm,which=1) # Residual plot
myOneWay <- lm(logTime ~ factor(Voltage))
anova(myLm, myOneWay) # Lack of fit test for simple regression (seems okay)
## INFERENCE AND INTERPREATION
beta <- myLm$coef
100*(1 - exp(beta[2]))
# Back-transform estimated slope
100*(1 - exp(confint(myLm,"Voltage")))
# Interpretation: Associated with each 1 kV increase in voltage is a 39.8%
# decrease in median breakdown time (95% CI: 32.5% decrease to 46.3% decrease).
## DISPLAY FOR PRESENTATION
options(scipen=50) # Do this to avoid scientific notation on y-axis
plot(Time ~ Voltage, log="y", xlab="Voltage (kV)",
ylab="Breakdown Time (min.); Log Scale",
main="Breakdown Time of Insulating Fluid as a Function of Voltage Applied",
pch=21, lwd=2, bg="green", cex=1.75 )
dummyVoltage <- c(min(Voltage),max(Voltage))
meanLogTime <- beta[1] + beta[2]*dummyVoltage
medianTime <- exp(meanLogTime)
lines(medianTime ~ dummyVoltage, lwd=2, col="blue")
detach(case0802)

case0901

Effects of Light on Meadowfoam Flowering

Description
Meadowfoam is a small plant found growing in moist meadows of the US Pacific Northwest. Researchers reported the results from one study in a series designed to find out how to elevate meadowfoam production to a profitable crop. In a controlled growth chamber, they focused on the effects
of two light–related factors: light intensity and the timeing of the onset of the ligth treatment.

26

case0902

Usage
case0901
Format
A data frame with 24 observations on the following 3 variables.
Flowers average number of flowers per meadowfoam plant
Time time light intensity regiments started; 1=Late, 2=Early
Intensity light intensity (in µmol/m2 /sec)
Source
Ramsey, F.L. and Schafer, D.W. (2013). The Statistical Sleuth: A Course in Methods of Data
Analysis (3rd ed), Cengage Learning.
Examples
str(case0901)
attach(case0901)
## EXPLORATION
plot(Flowers ~ Intensity, pch=ifelse(Time ==1, 19, 21))
myLm <- lm(Flowers ~ Intensity + factor(Time) + Intensity:factor(Time))
plot(myLm, which=1)
summary(myLm) # Note p-value for interaction term
# INFERENCE
myLm2 <- lm(Flowers ~ Intensity + factor(Time))
summary(myLm2)
confint(myLm2)
# DISPLAY FOR PRESENTATION
plot(Flowers ~ jitter(Intensity,.3),
xlab=expression("Light Intensity ("*mu*"mol/"*m^2*"/sec)"), # Include symbols
ylab="Average Number of Flowers per Plant",
main="Effect of Light Intensity and Timing on Meadowfoam Flowering",
pch=ifelse(Time ==1, 21, 22), bg=ifelse(Time==1, "orange","green"),
cex=1.7, lwd=2)
beta <- myLm2$coef
abline(beta[1],beta[2],lwd=2, lty=2)
abline(beta[1]+beta[3],beta[2],lwd=2,lty=3)
legend(700,79,c("Early Start","Late Start"),
pch=c(22,21),lwd=2,pt.bg=c("green","orange"),pt.cex=1.7,lty=c(3,2))
detach(case0901)

case0902

Why Do Some Mammals Have Large Brains for Their Size?

Description
The data are the average values of brain weight, body weight, gestation lengths (length of pregnancy) and litter size for 96 species of mammals.

case0902

27

Usage
case0902
Format
A data frame with 96 observations on the following 5 variables.
Species species
Brain average brain weight (in grams)
Body average body weight (in kilograms)
Gestation gestation period (in days)
Litter average litter size
Source
Ramsey, F.L. and Schafer, D.W. (2013). The Statistical Sleuth: A Course in Methods of Data
Analysis (3rd ed), Cengage Learning.
See Also
ex0333
Examples
str(case0902)
attach(case0902)
## EXPLORATION
myMatrix
<- cbind(Brain, Body, Litter, Gestation)
if(require(car)){
# Use the car library
scatterplotMatrix(myMatrix,
# Matrix of scatterplots
smooth=FALSE,
# Omit scatterplot smoother on plots
diagonal="histogram") # Draw histograms on diagonals
}
myLm <- lm(Brain ~ Body + Litter + Gestation)
plot(myLm, which=1)
logBrain <- log(Brain)
logBody <- log(Body)
logGestation <- log(Gestation)
myMatrix2 <- cbind(logBrain,logBody,Litter, logGestation)
if(require(car)){
# Use the car library
scatterplotMatrix(myMatrix2, smooth=FALSE, diagonal="histogram")
}
myLm2 <- lm(logBrain ~ logBody + Litter + logGestation)
plot(myLm2,which=1) # Residual plot.
if(require(car)){
# Use the car library
crPlots(myLm2) # Partial residual plots (Sleuth Ch.11)
}
plot(logBrain ~ logBody)
identify(logBrain ~ logBody,labels=Species)
# Identify points on
# Place the cursor over a point of interest, then left-click.
# Continue with other points if desired. When finished, pres Esc.
## INFERENCE

scatterplot

28

case1001
summary(myLm2)
confint(myLm2)
# DISPLAYS FOR PRESENTATION
myLm3 <- lm(logBrain ~ logBody + logGestation)
beta <- myLm3$coef
logBrainAdjusted <- logBrain - beta[2]*logBody
y <- exp(logBrainAdjusted)
ymod <- 100*y/median(y)
plot(ymod ~ Gestation, log="xy",
xlab="Average Gestation Length (Days); Log Scale",
ylab="Brain Weight Adjusted for Body Weight, as a Percentage of the Median",
main="Brain Weight Adjusted for Body Weight, Versus Gestation Length, for 96 Mammal Species",
pch=21,bg="green",cex=1.3)
identify(ymod ~ Gestation,labels=Species, cex=.7) # Identify points, as desired
# Press Esc to complete identify.
abline(h=100,lty=2) # Draw horizontal line at 100%
myLm4 <- lm(logBrain ~ logBody + Litter)
beta <- myLm4$coef
logBrainAdjusted <- logBrain - beta[2]*logBody
y2 <- exp(logBrainAdjusted)
y2mod <- 100*y2/median(y2)
plot(y2mod ~ Litter, log="y", xlab="Average Litter Size",
ylab="Brain Weight Adjusted for Body Weight, as a Percentage of the Median",
main="Brain Weight Adjusted for Body Weight, Versus Litter Size, for 96 Mammal Species",
pch=21,bg="green",cex=1.3)
identify(y2mod ~ Litter,labels=Species, cex=.7)
abline(h=100,lty=2)
detach(case0902)

case1001

Galileo’s Data on the Motion of Falling Bodies

Description
In 1609 Galileo proved mathematically that the trajectory of a body falling with a horizontal velocity
component is a parabola. His search for an experimental setting in which horizontal motion was
not affected appreciably (to study inertia) let him to construct a certain apparatus. The data comes
from one of his experiments.
Usage
case1001
Format
A data frame with 7 observations on the following 2 variables.
Distance horizontal distances (in punti)
Height initial height (in punti)

case1002

29

Source
Ramsey, F.L. and Schafer, D.W. (2013). The Statistical Sleuth: A Course in Methods of Data
Analysis (3rd ed), Cengage Learning.
Examples
str(case1001)
attach(case1001)
## EXPLORATION
plot(Distance ~ Height)
myLm <- lm(Distance ~ Height)
plot(myLm, which=1)
height2 <- Height^2
myLm2 <- lm(Distance ~ Height + height2)
plot(myLm2, which=1)
summary(myLm2) # Note p-value for quadratic term (it's small)
height3 <- Height^3
myLm3 <- update(myLm2, ~ . + height3)
plot(myLm3,which=1)
summary(myLm3) # Note p-value for cubic term (it's small)
height4 <- Height^4
myLm4 <- update(myLm3, ~ . + height4)
summary(myLm4) # Note p-value for quartic term (it's not small)
## DISPLAY FOR PRESENTATION
plot(Distance ~ Height, xlab="Initial Height (Punti)",
ylab="Horizontal Distance Traveled (Punti)",
main="Galileo's Falling Body Experiment",
pch=21, bg="green", lwd=2, cex=2)
dummyHeight
<- seq(min(Height),max(Height),length=100)
betaQ
<- myLm2$coef
quadraticCurve <- betaQ[1] + betaQ[2]*dummyHeight + betaQ[3]*dummyHeight^2
lines(quadraticCurve ~ dummyHeight,col="blue",lwd=3)
betaC
<- myLm3$coef # coefficients of cubic model
cubicCurve
<- betaC[1] + betaC[2]*dummyHeight + betaC[3]*dummyHeight^2 +
betaC[4]*dummyHeight^3
lines(cubicCurve ~ dummyHeight,lty=3,col="red",lwd=3)
legend(590,290,legend=c(expression("Quadratic Fit "*R^2*" = 99.0%"),
expression("Cubic Fit
"*R^2*" = 99.9%")),
lty=c(1,3),col=c("blue","red"), lwd=c(3,3))
detach(case1001)

case1002

The Energy Costs of Echolocation by Bats

Description
The data are on in–flight energy expenditure and body mass from 20 energy studies on three types
of flying vertebrates: echolocating bats, non–echolocating bats and non–echolocating birds.
Usage
case1002

30

case1002

Format
A data frame with 20 observations on the following 3 variables.
Mass mass (in grams)
Type a factor with 3 levels indicating the type of flying vertebrate: non-echolocating bats, nonecholocating birds, echolocating bats
Energy in–flight energy expenditure (in W)
Source
Ramsey, F.L. and Schafer, D.W. (2013). The Statistical Sleuth: A Course in Methods of Data
Analysis (3rd ed), Cengage Learning.
References
Speakman, J.R. and Racey, P.A. (1991). No cost of Echolocation for Bats in Flight, Nature 350:
421–423.
Examples
str(case1002)
attach(case1002)
## EXPLORATION
plot(Energy~Mass, case1002, log="xy", xlab = "Body Mass (g) (log scale)",
ylab = "Energy Expenditure (W) (log scale)",
pch = ifelse(Type=="echolocating bats", 19,
ifelse(Type=="non-echolocating birds", 21, 24)))
legend(7, 50, pch=c(24, 21, 19),
c("Non-echolocating bats", "Non-echolocating birds","Echolocating bats"))
logEnergy <- log(Energy)
logMass <- log(Mass)
myLm2 <- lm(logEnergy ~ logMass + Type + logMass:Type)
plot(myLm2, which=1)
myLm3 <- update(myLm2, ~ . - logMass:Type)
anova(myLm3, myLm2)
# Test for interaction with extra ss F-test
## INFERENCE AND INTERPRETATION
myLm4 <- update(myLm3, ~ . - Type) # Reduced model...with no effect of Type
anova(myLm4, myLm3)
# Test for Type effect
myType <- factor(Type,
levels=c("non-echolocating bats","echolocating bats","non-echolocating birds"))
myLm3a <- lm(logEnergy ~ logMass + myType)
summary(myLm3a)
100*(exp(myLm3a$coef[3]) - 1)
100*(exp(confint(myLm3a,3))-1)
# Conclusion: Adjusted for body mass, the median energy expenditure for
# echo-locating bats exceeds that for echo-locating bats by an estimated
# 8.2% (95% confidence interval: 29.6% LESS to 66.3% MORE)
# DISPLAY FOR
myPlotCode
myPointColor
plot(Energy ~

PRESENTATION
<- ifelse(Type=="non-echolocating birds",24,21)
<- ifelse(Type=="echolocating bats","green","white")
Mass, log="xy", xlab="Body Mass (g); Log Scale ",

case1101

31

ylab="In-Flight Energy Expenditure (W); Log Scale",
main="In-Flight Energy Expenditure Study",
pch=myPlotCode,bg=myPointColor,lwd=2, cex=1.5)
dummyMass <- seq(5,800,length=50)
beta
<- myLm3$coef
curve1
<- exp(beta[1] + beta[2]*log(dummyMass))
curve2
<- exp(beta[1] + beta[2]*log(dummyMass) + beta[3])
curve3
<- exp(beta[1] + beta[2]*log(dummyMass) + beta[4])
lines(curve1 ~ dummyMass)
lines(curve2 ~ dummyMass, lty=2)
lines(curve3 ~ dummyMass, lty=3)
legend(100,3,
c("Echolocating Bats","Non-Echolocating Bats","Non-Echolocating Birds"),
pch=c(21,21,24),lwd=2,pt.cex=c(1.5,1.5,1.5),pt.lwd=c(2,2,2),
pt.bg=c("green","white","white"),lty=c(1,2,3))
detach(case1002)

case1101

Alcohol Metabolism in Men and Women

Description
These data were collected on 18 women and 14 men to investigate a certain theory on why women
exhibit a lower tolerance for alcohol and develop alcohol–related liver disease more readily than
men.
Usage
case1101
Format
A data frame with 32 observations on the following 5 variables.
Subject subject number in the study
Metabol first–pass metabolism of alcohol in the stomach (in mmol/liter-hour)
Gastric gastric alcohol dehydrogenase activity in the stomach (in µmol/min/g of tissue)
Sex sex of the subject
Alcohol whether the subject is alcoholic or not
Source
Ramsey, F.L. and Schafer, D.W. (2013). The Statistical Sleuth: A Course in Methods of Data
Analysis (3rd ed), Cengage Learning.

32

case1101

Examples
str(case1101)
attach(case1101)
## EXPLORATION
library(lattice)
xyplot(Metabol~Gastric|Sex*Alcohol, case1101)
myPch <- ifelse(Sex=="Female",24,21)
myBg <- ifelse(Alcohol=="Alcoholic","gray","white")
plot(Metabol~Gastric, pch=myPch,bg=myBg,cex=1.5)
legend(1,12, pch=c(24,24,21,21), pt.cex=c(1.5,1.5,1.5,1.5),
pt.bg=c("white","gray", "white", "gray"),
c("Non-alcoholic Females", "Alcoholic Females",
"Non-alcoholic Males", "Alcoholic Males"))
identify(Metabol ~ Gastric)
# Left click on outliers to show case number; Esc when finished.
myLm1 <- lm(Metabol ~ Gastric + Sex + Gastric:Sex)
plot(myLm1, which=1)
plot(myLm1, which=4) # Show Cook's Distance; note cases 31 and 32.
plot(myLm1, which=5) # Note leverage and studentized residual for cases 31 and 32.
subject <- 1:32 # Create ID number from 1 to 32
# Refit model without cases 31 and 32:
myLm2 <- update(myLm1, ~ ., subset = (subject !=31 & subject !=32))
plot(myLm2,which=1)
plot(myLm2,which=4)
plot(myLm2,which=5)
summary(myLm1)
summary(myLm2) # Significance of interaction terms hinges on cases 31 and 32.
myLm3 <- update(myLm2, ~ . - Gastric:Sex) #Drop interaction (without 31,32).
summary(myLm3)
if(require(car)){
# Use the car library
crPlots(myLm3) # Show partial residual (component + residual) plots.
}
## INFERENCE AND INTERPRETATION
summary(myLm3)
confint(myLm3,2:3)
## DISPLAY FOR PRESENTATION
myCol <- ifelse(Sex=="Male","blue","red")
plot(Metabol ~ Gastric,
xlab=expression("Gastric Alcohol Dehydrogenase Activity in Stomach ("*mu*"mol/min/g of Tissue)"),
ylab="First-pass Metabolism in the Stomach (mmol/liter-hour)",
main="First-Pass Alcohol Metabolism and Enzyme Activity for 18 Females and 14 Males",
pch=myPch, bg=myBg,cex=1.75, col=myCol, lwd=1)
legend(0.8,12.2, c("Females", "Males"), lty=c(1,2),
pch=c(24,21), pt.cex=c(1.75,1.75), col=c("red", "blue"))
dummyGastric <- seq(min(Gastric),3,length=100)
beta <- myLm3$coef
curveF <- beta[1] + beta[2]*dummyGastric
curveM <- beta[1] + beta[2]*dummyGastric + beta[3]
lines(curveF ~ dummyGastric, col="red")

case1102

33

lines(curveM ~ dummyGastric, col="blue",lty=2)
text(.8,10,"gray indicates alcoholic",cex = .8, adj=0)
detach(case1101)

case1102

The Blood–Brain Barrier

Description
The human brain is protected from bacteria and toxins, which course through the blood–stream, by
a single layer of cells called the blood–brain barrier. These data come from an experiment (on rats,
which process a similar barrier) to study a method of disrupting the barrier by infusing a solution of
concentrated sugars.

Usage
case1102
Format
A data frame with 34 observations on the following 9 variables.
Brain Brain tumor count (per gm)
Liver Liver count (per gm)
Time Sacrifice time (in hours)
Treatment Treatment received
Days Days post inoculation
Sex Sex of the rat
Weight Initial weight (in grams)
Loss Weight loss (in grams)
Tumor Tumor weight (in 10−4 grams)

Source
Ramsey, F.L. and Schafer, D.W. (2013). The Statistical Sleuth: A Course in Methods of Data
Analysis (3rd ed), Cengage Learning.

See Also
ex1416, ex1417

34

case1102

Examples
str(case1102)
attach(case1102)
## EXPLORATION
logRatio <- log(Brain/Liver)
logTime <- log(Time)
myMatrix <- cbind (logRatio, Days, Weight, Loss, Tumor, logTime)
if(require(car)){
# Use the car library
scatterplotMatrix(myMatrix,groups=Treatment,
smooth=FALSE, diagonal="histogram", col=c("green","blue"), pch=c(16,17), cex=1.5)
}
myLm1 <- lm(logRatio ~ Treatment + logTime + Days + Sex + Weight + Loss + Tumor)
plot(myLm1, which=1)
if(require(car)){
# Use the car library
crPlots(myLm1) # Draw partial resdual plots.
}
myLm2
<- lm(logRatio ~ Treatment + factor(Time) +
Days + Sex + Weight + Loss + Tumor) # Include Time as a factor.
anova(myLm1,myLm2)
if(require(car)){
# Use the car library
crPlots(myLm2) # Draw partial resdual plots.
}
summary(myLm2) # Use backard elimination
myLm3 <- update(myLm2, ~ . - Days)
summary(myLm3)
myLm4 <- update(myLm3, ~ . - Sex)
summary(myLm4)
myLm5 <- update(myLm4, ~ . - Weight)
summary(myLm5)
myLm6 <- update(myLm5, ~ . - Tumor)
summary(myLm6)
myLm7 <- update(myLm6, ~ . - Loss)
summary(myLm7)
# Final model for inference
## INFERENCE AND INTERPRETATION
myTreatment <- factor(Treatment,levels=c("NS","BD")) # Change level ordering
myLm7a <- lm(logRatio ~ factor(Time) + myTreatment)
summary(myLm7a)
beta <- myLm7a$coef
exp(beta[5])
exp(confint(myLm7a,5))
# Interpetation: The median ratio of brain to liver tumor counts for barrier# disrupted rats is estimated to be 2.2 times the median ratio for control rats
# (95% CI: 1.5 times to 3.2 times as large).
## DISPLAY FOR PRESENTATION
ratio <- Brain/Liver
jTime <- exp(jitter(logTime,.2)) # Back-transform a jittered version of logTime
plot(ratio ~ jTime, log="xy",
xlab="Sacrifice Time (Hours), jittered; Log Scale",
ylab="Effectiveness: Brain Tumor Count Relative To Liver Tumor Count; Log Scale",

case1201

35

main="Blood Brain Barrier Disruption Effectiveness in 34 Rats",
pch= ifelse(Treatment=="BD",21,24), bg=ifelse(Treatment=="BD","green","orange"),
lwd=2, cex=2)
dummyTime
<- c(0.5, 3, 24, 72)
controlTerm
<- beta[1] + beta[2]*(dummyTime==3) +
beta[3]*(dummyTime==24) + beta[4]*(dummyTime==72)
controlCurve <- exp(controlTerm)
lines(controlCurve ~ dummyTime, lty=1,lwd=2)
BDTerm
<- controlTerm + beta[5]
BDCurve
<- exp(BDTerm)
lines(BDCurve ~ dummyTime,lty=2,lwd=2)
legend(0.5,10,c("Barrier disruption","Saline control"),pch=c(21,22),
pt.bg=c("green","orange"),pt.lwd=c(2,2),pt.cex=c(2,2), lty=c(2,1),lwd=c(2,2))
detach(case1102)

case1201

State Average SAT Scores

Description
Data on the average SAT scores for US states in 1982 and possible associated factors.
Usage
case1201
Format
A data frame with 50 observations on the following 8 variables.
State US state
SAT state averages of the total SAT (verbal + quantitative) scores
Takers the percentage of the total eligible students (high school seniors) in the state who took the
exam
Income the median income of families of test–takers (in hundreds of dollars)
Years the average number of years that the test–takers had formal studies in social sciences, natural
sciences and humanities
Public the percentage of the test–takers who attended public secondary schools
Expend the total state expenditure on secondary schools (in hundreds of dollars per student)
Rank the median percentile ranking of the test–takers within their secondary school classes
Source
Ramsey, F.L. and Schafer, D.W. (2013). The Statistical Sleuth: A Course in Methods of Data
Analysis (3rd ed), Cengage Learning.

36

case1201

Examples
str(case1201)
attach(case1201)
## EXPLORATION
logTakers <- log(Takers)
myMatrix
<- cbind(SAT, logTakers,Income, Years, Public, Expend, Rank)
if(require(car)){
# Use the car library
scatterplotMatrix(myMatrix, diagonal="histogram", smooth=FALSE)
}
State[Public < 50] # Identify state with low Public (Louisiana)
State[Expend > 40] # Alaska
myLm1
<- lm(SAT ~ logTakers + Income+ Years + Public + Expend + Rank)
plot(myLm1,which=1)
plot(myLm1,which=4) # Cook's Distance
State[29] # Identify State number 29? ([1] Alaska)
plot(myLm1,which=5)
if(require(car)){
# Use the car library
crPlots(myLm1) # Partial residual plot
}
myLm2 <- update(myLm1, ~ . ,subset=(State != "Alaska"))
plot(myLm2,which=1)
plot(myLm2,which=4)
if(require(car)){
# Use the car library
crPlots(myLm2) # Partial residual plot
}
## RANK STATES ON SAT SCORES, ADJUSTED FOR Takers AND Rank
myLm3
<- lm(SAT ~ logTakers + Rank)
myResiduals <- myLm3$res
myOrder
<- order(myResiduals)
State[myOrder]
## DISPLAY FOR PRESENTATION
dotchart(myResiduals[myOrder], labels=State[myOrder],
xlab="SAT Scores, Adjusted for Percent Takers and HS Ranks (Deviation From Average)",
main="States Ranked by Adjusted SAT Scores",
bg="green", cex=.8)
abline(v=0, col="gray")
## VARIABLE SELECTION (FOR RANKING STATES AFTER ACCOUNTING FOR ALL VARIABLES)
expendSquared <- Expend^2
if(require(leaps)){
# Use the leaps library
mySubsets
<- regsubsets(SAT ~ logTakers + Income+ Years + Public + Expend +
Rank + expendSquared, nvmax=8, data=case1201, subset=(State != "Alaska"))
mySummary <- summary(mySubsets)
p <- apply(mySummary$which, 1, sum)
plot(p, mySummary$bic, ylab = "BIC")
cbind(p,mySummary$bic)
mySummary$which[4,]
myLm4 <- lm(SAT ~ logTakers + Years + Expend + Rank, subset=(State != "Alaska"))
summary(myLm4)
## DISPLAY FOR PRESENTATION
myResiduals2 <- myLm4$res
myOrder2 <- order(myResiduals2)
newState <- State[State != "Alaska"]

case1202

}

37

newState[myOrder2]
dotchart(myResiduals2[myOrder2], labels=State[myOrder2],
xlab="Adjusted SAT Scores (Deviation From Average Adjusted Value)",
main=paste("States Ranked by SAT Scores Adjusted for Demographics",
"of Takers and Education Expenditure", sep = " "),
bg="green", cex = .8)
abline(v=0, col="gray")

detach(case1201)

case1202

Sex discrimination in Employment

Description
Data on employees from one job category (skilled, entry–level clerical) of a bank that was sued for
sex discrimination. The data are on 32 male and 61 female employees, hired between 1965 and
1975.
Usage
case1202
Format
A data frame with 93 observations on the following 7 variables.
Bsal Annual salary at time of hire
Sal77 Salary as of March 1975
Sex Sex of employee
Senior Seniority (months since first hired)
Age Age of employee (in months)
Educ Education (in years)
Exper Work experience prior to employment with the bank (months)
Source
Ramsey, F.L. and Schafer, D.W. (2013). The Statistical Sleuth: A Course in Methods of Data
Analysis (3rd ed), Cengage Learning.
References
Roberts, H.V. (1979). Harris Trust and Savings Bank: An Analysis of Employee Compensation,
Report 7946, Center for Mathematical Studies in Business and Economics, University of Chicago
Graduate School of Business.
See Also
case0102

38

case1202

Examples
str(case1202)
attach(case1202)
## EXPLORATION
logSal <- log(Bsal)
myMatrix <- cbind (logSal, Senior,Age, Educ, Exper)
if(require(car)){
# Use the car library
scatterplotMatrix(myMatrix, smooth=FALSE, diagonal="histogram",
groups=Sex, col=c("red","blue") )
}
myLm1 <- lm(logSal ~ Senior + Age + Educ + Exper + Sex)
plot(myLm1, which=1)
plot(myLm1, which=4) # Cook's Distance
if(require(car)){
# Use the car library
crPlots(myLm1)
# Partial residual plots
}
ageSquared
<- Age^2
ageCubed
<- Age^3
experSquared <- Exper^2
experCubed
<- Exper^3
myLm2 <- lm(logSal ~ Senior + Age + ageSquared + ageCubed +
Educ + Exper + experSquared + experCubed + Sex)
plot(myLm2, which=1) # Residual plot
plot(myLm1, which=4) # Cook's distance
if(require(leaps)){
# Use the leaps library
mySubsets
<- regsubsets(logSal ~ (Senior + Age + Educ + Exper +
ageSquared + experSquared)^2, nvmax=25, data=case1202)
mySummary <- summary(mySubsets)
p <- apply(mySummary$which, 1, sum)
plot(mySummary$bic ~ p, ylab = "BIC")
cbind(p,mySummary$bic)
mySummary$which[8,] # Note that Age:ageSquared = ageCubed
}
myLm3
<- lm(logSal ~ Age + Educ + ageSquared + Senior:Educ +
Age:Exper + ageCubed + Educ:Exper + Exper:ageSquared)
summary(myLm3)
myLm4 <- update(myLm3, ~ . + Sex)
summary(myLm4)
myLm5 <- update(myLm4, ~ . + Sex:Age + Sex:Educ + Sex:Senior +
Sex:Exper + Sex:ageSquared)
anova(myLm4, myLm5)
## INFERENCE AND INTERPRETATION
summary(myLm4)
beta
<- myLm4$coef
exp(beta[6])
exp(confint(myLm4,6))
# Conclusion: The median beginning salary for males was estimated to be 12%
# higher than the median salary for females with similar values of the available
# qualification variables (95% confidence interval: 7% to 17% higher).
## DISPLAY FOR PRESENTATION
years <- Exper/12 # Change months to years

case1301

39

plot(Bsal ~ years, log="y", xlab="Previous Work Experience (Years)",
ylab="Beginning Salary (Dollars); Log Scale",
main="Beginning Salaries and Experience for 61 Female and 32 Male Employees",
pch= ifelse(Sex=="Male",24,21), bg = "gray",
col= ifelse(Sex=="Male","blue","red"), lwd=2, cex=1.8 )
myLm6 <- lm(logSal ~ Exper + experSquared + experCubed + Sex)
beta <- myLm6$coef
dummyExper <- seq(min(Exper),max(Exper),length=50)
curveF <- beta[1] + beta[2]*dummyExper + beta[3]*dummyExper^2 +
beta[4]*dummyExper^3
curveM <- curveF + beta[5]
dummyYears <- dummyExper/12
lines(exp(curveF) ~ dummyYears, lty=1, lwd=2,col="red")
lines(exp(curveM) ~ dummyYears, lty = 2, lwd=2, col="blue")
legend(28,8150, c("Male","Female"),pch=c(24,21), pt.cex=1.8, pt.lwd=2,
pt.bg=c("gray","gray"), col=c("blue","red"), lty=c(2,1), lwd=2)
detach(case1202)

case1301

Seaweed Grazers

Description
To study the influence of ocean grazers on regeneration rates of seaweed in the intertidal zone, a
researcher scraped rock plots free of seaweed and observed the degree of regeneration when certain
types of seaweed-grazing animals were denied access. The grazers were limpets (L), small fishes (f)
and large fishes (F). Each plot received one of six treatments named by which grazers were allowed
access. In addition, the researcher applied the treatments in eight blocks of 12 plots each. Within
each block she randomly assigned treatments to plots. The blocks covered a wide range of tidal
conditions.
Usage
case1301
Format
A data frame with 96 observations on the following 3 variables.
Cover percent of regenerated seaweed cover
Block a factor with levels "B1", "B2", "B3", "B4", "B5", "B6", "B7" and "B8"
Treat a factor indicating treatment, with levels "C", "f", "fF", "L", "Lf" and "LfF"
Source
Ramsey, F.L. and Schafer, D.W. (2013). The Statistical Sleuth: A Course in Methods of Data
Analysis (3rd ed), Cengage Learning.
References
Olson, A. (1993). Evolutionary and Ecological Interactions Affecting Seaweeds, Ph.D. Thesis.
Oregon State University.

40

case1302

Examples
str(case1301)
attach(case1301)
## EXPLORATION AND MODEL DEVELOPMENT
plot(Cover ~ Treat,xlab="Animals Present",ylab="Remaining Seaweed Coverage (%)")
myLm1 <- lm(Cover ~ Block + Treat + Block:Treat)
plot(myLm1,which=1)
ratio <- Cover/(100 - Cover)
logRatio <- log(ratio)
myLm2 <- lm(logRatio ~ Block + Treat + Block:Treat)
plot(myLm2, which=1)
myLm3 <- lm(logRatio ~ Block + Treat)
anova(myLm3, myLm2) # Test for interaction with extra ss F-test
if(require(car)){
# Use the car library
crPlots(myLm3)
# Partial residual plots
myLm4 <- lm(logRatio ~ Treat)
anova(myLm4, myLm3)
# Test for Block effect
myLm5 <- lm(logRatio ~ Block)
anova(myLm5, myLm3)
# Test for Treatment effect
lmp <- factor(ifelse(Treat %in% c("L", "Lf", "LfF"), "yes", "no"))
sml <- factor(ifelse(Treat %in% c("f", "fF", "Lf", "LfF"), "yes","no"))
big <- factor(ifelse(Treat %in% c("fF", "LfF"), "yes","no"))
myLm6 <- lm(logRatio ~ Block + lmp + sml + big)
crPlots(myLm6)
myLm7 <- lm(logRatio ~ Block + (lmp + sml + big)^2)
anova(myLm6, myLm7) # Test for interactions of lmp, sml, and big
## INFERENCE AND INTERPRETATION
summary(myLm6)
# Get p-values for lmp, sml, and big effects; R^2 = .8522
beta <- myLm6$coef
exp(beta[9:11])
exp(confint(myLm6,9:11) )
myLm7 <- update(myLm6, ~ . - lmp)
summary(myLm7) # R^2 = .4568; Note .8522-.4580 = 39.54# (explained by limpets)
myLm8 <- update(myLm6, ~ . - big)
summary(myLm8) # R^2 = .8225; Note .8522-.8255= 2.97# (explained by big fish)
myLm9 <- update(myLm6, ~ . - sml)
summary(myLm9) # R^2: .8400; Note .8522-.8400 = 1.22# (explained by small fish)
## DISPLAY FOR PRESENTATION
myYLab <- "Adjusted Seaweed Regeneration (Log Scale; Deviation from Average)"
crPlots(myLm6, ylab=myYLab, ylim=c(-2.2,2.2),
main="Effects of Blocks and Treatments on Log Regeneration Ratio, Adjusted for Other Factors")
}
detach(case1301)

case1302

Pygmalion Effect

case1302

41

Description
One company of soldiers in each of 10 platoons was assigned to a Pygmalion treatment group, with
remaining companies in the platoon assigned to a control group. Leaders of the Pygmalion platoons were told their soldiers had done particularly well on a battery of tests which were, in fact,
non-existent. In this randomised block experiment, platoons are experimental units, companies are
blocks, and average Practical Specialty test score for soldiers in a platoon is the response. The researchers wished to see if the platoon response was affected by the artificially-induced expectations
of the platoon leader.
Usage
case1302
Format
A data frame with 29 observations on the following 3 variables.
Company a factor indicating company identification, with levels "C1", "C2", . . . , "C10"
Treat a factor indicating treatment with two levels, "Pygmalion" and "Control"
Score average score on practical specialty test of all soldiers in the platoon
Source
Ramsey, F.L. and Schafer, D.W. (2013). The Statistical Sleuth: A Course in Methods of Data
Analysis (3rd ed), Cengage Learning.
References
Eden, D. (1990). Pygmalion Without Interpersonal Contrast Effects: Whole Groups Gain from
Raising Manager Expectations, Journal of Applied Psychology 75(4): 395–398.
Examples
str(case1302)
attach(case1302)
## EXPLORATION AND MODEL DEVELOPMENT
plot(Score ~ as.numeric(Company),cex=1.5, pch=21,
bg=ifelse(Treat=="Pygmalion","blue","light gray"))
myLm1
<- lm(Score ~ Company + Treat + Company:Treat) # Fit with interaction.
plot(myLm1,which=1) # Plot residuals.
myLm2
<- update(myLm1, ~ . - Company:Treat) # Refit, without interaction.
anova(myLm2, myLm1) # Show extra-ss-F-test p-value (for interaction effect).
if(require(car)){
# Use the car library
crPlots(myLm2)
}
## INFERENCE
myLm3 <- update(myLm2, ~ . - Company) # Fit reduced model without Company.
anova(myLm3, myLm2)
# Test for Company effect.
summary(myLm2)
# Show estimate and p-value for Pygmalion effect.
confint(myLm2,11) # Show 95% CI for Pygmalion effect.
## DISPLAY FOR PRESENTATION
beta
<- myLm2$coef

42

case1401
partialRes <- myLm2$res + beta[11]*ifelse(Treat=="Pygmalion",1,0) # partial res
boxplot(partialRes ~ Treat, # Boxplots of partial residuals for each treatment
ylab="Average Test Score, Adjusted for Company Effect (Deviation from Company Average)",
names=c("19 Control Platoons","10 Pygmalion Treated Platoons"),
col="green", boxlwd=2, medlwd=2,whisklty=1, whisklwd=2, staplewex=.2,
staplelwd=2, outlwd=2, outpch=21, outbg="green", outcex=1.5 )
detach(case1302)

case1401

Chimp Learning Times

Description
Researchers taught each of 4 chimps to learn 10 words in American sign language and recorded the
learning time for each word for each chimp. They wished to describe chimp differences and word
differences.
Usage
case1401
Format
A data frame with 40 observations on the following 4 variables.
Minutes learning time in minutes
Chimp a factor indicating chimp, with four levels "Booee", "Cindy", "Bruno" and "Thelma"
Sign a factor indicating word taught, with 10 levels
Order the order in which the sign was taught
Source
Ramsey, F.L. and Schafer, D.W. (2013). The Statistical Sleuth: A Course in Methods of Data
Analysis (3rd ed), Cengage Learning.
References
Fouts, R.S. (1973). Acquisition and Testing of Gestural Signs in Four Young Chimpanzees, Science
180: 978–980.
Examples
str(case1401)
attach(case1401)
## EXPLORATION AND MODEL DEVELOPMENT
plot(Minutes ~ Sign)
myLm1 <- lm(Minutes ~ Chimp + Sign)
plot(myLm1,which=1) # Plot residuals (indicates a need for transformation).
logMinutes <- log(Minutes)
myLm2 <- lm(logMinutes ~ Chimp + Sign)
plot(myLm2, which=1) # This looks fine.

case1402

43

if(require(car)){
# Use the car library
crPlots(myLm2) # Partial residual plots
}
## INFERENCE AND INTERPRETATION
myLm3 <- update(myLm2, ~ . - Chimp) # Fit reduced model without Chimp.
anova(myLm3, myLm2)
# Test for Chimp effect.
myLm4 <- update(myLm2, ~ . - Sign) # Fit reduced model without Sign.
anova(myLm4, myLm2)
# Test for Sign effect.
# Fit 2-way model without intercept to order signs from easiest to hardest
myAov1 <- aov(logMinutes ~ Sign + Chimp - 1)
sort(myAov1$coef[1:10]) # Show the ordering of Signs
orderedSign <- factor(Sign,levels=c("listen","drink","shoe","key","more",
"food","fruit","hat","look","string") ) # Re-order signs, easiest 1st
myAov2 <- aov(logMinutes ~ orderedSign + Chimp - 1) # Refit
opar <- par(no.readonly=TRUE) # Store current graphics parameters settings
par(mar=c(4.1,7.1,4.1,2.1)) # Adjust margins to allow room for y-axis labels
## takes too long to run
if(require(multcomp)){
# Use the multcomp library
myMultComp
<- glht(myAov2, linfct = mcp(orderedSign = "Tukey"))
plot(myMultComp) # Plot Tukey-adjusted confidence intervals.
summary(myMultComp)
# Show Tukey-adjusted p-values pairwise comparisons
confint(myMultComp)
# Show Tukey-adjusted 95% confidence intervals
}
par(opar) # Restore original graphics parameters settings
## DISPLAY FOR PRESENTATION
myYLab <- "Log Learning Time, Adjusted for Chimp Effect"
myXLab <- "Sign Learned"
if(require(car)){
# Use the car library
crPlots(myAov2, ylab=myYLab, xlab=myXLab,
main="Learning Times by Sign, Adjusted for Chimp Effects",
layout=c(1,1)) # Click on graph area to show next page (Just use first one.)
}
detach(case1401)

case1402

Effect of Ozone, SO2 and Drought on Soybean Yield

Description
In a completely randomized design with a 2x3x5 factorial treatment structure, researchers randomly
assigned one of 30 treatment combinations to open-topped growing chambers, in which two soybean
cultivars were planted. The responses for each chamber were the yields of the two types of soybean.
Usage
case1402

44

case1402

Format
A data frame with 30 observations on the following 5 variables.
Stress a factor indicating treatment, with two levels "Well-watered" and "Stressed"
SO2 a quantitative treatment with three levels 0, 0.02 and 0.06
O3 a quantitative treatment with five levels 0.02, 0.05, 0.07, 0.08 and 0.10
Forrest the yield of the Forrest cultivar of soybean (in kg/ha)
William the yield of the Williams cultivar of soybean (in kg/ha)
Source
Ramsey, F.L. and Schafer, D.W. (2013). The Statistical Sleuth: A Course in Methods of Data
Analysis (3rd ed), Cengage Learning.
References
Heggestad, H.E. and Lesser, V.M. (1990). Effects of Chronic Doses of Sulfur Dioxide, Ozone,
and Drought on Yields and Growth of Soybeans Under Field Conditions, Journal of Environmental
Quality 19: 488–495.
Examples
str(case1402)
attach(case1402)
## EXPLORATION AND MODEL DEVELOPMENT; FORREST CULTIVAR
logForrest <- log(Forrest)
# Fit model without interactions first--to examine partial residual plots.
myLm1 <- lm(logForrest ~ Stress + SO2 + O3)
if(require(car)){ # Use the car library
crPlots(myLm1)
# Partial res plots => linear effects of SO2 and O3 look ok.
}
myLm2 <- lm(logForrest ~ (Stress + SO2 + O3)^2) # all 2-factor interactions.
plot(myLm1, which=1)
# Residual plot looks ok.
anova(myLm1,myLm2) # Test for interactive effects.
## INFERENCE AND INTERPRETATION; FORREST CULTIVAR
summary(myLm1)
betaF <- myLm1$coef
# Effect of 0.01 increase in SO2 (note coeff is negative):
100*(1 - exp(0.01*betaF[3]))
#1.655701;
a 1.65% decrease in median yield
100*(1-exp(0.01*confint(myLm1,"SO2")))
#3.772277 -0.5074294: 95% onfidence interval for effect of 0.01 increase in SO2
# Effect of 0.01 increase in O3 (note coeff is negative):
100*(1 - exp(0.01*betaF[4]))
# 5.585979
I.e. a 5.6% decrease in median yield
100*(1-exp(0.01*confint(myLm1,"O3")))
#7.445966 3.688613: 95% confidence interval for effect of 0.01 increase in O3
# Effect of water stress (note coeff is positive for effect of well-watered):
100*(1 - exp(-betaF[2])) # Get estimate for negative of this beta
#3.220556: a 3.2% decrease in median yield due to water stress
100*(1-exp(-confint(myLm1,2)))
#-7.875521 13.17529: 95% confidence interval

case1501

45

## DISPLAY FOR PRESENTATION; FORREST CULTIVAR
jO3
<- jitter(O3,factor=.25) # Jitter for plotting; jittering factor 0.25.
jS
<- jitter(SO2,factor=.25) # Jitter SO2 for plotting.
cexfac <- 1.75 # Use character expansion factor of 1.75 for plotting symbols.
opar <- par(no.readonly=TRUE) # Store current graphics parameters settings
par(mfrow=c(1,2)) # Make a panel of 2 plots in 1 row.
myPointCode <- ifelse(Stress=="Well-watered",21,24)
myPointColor <- ifelse(Stress=="Well-watered","green","orange")
par(mar=c(4.1,4.1,2.1,0.1))
plot(Forrest ~ jO3, log="y", ylab="Yield of Forrest Cultivar (kg/ha)",
xlab=expression(paste(italic("Ozone ("),mu,"L/L), jittered")),
pch=myPointCode, lwd=2, bg=myPointColor, cex=cexfac)
legend(.02,2400, c("Well-watered","Water Stressed"), pch=c(21,24),
pt.cex=cexfac, pt.bg=c("green","orange"), pt.lwd=2, lty=c(3,1), lwd=c(2,2))
dummyO
<- seq(min(O3), max(O3), length=2)
curve1
<- exp(betaF[1] + betaF[3]*mean(SO2) + betaF[4]*dummyO)
curve2
<- exp(betaF[1] + betaF[2] + betaF[3]*mean(SO2)+ betaF[4]*dummyO)
lines(curve1 ~ dummyO,lwd=2)
lines(curve2 ~ dummyO,lwd=2,lty=3)
# PLOT FORREST VS SO2
par(mar=c(4.1,2.1,2.1,2.1)) # Change margins
plot(Forrest ~ jS, log="y", ylab="",
xlab=expression(paste(italic("Sulfur Dioxide ("),mu,"L/L), jittered")),
yaxt="n", pch=myPointCode, lwd=2, bg=myPointColor, cex=cexfac)
dummyS
<- seq(min(SO2),max(SO2),length=2)
curve1
<- exp(betaF[1] + betaF[3]*dummyS + betaF[4]*mean(O3))
curve2
<- exp(betaF[1] + betaF[2] + betaF[3]*dummyS + betaF[4]*mean(O3))
lines(curve1 ~ dummyS,lwd=2)
lines(curve2 ~ dummyS,lwd=2,lty=3)
par(opar) # Restore previous graphics parameter settings
detach(case1402)

case1501

Logging and Water Quality

Description
Data from an observational study of nitrate levels measured at three week intervals for five years
in two watersheds. One of the watersheds was undisturbed and the other had been logged with a
patchwork pattern.
Usage
case1501
Format
A data frame with 88 observations on the following 3 variables.
Week week after the start of the study
Patch natural logarithm of nitrate level (ppm) in the logged watershed (ppm)
NoCut natural logarithm of nitrate level in the undisturbed watershed (ppm)

46

case1501

Source
Ramsey, F.L. and Schafer, D.W. (2013). The Statistical Sleuth: A Course in Methods of Data
Analysis (3rd ed), Cengage Learnings.
References
Harr, R.D., Friderksen, R.L., and Rothacher, J. (1979). Changes in Streamflow Following Timber
Harvests in Southwestern Oregon, USDA/USFS Research Paper PNW-249, Pacific NW Forest and
Range Experiment Station, Portland, Oregon.
Examples
str(case1501)
attach(case1501)
## EXPLORATION
opar <- par(no.readonly=TRUE) # Store current graphics parameters settings
par(mfrow=c(2,1))
# Set graphics parameters: 2 row, 1 column layout
plot(NoCut ~ Week, type="b", ylab="Log of Nitrate Concentration; NoCut")
abline(h=mean(NoCut)) # Horizontal line at the mean
plot(Patch ~ Week, type="b", ylab="Log of Nitrate Concentration; Patch Cut")
abline(h=mean(Patch))
par(opar) # Restore previous graphics settings
lag.plot(NoCut,do.lines=FALSE) # Lag plot for NoCut
lag.plot(Patch,do.lines=FALSE) # Lag plot for Patch
pacf(NoCut) # partial autocorrelation function plot; noCut
pacf(Patch) # partial autocorrelation function plot; Patch
## INFERENCE (2-sample comparison, accounting for first serial correlation)
diff
<- mean(Patch) - mean(NoCut)
nPatch
<- length(Patch) # length of Patch series
nNoCut
<- length(NoCut)
# length of NoCut series
acfPatch <- acf(Patch, type="covariance") # auto covariances for Patch series
c0Patch <- acfPatch$acf[1]*nPatch/(nPatch-1) # variance; n-1 divisor (Patch)
c1Patch <- acfPatch$acf[2]*nPatch/(nPatch-1) # autocov; n-1 divisor (Patch)
acfNoCut <- acf(NoCut, type="covariance") # auto covariances for NoCut series
c0NoCut <- acfNoCut$acf[1]*nNoCut/(nNoCut - 1) # variance; n-1 divisor (NoCut)
c1NoCut <- acfNoCut$acf[2]*nNoCut/(nNoCut - 1) # autocov; n-1 divisor (NoCut)
dfPatch <- nPatch - 1
# DF (n-1); Patch
dfNoCut <- nNoCut - 1
# DF (n-1); NoCut
c0Pooled
c0Pooled
c1Pooled
c1Pooled

<- (dfPatch*c0Patch + dfNoCut*c0NoCut)/(dfPatch + dfNoCut)
#[1] 1.413295 = pooled estimate of variance
<- (dfPatch*c1Patch + dfNoCut*c1NoCut)/(dfPatch + dfNoCut)
#[1] 0.9103366 = pooled estimate of lag 1 covariance

# Pooled estimate of first serial correlation coefficient:
r1 <- c1Pooled/c0Pooled
#[1] 0.6441233
SEdiff <- sqrt((1 + r1)/(1-r1))*sqrt(c0Pooled*(1/nPatch + 1/nNoCut))
# t-test and confidence interval
tStat
<- diff/SEdiff #[1] 0.2713923
pValue
<- 1 - pt(tStat,dfPatch + dfNoCut)
# One-sided p-value
halfWidth <- qt(.975,dfPatch + dfNoCut)*SEdiff # half width of 95% CI
diff + c(-1,1)*halfWidth #95% CI -0.6557578 0.8648487

case1502

47

## GRAPHICAL DISPLAY FOR PRESENTATION
par(mfrow=c(1,1))
# Reset mfrow to a single plot per page
plot(exp(Patch) ~ Week, # Use exp(Patch) to show results in original units
log="y", type="b", xlab="Weeks After Logging",
ylab="Nitrate Concentration in Watershed Runoff (ppm)",
main="Nitrate Series in Patch-Cut and Undisturbed Watersheds",
pch=21, col="dark green", lwd=3, bg="green", cex=1.3 )
points(exp(NoCut) ~ Week, pch=24, col="dark blue", lwd=3, bg="orange",cex=1.3)
lines(exp(NoCut) ~ Week, lwd=3, col="dark blue",lty=3)
abline(h=exp(mean(Patch)),col="dark green",lwd=2)
abline(h=exp(mean(NoCut)),col="dark blue", lwd=2,lty=2)
legend(205,100,legend=c("Patch Cut", "Undisturbed"),
pch=c(21,24), col=c("dark green","dark blue"), pt.bg = c("green","orange"),
pt.cex=c(1.3,1.3), lty=c(1,3), lwd=c(3,3))
text(-1, 8.5, "Mean",col="dark green")
text(-1,6.3,"Mean", col="dark blue")
detach(case1501)

case1502

Global Warming

Description
The data are the temperatures (in degrees Celsius) averaged for the northern hemisphere over a
full year, for years 1850 to 2010. The 161-year average temperature has been subtracted, so each
observation is the temperature difference from the series average.
Usage
case1502
Format
A data frame with 161 observations on the following 2 variables.
Year year in which yearly average temperature was computed, from 1850 to 2010
Temperature northern hemisphere temperature minus the 161-year average (degrees Celsius)
Source
Ramsey, F.L. and Schafer, D.W. (2013). The Statistical Sleuth: A Course in Methods of Data
Analysis (3rd ed), Cengage Learning.
References
Jones, P.D., D. E. Parker, T. J. Osborn, and K. R. Briffa, (2011) Global and Hemispheric Temperature Anomalies and and Marine Instrumental Records, CDIAC, http://cdiac.ornl.gov/
trends/temp/jonescru/jones.html, August 4, 2011.

48

case1601

See Also
ex1519
Examples
str(case1502)
attach(case1502)
## EXPLORATION AND MODEL BUILDING
plot(Temperature ~ Year, type="b")

# Type = "b" for *both* points and lines

yearSquared <- Year^2
yearCubed <- Year^3
myLm1 <- lm(Temperature ~ Year + yearSquared + yearCubed)
res1 <- myLm1$res
myPacf <- pacf(res1) # Partial autocorrelation from residuals
r1 <- myPacf$acf[1] #First serial correlation coefficient
n <- length(Temperature)
# Series length = 161
v <- Temperature[2:n] - r1*Temperature[1:(n-1)]
# Filtered response
ones <- rep(1-r1, n-1)
# make a variable of all 1's
u1 <- Year[2:n]
- r1*Year[1:(n-1)]
# Filtered "ones"
u2 <- yearSquared[2:n] - r1*yearSquared[1:(n-1)] # Filtered X1
u3 <- yearCubed[2:n]
- r1*yearCubed[1:(n-1)] # Filtered X2
myLm2 <- lm(v ~ u1 + u2 + u3 )
res2 <- myLm2$res
pacf(res2)
# Looks fine; don't worry about lag 4 marginal significance
plot(myLm2, which=1) # Residual plot
summary(myLm2) # Cubic term isn't needed.
myLm3
<- update(myLm2, ~ . - u3) # Drop cubic term
## INFERENCE
summary(myLm3) # Everything remaining is statistically significant.
## GRAPHICAL DISPLAY FOR PRESENTATION
plot(Temperature ~ Year, xlab="Year",
ylab=expression(paste("Annual Average Temperature (Difference From Average), ",
degree,"C")),main="Annual Average Temperature in Northern Hemisphere; 1850-2010",
type="b", pch=21, lwd=2, bg="green", cex=1.5)
myFits <- myLm3$fit
lines(myFits ~ Year[2:161], col="blue", lwd=2)
legend(1850,0.6,"Quadratic Regression Fit, Adjusted for AR(1) Serial Correlation",
col="blue", lwd=2, box.lty=0)
detach(case1502)

case1601

Sites of Short- and Long-Term Memory

Description
Researchers taught 18 monkeys to distinguish each of 100 pairs of objects, 20 pairs each at 16, 12,
8, 4, and 2 weeks prior to a treatment. After this training, they blocked access to the hippocampal

case1601

49

formation in 11 of the monkeys. All monkeys were then tested on their ability to distinguish the objects. The five-dimensional response for each monkey is the number of correct objects distinguished
among those taught at 16, 12, 8, 4, and 2 weeks prior to treatment.
Usage
case1601
Format
A data frame with 18 observations on the following 7 variables.
Monkey Monkey name
Treatment a treatment factor with levels "Control" and "Treated"
Week2 percentage of 20 objects taught 2 weeks prior to treatment that were correctly distinguished
in the test
Week4 percentage of 20 objects taught 4 weeks prior to treatment that were correctly distinguished
in the test
Week8 percentage of 20 objects taught 8 weeks prior to treatment that were correctly distinguished
in the test
Week12 percentage of 20 objects taught 12 weeks prior to treatment that were correctly distinguished in the test
Week16 percentage of 20 objects taught 16 weeks prior to treatment that were correctly distinguished in the test
Source
Ramsey, F.L. and Schafer, D.W. (2013). The Statistical Sleuth: A Course in Methods of Data
Analysis (3rd ed), Cengage Learning.
References
Sola-Morgan, S. M. and Squire, L. R. (1990). The Primate Hippocampal Formation: Evidence for
a Time-limited Role in Memory Storage, Science 250: 288–290.
Examples
str(case1601)
attach(case1601)
## EXPLORATION
short <- (Week2 + Week4)/2
long <- (Week8 + Week12 + Week16)/3
myPointCode <- ifelse(Treatment=="Control",15,16)
myPointColor <- ifelse(Treatment=="Control","orange","green")
plot(long ~ short, pch=myPointCode, col=myPointColor, cex=2)
abline(h=mean(long),lty=2)
abline(v=mean(short),lty=2)
identify(short,long,labels=Monkey)
# Identify outliers; press Esc when done
## INFERENCE USING HOTELLING's T-SQUARED TEST
myLm1
<- lm(cbind(short,long) ~ Treatment) # Full model
myLm2
<- lm(cbind(short,long) ~ 1) # Reduced model, with only intercept

50

case1602
anova(myLm2, myLm1, test="Hotelling") # p-value for Treatment effect
# confidence intervals
n1 <- sum(Treatment=="Control") # 7 control monkeys
n2 <- sum(Treatment=="Treated") # 11 treated monkeys
multiplier
<- sqrt(2*((n1+n2-2)/(n1+n2-3))*qf(.95,2,n1+n2-3)) # Sleuth p. 492
summary(myLm1)
shortEffect
<- myLm1$coef[2,1] # Difference in sample averages; Short
seShortEffect <- 3.352
# Read this from summary(myLm1)
halfWidth <- multiplier*seShortEffect # Half width of 95% confidence interval
shortEffect + c(-1,1)*halfWidth #95% CI for effect of treatment on Short
longEffect
<- myLm1$coef[2,2] # Difference in sample averages; Long
seLongEffect <- 3.2215 # Read this from summary(myLm1)
halfWidth
<- multiplier*seLongEffect # Half width of 95% confidence interval
longEffect + c(-1,1)*halfWidth #95% CI for effect of treatment on Long
## GRAPHICAL DISPLAY FOR PRESENTATION
myPointCode
<- ifelse(Treatment=="Control",21,22)
myPointColor <- ifelse(Treatment=="Control","green","orange")
plot(long ~ jitter(short),
xlab="Short-Term Memory Score (Percent Correct)",
ylab="Long-Term Memory Score (Percent Correct)",
main="Memory Scores for 11 Hippocampus-Blocked and 7 Control Monkeys",
pch=myPointCode, bg=myPointColor, cex=2.5, lwd=3)
identify(short,long,labels=Monkey) # Label the outliers; press Esc when done
legend(52,54,legend=c("Control","Hippocampus Blocked"), pch=c(21,22),
pt.bg=c("green","orange"), pt.cex=c(2.5,2.5), pt.lwd=c(3,3), cex=1.5)
## ADVANCED: RANDOMIZATION TEST FOR EQUALITY OF BIVARIATE RESPONSES
myAnova <- anova(myLm2, myLm1, test="Hotelling") #Hotelling Test for Treatment
myAnova$approx[2]
#[1] 12.32109: F-statistic
numRep <- 50 # Number of random regroupings (change to 50,000)
FStats <- rep(0,numRep) # Initialize a variable for storing the F-statistics
myLmReduced <- lm(cbind(short,long) ~ 1)# Fit the reduced model once
for (rep in 1:numRep) { # Do the following commands in parenthese num.rep times
randomGroup <- rep("Group1",18) # Set randomGroup initially to all "Group1"
randomGroup[sample(1:18,7)] <- "Group2" # Change 7 at random to "Group2"
randomGroup <- factor(randomGroup) # Make the character variable a factor
myLmFull <- lm(cbind(short,long) ~ randomGroup) # Fit full model
myAnova2 <- anova(myLmReduced, myLmFull, test="Hotelling") # Hotelling's test
FStats[rep] <- myAnova2$approx[2]
# Store the F-statistic
} # If numRep = 50,000, go get a cup of coffee while you wait for this.
hist(FStats, main="Approx. Randomizatin Dist of F-stat if No Treatment Effect")
abline(v=12.32109)
# Show actually observed Hotelling F-statistic
pValue <- sum(FStats >= 12.32109)/numRep
pValue # Approximate randomization test p-value (no distributional assumptions)
detach(case1601)

case1602

Oat Bran and Cholesterol

case1602

51

Description
In a randomized, double-blind, crossover experiment, researchers randomly assigned 20 volunteer
hospital employees to either a low-fiber or low-fiber treatment group. The subjects followed the
diets for six weeks. After two weeks on their normal diet, all patients crossed over to the other
treatment group for another six weeks. The total serum cholesterol (in mg/dl) was measured on
each patient before the first treatment, at the end of the first six week treatment, and at the end of
the second six week treatment.
Usage
case1602
Format
A data frame with 20 observations on the following 4 variables.
Baseline total serum cholesterol before treatment
HiFiber total serum cholesterol after the high fiber diet
LoFiber total serum cholesterol after the low fiber diet
Order factor to identify order of treatment, with two levels "HL" and "LH"
Source
Ramsey, F.L. and Schafer, D.W. (2013). The Statistical Sleuth: A Course in Methods of Data
Analysis (3rd ed), Cengage Learning.
References
Swain, J.F., Rouse, I.L., Curley, C.B., and Sacks, F.M. (1990). Comparison of the Effects of Oat
Bran and Low-fiber Wheat on Serum Lipoprotein Levels and Blood Pressure, New England Journal
of Medicine 320: 1746–1747.
Examples
str(case1602)
attach(case1602)
## EXPLORATION
highMinusBase <- HiFiber-Baseline
highMinusLow <- HiFiber-LoFiber
plot(highMinusBase ~ highMinusLow)
abline(h=0) # Horizontal line at 0
abline(v=0) # Vertical line at 0
# Hotelling 2-sample t-test for order effect on bivariate response:
myLm1
<- lm(cbind(highMinusBase,highMinusLow) ~ Order )
# Full model
myLm2
<- update(myLm1, ~ . - Order) # Reduced model withour Order effect
anova(myLm2, myLm1, test="Hotelling") # p-value for Order effect
## INFERENCE: HOTELLING ONE-SAMPLE TEST THAT MEAN OF BIVARIATE RESPONSE IS (0,0)
myLm3
<- lm(cbind(highMinusBase, highMinusLow) ~ 1)
# Full model
myLm4
<- update(myLm3, ~ . - 1) # Reduced model (with both means = 0)
anova(myLm4, myLm3, test="Hotelling") # test that the bivariate mean is (0,0)
# Confidence intervals

52

case1701
summary(myLm3)
HighMinusBase <- myLm3$coef[1] # -13.850
seHighMinusBase <- 3.533 # Standard error, read from summary(myLm3)
HighMinusLow <- myLm3$coef[2] # -0.850
seHighMinusLow <- 3.527 # Standard error, read from summary(myLm3)
n <- length(highMinusBase) # 20: sample size
multiplier  strong evidence of interaction
# It appears that the intercepts are the same for both light and dark morphs,
# that there is no effect of Distance for light morphs, but there is an effect
# of Distance for dark morphs.
## INFERENCE AND INTERPREATION
myTerm <- Distance*ifelse(Morph=="dark",1,0) # Create indicator var for "dark"
myGlm3 <- glm(binResponse ~ myTerm, family=binomial)
summary(myGlm3)
## GRAPHICAL DISPLAY FOR PRESENTATION
myPointCode <- ifelse(Morph=="dark",22,24)
myPointColor <- ifelse(Morph=="dark","blue","orange")
plot(proportionRemoved ~ Distance, ylab="Proportion of Moths Taken",
main="Proportions of Moths Taken by Predators at Seven Locations",
xlab="Distance from Liverpool (km)", pch=myPointCode, bg=myPointColor, cex=2,
lwd=2)
beta <- myGlm3$coef
dummyDist <- seq(0,55,length=50)
lp <- beta[1] + beta[2]*dummyDist
propDark <- exp(lp)/(1 + exp(lp))
lines(propDark ~ dummyDist,lwd=2,col="blue")
propLight <- rep(exp(beta[1])/(1 + exp(beta[1])),length(dummyDist))
lines(propLight ~ dummyDist,lwd=2,col="orange")
legend(0,0.47,legend=c("Dark Morph","Light Morph"),
pch=c(22,24),pt.bg=c("blue","orange"),pt.cex=c(2,2),pt.lwd=c(2,2))
detach(case2102)

case2201

Age and Mating Success of Male Elephants

Description
Although male elephants are capable of reproducing by 14 to 17 years of age, your adult males
are usually unsuccessful in competing with their larger elders for the attention of receptive females.

72

case2201
Since male elephants continue to grow throughout their lifetimes, and since larger males tend to be
more successful at mating, the males most likely to pass their genes to future generations are those
whose characteristics enable them to live long lives. Joyce Poole studied a population of African
elephants in Amboseli National Park, Kenya, for 8 years. This data frame contains the number of
successful matings and ages (at the study’s beginning) of 41 male elephants.

Usage
case2201
Format
A data frame with 41 observations on the following 2 variables.
Age Age of elephant at beginning of study
Matings Number of successful matings
Source
Ramsey, F.L. and Schafer, D.W. (2002). The Statistical Sleuth: A Course in Methods of Data
Analysis (2nd ed), Duxbury.
References
Poole, J.H. (1989). Mate Guarding, Reproductive Success and Female Choice in African Elephants,
Animal Behavior 37: 842–849.
Examples
str(case2201)
attach(case2201)
## EXPLORATION AND MODEL BUILDING
plot(Matings ~ Age, log="y")
ageSquared <- Age^2
myGlm1 <- glm(Matings ~ Age + ageSquared, family=poisson)
summary(myGlm1) # No evidence of a need for ageSquared
## INFERENCE AND INTERPRETATION
myGlm2 <- update(myGlm1, ~ . - ageSquared)
summary(myGlm2)
beta <- myGlm2$coef
exp(beta[2]) #1.071107
exp(confint(myGlm2,2)) #1.042558 1.100360
# Interpretation: Associated with each 1 year increase in age is a 7% increase
# in the mean number of matings (95% confidence interval 4% to 10% increase).
## GRAPHICAL DISPLAY FOR PRESENTATION
plot(Matings ~ Age, ylab="Number of Successful Matings",
xlab="Age of Male Elephant (Years)",
main="Age and Number of Successful Matings for 41 African Elephants",
pch=21, bg="green", cex=2, lwd=2)
dummyAge <- seq(min(Age),max(Age), length=50)
lp <- beta[1] + beta[2]*dummyAge

case2202

73

curve <- exp(lp)
lines(curve ~ dummyAge,lwd=2)
detach(case2201)

case2202

Characteristics Associated with Salamander Habitat

Description
The Del Norte Salamander (plethodon elongates) is a small (5–7 cm) salamander found among rock
rubble, rock outcrops and moss-covered talus in a narrow range of northwest California. To study
the habitat characteristics of the species and particularly the tendency of these salamanders to reside
in dwindling old-growth forests, researchers selected 47 sites from plausible salamander habitat in
national forest and parkland. Randomly chosen grid points were searched for the presence of a site
with suitable rocky habitat. At each suitable site, a 7 metre by 7 metre search are was examined for
the number of salamanders it contained. This data frame contains the counts of salamanders at the
sites, along with the percentage of forest canopy and age of the forest in years.
Usage
case2202
Format
A data frame with 47 observations on the following 4 variables.
Site Investigated site
Salamanders Number of salamanders found in 49 m2 area
PctCover Percentage of canopy cover
ForestAge Forest age
Source
Ramsey, F.L. and Schafer, D.W. (2013). The Statistical Sleuth: A Course in Methods of Data
Analysis (3rd ed), Cengage Learning.
References
Welsh, H.H. and Lind, A.J. (1995). Journal of Herpetology 29(2): 198–210.
Examples
str(case2202)
attach(case2202)
## EXPLORATION AND MODEL BUILDING
logSalamanders <- log(Salamanders + .5)
logForestAge
<- log(ForestAge + .5)
myMatrix
<- cbind(PctCover,logForestAge,logSalamanders)
if (require(car)) { # Use car library
scatterplotMatrix(myMatrix, diagonal="histogram", reg.line=FALSE, spread=FALSE)

74

ex0112
}
myGlm1 <- glm(Salamanders ~ PctCover + logForestAge + PctCover:logForestAge,
family=poisson)
summary(myGlm1)
# Backward elimination...
myGlm2 <- update(myGlm1, ~ . - PctCover:logForestAge)
summary(myGlm2)
myGlm3 <- update(myGlm2, ~ . - logForestAge)
summary(myGlm3)
# PctCover is the only explanatory variable remaining
plot(Salamanders ~ PctCover) # It appears that there are 2 distributions
# of Salamander counts; one for PctCover < 70 and one for PctCover > 70
# See if PctCover is associated Salamanders in each subset
myGlm4 <- glm(Salamanders ~ PctCover, family=poisson,subset=(PctCover > 70))
summary(myGlm4)
# No evidence of an effect for this subset
myGlm5 <- glm(Salamanders ~ PctCover, family=poisson,subset=(PctCover < 70))
summary(myGlm5)
# No evidence on this subset either
## INFERENCE (2 means)
Group <- ifelse(PctCover > 70,"High","Low")
Group <- factor(Group, levels=c("Low","High") ) # Make "Low Cover" the ref group
myGlm6 <- glm(Salamanders ~ Group, family=poisson)
summary(myGlm6)
## GRAPHICAL DISPLAY FOR PRESENTATION
plot(Salamanders ~ PctCover, ylab="Number of Salamanders",
xlab="Percentage of Canopy Covered",
main="Number of Salamanders versus Percent Canopy Cover",
pch=21,bg="green", cex=2, lwd=2)
beta <- myGlm6$coef
lines(c(0,55),exp(c(beta[1],beta[1])),lwd=2)
text(56,exp(beta[1]),paste("mean= ",round(exp(beta[1]),1)),adj=0)
lines(c(76,93),exp(c(beta[1]+beta[2],beta[1]+beta[2])),lwd=2)
text(56,exp(beta[1]+beta[2]),paste("mean=",round((beta[1]+beta[2]),1)),adj=-1)
detach(case2202)

ex0112

Fish Oil and Blood Pressure

Description
Researchers used 7 red and 7 black playing cards to randomly assign 14 volunteer males with high
blood pressure to one of two diets for four weeks: a fish oil diet and a standard oil diet. These data
are the reductions in diastolic blood pressure.
Usage
ex0112

ex0116

75

Format
A data frame with 14 observations on the following 2 variables.
BP reduction in diastolic blood pressure (in mm of mercury)
Diet factor variable indicating the diet that the subject followed, with levels "FishOil" and "RegularOil"
Source
Ramsey, F.L. and Schafer, D.W. (2013). The Statistical Sleuth: A Course in Methods of Data
Analysis (3rd ed), Cengage Learning.
References
Knapp, H.R. and FitzGerald, G.A. (1989). The Antihypertensive Effects of Fish Oil, New England
Journal of Medicine 320: 1037–1043.
Examples
str(ex0112)

ex0116

Gross Domestic Product (GDP) per Capita

Description
The data are the gross domestic product per capita for 228 countries in 2010.
Usage
ex0116
Format
A data frame with 228 observations on the following 3 variables.
Rank rank order of country from highest to lowest GDP
Country name of country
PerCapitaGDP per capita GDP in $US
Source
Ramsey, F.L. and Schafer, D.W. (2013). The Statistical Sleuth: A Course in Methods of Data
Analysis (3rd ed), Cengage Learning.
References
Central Intelligence Agency, Country Comparison: GDP per capita (PPP), The World Factbook,
https://www.cia.gov/library/publications/theworld-factbook/rankorder/2004rank.html (retrieved June
30,2011).
Examples
str(ex0116)

76

ex0126

ex0125

Zinc concentrations for two groups of rats

Description
The data are the zinc concentrations (in mg/ml) in the blood of rats that received a dietary supplement and rats that did not receive the supplement.
Usage
ex0125
Format
A data frame with 39 observations on the following 2 variables.
Group a factor representing the group, with levels "A" for the dietary supplement group and "B"
for the control group
Zinc measured zinc concentration in mg/ml
Source
Ramsey, F.L. and Schafer, D.W. (2013). The Statistical Sleuth: A Course in Methods of Data
Analysis (3rd ed), Cengage Learning.
Examples
str(ex0125)

ex0126

Environmental Voting of Democrats and Republicans in the U.S.
House of Representatives

Description
The data are the number of pro- and anti-environmental votes, according to the League of Conservation Voters, for each member of the U.S. House of Representatives in 2005, 2006, or 2007.
Usage
ex0126

ex0127

77

Format
A data frame with 492 observations on the following 10 variables.
State the state that the member represented
Representative name of the representative
Party a factor representing political party, with levels "R" for Republican, "D" for Democratic, and
"I" for Independent
Pro05 the number of pro-environmental votes in 2005
Anti05 the number of anti-environmental votes in 2005
Pro06 the number of pro-environmental votes in 2006
Anti06 the number of anti-environmental votes in 2006
Pro07 the number of pro-environmental votes in 2007
Anti07 the number of anti-environmental votes in 2007
PctPro the total percentage of a representative’s votes between 2005 and 2007 that were deemd to
be pro-environmental
Source
Ramsey, F.L. and Schafer, D.W. (2013). The Statistical Sleuth: A Course in Methods of Data
Analysis (3rd ed), Cengage Learning.
See Also
ex0127
Examples
str(ex0126)

ex0127

Environmental Voting of Democrats and Republicans in the U.S. Senate

Description
The data are the number of pro- and anti-environmental votes, according to the League of Conservation Voters, for each member of the U.S. Senate in 2005, 2006, or 2007.
Usage
ex0127

78

ex0211

Format
A data frame with 112 observations on the following 10 variables.
State the state that the member represented
Senator name of the senator
Party a factor representing political party, with levels "R" for Republican, "D" for Democratic, and
"I" for Independent
Pro2005 the number of pro-environmental votes in 2005
Anti2005 the number of anti-environmental votes in 2005
Pro2006 the number of pro-environmental votes in 2006
Anti2006 the number of anti-environmental votes in 2006
Pro2007 the number of pro-environmental votes in 2007
Anti2007 the number of anti-environmental votes in 2007
PctPro the total percentage of a representative’s votes between 2005 and 2007 that were deemd to
be pro-environmental
Source
Ramsey, F.L. and Schafer, D.W. (2013). The Statistical Sleuth: A Course in Methods of Data
Analysis (3rd ed), Cengage Learning.
See Also
ex0126
Examples
str(ex0127)

ex0211

Lifetimes of Guinea Pigs

Description
The data are survival times (in days) of guinea pigs that were randomly assigned either to a control
group or to a treatment group that received a dose of tubercle bacilli.
Usage
ex0211
Format
A data frame with 122 observations on the following 2 variables.
Lifetime survival time of guinea pig (in days)
Group a factor with levels "Bacilli" and "Control", indicating the group to which the guinea
pig was assigned

ex0218

79

Source
Ramsey, F.L. and Schafer, D.W. (2013). The Statistical Sleuth: A Course in Methods of Data
Analysis (3rd ed), Cengage Learning.
References
Doksum, K. (1974). Empirical Probability Plots and Statistical Inference for Nonlinear Models in
the Two–sample Case, Annals of Statistics 2: 267–277.
Examples
str(ex0211)

ex0218

Peter and Rosemary Grant’s Finch Beak Data

Description
In the 1980s, biologists Peter and Rosemary Grant caught and measured all the birds from more
than 20 generations of finches on the Galapagos island of Daphne Major. In one of those years,
1977, a severe drought caused vegetation to wither, and the only remaining food source was a
large, tough seed, which the finches ordinarily ignored. Were the birds with larger and stronger
beaks for opening these tough seeds more likely to survive that year, and did they tend to pass this
characteristic to their offspring? The data are beak depths (height of the beak at its base) of 751
finches caught the year before the drought (1976) and 89 finches captured the year after the drought
(1978).
Usage
ex0218
Format
A data frame with 840 observations on the following 2 variables.
Year Year the finch was caught, 1976 or 1978
Depth Beak depth of the finch (mm)
Source
Ramsey, F.L. and Schafer, D.W. (2013). The Statistical Sleuth: A Course in Methods of Data
Analysis (3rd ed), Cengage Learning.
References
Grant, P. (1986). Ecology and Evolution of Darwin’s Finches, Princeton University Press, Princeton, N.J.
See Also
case0201

80

ex0221

Examples
str(ex0218)

ex0221

Bumpus’s Data on Natural Selection

Description
As evidence in support of natural selection, Bumpus presented measurements on house sparrows
brought to the Anatomical Laboratory of Brown University after an uncommonly severe winter
storm. Some of these birds had survived and some had perished. Bumpus asked whether those that
perished did so because they lacked physical characteristics enabling them to withstand the intensity
of that particular instance of selective elimination. The data are on the the weights, in grams, for
the 24 adult male sparrows that perished and for the 35 adult males that survived.

Usage
ex0221
Format
A data frame with 59 observations on the following 2 variables.
Humerus humerus length of adult male sparrows (inches)
Status factor variable indicating whether the sparrow perished or survived in a winter storm, with
levels Perished and Survived
Source
Ramsey, F.L. and Schafer, D.W. (2013). The Statistical Sleuth: A Course in Methods of Data
Analysis (3rd ed), Cengage Learning.

See Also
ex2016
Examples
str(ex0221)

ex0222

ex0222

81

Male and Female Intelligence

Description
These data are armed Forces Qualifying Test (AFQT) score percentiles and component test scores
in arithmetic reasoning, word knowledge, paragraph comprehension, and mathematical knowledge
for a sample of 1,278 U.S. women and 1,306 U.S. men in 1981.
Usage
ex0222
Format
A data frame with 2,584 observations on the following 6 variables.
Gender a factor with levels "female" and "male"
Arith score on the arithmetic reasoning component of the AFQT test
Word score on the word knowledge component
Parag score on the paragraph comprehension component
Math score on the mathematical knowledge component
AFQT percentile score on the AFQT test
Source
Ramsey, F.L. and Schafer, D.W. (2013). The Statistical Sleuth: A Course in Methods of Data
Analysis (3rd ed), Cengage Learning.
References
National Longitudinal Survey of Youth, U.S. Bureau of Labor Statistics, http://www.bls.gov/
nls/home.htm (May 8, 2008).
See Also
ex0330, ex0331, ex0524, ex0525, ex0828, ex0923, ex1033, ex1223
Examples
str(ex0222)

82

ex0223

ex0223

Speed Limits and Traffic Fatalities

Description
The National Highway System Designation Act was signed into law in the United States on November 28, 1995. Among other things, the act abolished the federal mandate of 55 mile per hour maximum speed limits on roads in the United States and permitted states to establish their own limits.
Of the 50 states (plus the District of Columbia), 32 increased their speed limits at the beginning of
1996 or sometime during 1996. These data are the percentage changes in interstate highway traffic
fatalities from 1995 to 1996.

Usage
ex0223
Format
A data frame with 51 observations on the following 5 variables.
State US state
Fatalities1995 number of traffic fatalities in 1995
Fatalities1996 number of traffic fatalities in 1996
PctChange percentage change in interstate traffic fatalities between 1995 and 1996
SpeedLimit a factor with levels "Inc" and "Ret", indicating whether the state increased or retained its speed limit

Source
Ramsey, F.L. and Schafer, D.W. (2002). The Statistical Sleuth: A Course in Methods of Data
Analysis (2nd ed), Duxbury.

References
Report to Congress: The Effect of Increased Speed Limits in the Post-NMSL Era, National Highway Traffic Safety Administration, February, 1998; available in the reports library at http://
www-fars.nhtsa.dot.gov/.
Examples
str(ex0223)

ex0321

ex0321

83

Umpire Life Lengths

Description
Researchers collected historical and current data on umpires to investigate their life expectancies
following the collapse and death of a U.S. major league baseball umpire. They were investigating
speculation that stress associated with the job posed a health risk. Data were found on 227 umpires
who had died or had retired and were still living. The data set includes the dates of birth and death.
Usage
ex0321
Format
A data frame with 227 observations on the following 3 variables.
Lifelength observed lifetime for those umpires who had died by the time of the study or current
age of those still living
Censored 0 for those who had died by the time of the study or 1 for those who were still living
Expected length from actuarial life tables for individuals who were alive at the time the person first
became an umpire
Source
Ramsey, F.L. and Schafer, D.W. (2013). The Statistical Sleuth: A Course in Methods of Data
Analysis (3rd ed), Cengage Learning.
References
Cohen, R.S., Kamps, C.A., Kokoska, S., Segal E.M. and Tucker, J.B.(2000). Life Expectancy of
Major League Baseball Umpires, The Physician and Sportsmedicine 28(5): 83–89.
Examples
str(ex0321)

ex0323

Solar Radiation and Skin Cancer

Description
Data contains yearly skin cancer rates (per 100,000 people) in Connecticut from 1938 to 1972 with
a code indicating those years that came two years after higher than average sunspot activity and
those years that came two years after lower than average sunspot activity.
Usage
ex0323

84

ex0327

Format
A data frame with 35 observations on the following 3 variables.
Year year
CancerRate skin cancer rate per 100,000 people
SunspotActivity a factor with levels "High" and "Low"
Source
Ramsey, F.L. and Schafer, D.W. (2013). The Statistical Sleuth: A Course in Methods of Data
Analysis (3rd ed), Cengage Learning.
References
Andrews, D.F. and Herzberg, A.M. (1985). Data: A Collection of Problems from many Fields for
the Student and Research Worker, Springer-Verlag.
Examples
str(ex0323)

ex0327

Pollen Removal

Description
As part of a study to investigate reproductive strategies in plants, biologists recorded the time spent
at sources of pollen and the proportions of pollen removed by bumblebee queens and honeybee
workers pollinating a species of lily.
Usage
ex0327
Format
A data frame with 47 observations on the following 3 variables.
PollenRemoved proportion of pollen removed
DurationOfVisit duration of visit (in seconds)
BeeType factor variable with levels "Queen" and "Worker"
Source
Ramsey, F.L. and Schafer, D.W. (2013). The Statistical Sleuth: A Course in Methods of Data
Analysis (3rd ed), Cengage Learning.
References
Harder, L.D. and Thompson, J.D. (1989). Evolutionary Options for Maximizing Pollen Dispersal
of Animal-pollinated Plants, American Naturalist 133: 323–344.

ex0330

85

Examples
str(ex0327)

ex0330

Education and Income

Description
The data are incomes in U.S. dollars for 1,020 working Americans who had 12 years of education
and 406 working Americans who had 16 years of education, in 2005.
Usage
ex0330
Format
A data frame with 1,426 observations on the following 3 variables.
Subject a subject identification number
Educ number of years of education–either 12 or 16
Income2005 income, in dollars
Source
Ramsey, F.L. and Schafer, D.W. (2013). The Statistical Sleuth: A Course in Methods of Data
Analysis (3rd ed), Cengage Learning.
References
National Longitudinal Survey of Youth, U.S. Bureau of Labor Statistics, http://www.bls.gov/
nls/home.htm (May 8, 2008).
See Also
ex0222, ex0331, ex0524, ex0525, ex0828, ex0923, ex1033, ex1223
Examples
str(ex0330)

86

ex0332

ex0331

Education and Income

Description
The data are incomes in U.S. dollars for 406 working Americans who had 16 years of education
and 374 working Americans who had more than 16 years of education, in 2005.
Usage
ex0331
Format
A data frame with 780 observations on the following 3 variables.
Subject a subject identification number
Educ factor with levels "16" and ">16"
Income2005 income, in dollars
Source
Ramsey, F.L. and Schafer, D.W. (2013). The Statistical Sleuth: A Course in Methods of Data
Analysis (3rd ed), Cengage Learning.
References
National Longitudinal Survey of Youth, U.S. Bureau of Labor Statistics, http://www.bls.gov/
nls/home.htm (May 8, 2008).
See Also
ex0222, ex0330, ex0524, ex0525, ex0828, ex0923, ex1033, ex1223
Examples
str(ex0331)

ex0332

College Tuition

Description
In-state and out-of-state tuition in dollars for random samples of 25 private and 25 public U.S.
colleges and universities in 2011-2012.
Usage
ex0332

ex0333

87

Format
A data frame with 50 observations on the following 4 variables.
College name of the college
Type a factor with levels "Private" and "Public"
InState in-state tuition in dollars
OutOfState out-of-state tuition in dollars
Source
Ramsey, F.L. and Schafer, D.W. (2013). The Statistical Sleuth: A Course in Methods of Data
Analysis (3rd ed), Cengage Learning.
References
College Board: http://www.collegeboard.com/student/ (11 July 2011)
Examples
str(ex0332)

ex0333

Brain Size and Litter Size

Description
Relative brain weights for 51 species of mammal whose average litter size is less than 2 and for 45
species of mammal whose average litter size is greater than or equal to 2.
Usage
ex0333
Format
A data frame with 96 observations on the following 2 variables.
BrainSize relative brain sizes (1000 * Brain weight/Body weight) for 96 species of mammals
LitterSize factor variable with levels "Small" and "Large"
Source
Ramsey, F.L. and Schafer, D.W. (2013). The Statistical Sleuth: A Course in Methods of Data
Analysis (3rd ed), Cengage Learning.
References
Sacher, G.A. and Staffeldt, E.F. (1974). Relation of Gestation Time to Brain Weight for Placental
Mammals: Implications for the Theory of Vertebrate Growth, American Naturalist 108: 593–613.

88

ex0428

See Also
case0902
Examples
str(ex0333)

ex0428

Darwin’s Data

Description
Plant heights (inches) for 15 pairs of plants of the same age, one of which was grown from a seed
from a cross-fertilized flower and the other of which was grown from a seed from a self-fertilized
flower.

Usage
ex0428
Format
A data frame with 15 observations on the following 2 variables.
Cross height (inches) of cross-fertilized plant
Self height (inches) of self-fertilized plant

Source
Ramsey, F.L. and Schafer, D.W. (2013). The Statistical Sleuth: A Course in Methods of Data
Analysis (3rd ed), Cengage Learning.

References
Andrews, D.F. and Herzberg, A.M. (1985). Data: A Collection of Problems from many Fields for
the Student and Research Worker, Springer-Verlag.

Examples
str(ex0428)

ex0429

ex0429

89

Salvage Logging

Description
The data are the number of tree seedlings per transect in nine logged (L) and seven unlogged (U)
plots affected by the Oregon Biscuit Fire, in 2004 and 2005, and the percentage of seedlings lost
between 2004 and 2005. The goal is to see whether the distribution of seedlings lost differs in
logged and unlogged plots.
Usage
ex0429
Format
A data frame with 16 observations on the following 5 variables.
Plot an identification code for plot
Action a factor with levels "L" for logged and "U" for unlogged
Seedlings2004 the number of seedlings in the plot in 2004
Seedlings2005 the number of seedlings in the plot in 2005
PercentLost the percentage of 2004 seedlngs that were lost
Source
Ramsey, F.L. and Schafer, D.W. (2013). The Statistical Sleuth: A Course in Methods of Data
Analysis (3rd ed), Cengage Learning.
References
Donato, D.C., Fontaine, J.B., Campbell, J.L., Robinson, W.D., Kauffman, J.B., and Law, B.E.
(2006). Post-Wildfire Logging Hinders Regeneration and Increases Fire Risk, Science 311: 352.
Examples
str(ex0429)

ex0430

Sunlight Protection Factor

Description
Tolerance to sunlight (in minutes) for 13 patients prior to and after treatment with a sunscreen.
Usage
ex0430

90

ex0431

Format
A data frame with 13 observations on the following 2 variables.
PreTreatment tolerance to sunlight (minutes) prior to sunscreen application
Sunscreen tolerance to sunlight (minutes) after sunscreen application
Source
Ramsey, F.L. and Schafer, D.W. (2013). The Statistical Sleuth: A Course in Methods of Data
Analysis (3rd ed), Cengage Learning.
References
Fusaro, R.M. and Johnson, J.A. (1974). Sunlight Protection for Erythropoietic Protoporphyria Patients, Journal of the American Medical Association 229(11): 1420.
Examples
str(ex0430)

ex0431

Effect of Group Therapy on Survival of Breast Cancer Patients

Description
Researchers randomly assigned metastatic breast cancer patients to either a control group or a group
that received weekly 90 minute sessions of group therapy and self-hypnosis, to see whether the latter
treatment improved the patients’ quality of life.
Usage
ex0431
Format
A data frame with 58 observations on the following 3 variables.
Survival months of survival after beginning of study
Group a factor with levels "Control" and "Therapy"
Censor 0 if entire lifetime observed, 1 if patient known to have lived at least 122 months
Source
Ramsey, F.L. and Schafer, D.W. (2013). The Statistical Sleuth: A Course in Methods of Data
Analysis (3rd ed), Cengage Learning.
References
Spiegel, D., Bloom, J.R., Kraemer, H.C. and Gottheil, E. (1989). Effect of Psychosocial Treatment
on Survival of Patients with Metastatic Breast Cancer, Lancet 334(8668): 888–891.
Examples
str(ex0431)

ex0432

ex0432

91

Therapeutic Marijuana

Description
To investigate the capacity of marijuana to reduce the side effects of cancer chemotherapy, researchers performed a double-blind, randomized, crossover trial. Fifteen cancer patients on chemotherapy were randomly assigned to receive either a marijuana treatment or a placebo treatment after their
first three sessions of chemotherapy. They were then crossed over to the opposite treatment for their
next 3 sessions.
Usage
ex0432
Format
A data frame with 15 observations on the following 3 variables.
Subject subject number 1–15
Marijuana total number of vomiting and retching episodes under marijuana treatment
Placebo total number of vomiting and retching episodes under placebo treatment
Source
Ramsey, F.L. and Schafer, D.W. (2013). The Statistical Sleuth: A Course in Methods of Data
Analysis (3rd ed), Cengage Learning.
References
Chang, A.E., Shiling, D.J., Stillman, R.C., Goldberg, N.H., Seipp, C.A., Barofsky, I., Simon, R.M.
and Rosenberg, S.A. (1979). Delta-9-Tetrahydrocannabinol as an Antiemetic in Cancer Patients
Receiving High Dose Methotrexate, Annals of Internal Medicine 91(6): 819–824.
Examples
str(ex0432)

ex0518

Fatty Acid

Description
A randomized experiment was performed to estimate the effect of a certain fatty acid CPFA on the
level of a certain protein in rat livers.
Usage
ex0518

92

ex0523

Format
A data frame with 30 observations on the following 4 variables.
Protein levels of protein (x 10) found in rat livers
Treatment a factor with levels "Control", "CPFA50", "CPFA150", "CPFA300", "CPFA450" and
"CPFA600"
Day a factor with levels "Day1", "Day2", "Day3", "Day4" and "Day5"
TrtDayGroup a factor with levels "Group1", "Group2", . . . , "Group10"; the observed levels of
the Treatment and Day interaction
Source
Ramsey, F.L. and Schafer, D.W. (2013). The Statistical Sleuth: A Course in Methods of Data
Analysis (3rd ed), Cengage Learning.
Examples
str(ex0518)

ex0523

Was Tyrannosaurus Rex Warm-Blooded?

Description
Data frame with measurements of oxygen isotopic composition of vertebrate bone phosphate (per
mil deviations from SMOW) in 12 bones of a singe Tyrannosaurus rex specimen
Usage
ex0523
Format
A data frame with 52 observations on the following 2 variables.
Oxygen oxygen isotopic composition
Bone a factor with levels "Bone1", "Bone2", . . . , "Bone12"
Source
Ramsey, F.L. and Schafer, D.W. (2013). The Statistical Sleuth: A Course in Methods of Data
Analysis (3rd ed), Cengage Learning.
References
Barrick, R.E. and Showers, W.J. (1994). Thermophysiology of Tyrannosaurus rex: Evidence from
Oxygen Isotopes, Science 265(5169): 222–224.
See Also
ex1120

ex0524

93

Examples
str(ex0523)

ex0524

IQ and Future Income

Description
These data are annual incomes in 2005 for 2,584 Americans who were selected in the National
Longitudinal Study of Youth 1979, who were available for re- interview in 2006, and who had
paying jobs in 2005, along with the quartile of their AFQT (IQ) test score taken in 1981. How
strong is the evidence that the distributions of 2005 annual incomes differ in the four populations?
By how many dollars or by what percent does the distribution of 2005 incomes for those within the
highest (fourth) quartile of IQ test scores exceed the distribution for the lowest (first) quartile?
Usage
ex0524
Format
A data frame with 2,584 observations on the following 3 variables.
Subject subject identification number
IQquartile a factor with levels "1stQuartile", "2ndQuartile", "3rdQuartile" and "4thQuartile"
Income2005 annual income in U.S. dollars, 2005
Source
Ramsey, F.L. and Schafer, D.W. (2013). The Statistical Sleuth: A Course in Methods of Data
Analysis (3rd ed), Cengage Learning.
References
National Longitudinal Survey of Youth, U.S. Bureau of Labor Statistics, http://www.bls.gov/
nls/home.htm (May 8, 2008).
See Also
ex0222, ex0330, ex0331, ex0525, ex0828, ex0923, ex1033, ex1223
Examples
str(ex0524)

94

ex0525

ex0525

IQ and Future Income

Description
These data are annual incomes in 2005 of a random sample of 2,584 Americans who were selected
for the National Longitudinal Survey of Youth in 1979 and who had paying jobs in 2005. The data
set also includes a code for the number of years of education that each individual had completed
by 2006: <12, 12, 13–15, 16, and >16. How strong is the evidence that at least one of the five
population distributions (corresponding to the different years of education) is different from the
others? By how many dollars or by what percent does the mean or median for each of the last four
categories exceed that of the next lowest category?
Usage
ex0525
Format
A data frame with 2,584 observations on the following 3 variables.
Subject subject identification number
Educ a factor for years of education category with levels "<12", "12", "13-15", "16" and ">16"
Income2005 Annual income in 2005, in U.S. dollars
Source
Ramsey, F.L. and Schafer, D.W. (2013). The Statistical Sleuth: A Course in Methods of Data
Analysis (3rd ed), Cengage Learning.
References
National Longitudinal Survey of Youth, U.S. Bureau of Labor Statistics, http://www.bls.gov/
nls/home.htm (May 8, 2008).
See Also
ex0222, ex0330, ex0331, ex0524, ex0828, ex0923, ex1033, ex1223
Examples
str(ex0525)

ex0623

ex0623

95

Diet Wars

Description
These data are simulated to match the summary and conclusions of a real study of overweight
employees who were randomly assigned to three diet groups: a low-fat diet, a low-carb diet (similar
to the Atkins diet), and a Mediterranean diet. The study ran for two years, with 272 employees
completing the entire protocol. Is there evidence of differences in average weight loss among these
diets? If so, which diets appear to be better than which others?
Usage
ex0623
Format
A data frame with 272 observations on the following 3 variables.
Subject subject identification number
Group a factor with levels "Low-Carbohydrate", "Low-Fat", and "Meditrranean"
WtLoss24 weight at the end of the 24 month study minus initial weight, in kg
Source
Ramsey, F.L. and Schafer, D.W. (2013). The Statistical Sleuth: A Course in Methods of Data
Analysis (3rd ed), Cengage Learning.
See Also
ex1420, ex1921, ex1922
Examples
str(ex0623)

ex0624

A Biological Basis for Homosexuality

Description
Is there a physiological basis for sexual preference? Researchers measured the volumes of four cell
groups in the interstitial nuclei of the anterior hypothalamus in postmortem tissue from 41 subjects
at autopsy from seven metropolitan hospitals in New York and California.
Usage
ex0624

96

ex0721

Format
A data frame with 41 observations on the following 5 variables.
Volume volumes of INAH3 (1000 × mm3 ) cell clusters from 41 humans
Group a factor with levels
"Group1"
"Group2"
"Group3"
"Group4"
"Group5"

heterosexual male with AIDS death
heterosexual male with Non-AIDS death
homosexual male with AIDS death
heterosexual female with AIDS death
heterosexual female with Non-AIDS death

Sex a factor with levels "Female" and "Male"
Orientation a factor with levels "Heterosexual" and "Homosexual"
Death a factor with levels "AIDS" and "Non-AIDS"
Source
Ramsey, F.L. and Schafer, D.W. (2013). The Statistical Sleuth: A Course in Methods of Data
Analysis (3rd ed), Cengage Learning.
References
LeVay, S. (1991). A Difference in Hypothalamic Structure Between Heterosexual and Homosexual
Men, Science 253(5023): 1034–1037.
Examples
str(ex0624)

ex0721

Planetary Distances and Order from the Sun

Description
The first three columns are the names, orders of distance from the sun and distances from the sun
(scaled so that earth is 1) of the 8 planets in our solar system and the dwarf planet, Pluto. The next
three columns are the same, but also include the asteroid belt.
Usage
ex0721
Format
A data frame with observations on the following 6 variables.
Name name of object in solar system, 9 objects
Order order of object’s distance from the sun
Distance distance of object from sun, with earth = 1

ex0722

97

Name2 name of object in solar system, including asteroid belt
Order2 order of object’s distance from the sun
Distance2 distance of object from sun, with earth = 1
Source
Ramsey, F.L. and Schafer, D.W. (2013). The Statistical Sleuth: A Course in Methods of Data
Analysis (3rd ed), Cengage Learning.
See Also
ex2226
Examples
str(ex0721)

ex0722

Crab Claw Size and Force

Description
As part of a study of the effects of predatory intertidal crab species on snail populations, researchers
measured the mean closing forces and the propdus heights of the claws on several crabs of three
species.
Usage
ex0722
Format
A data frame with 38 observations on the following 3 variables.
Force closing strength of claw of the crab
Height propodus height of claw of the crab
Species species to which the crab belongs
Source
Ramsey, F.L. and Schafer, D.W. (2013). The Statistical Sleuth: A Course in Methods of Data
Analysis (3rd ed), Cengage Learning.
References
Yamada, S.B. and Boulding, E.G. (1992). Claw Morphology, Prey Size Selection and Foraging Efficiency in Generalist and Specialist Shell-Breaking Crabs. Journal of Experimental Marine Biology
and Ecolog, 220 191–211.
Examples
str(ex0722)

98

ex0725

ex0724

Decline in Male Births

Description
The data are on the proportion of male birts in Denmark, The Netherlands, Canada and the United
States for a number of yeras. Notice that the proportions for Canada and the United States are only
provided for the years 1970 to 1990, while Denmark and The Netherlands have data listed for 1950
to 1994.
Usage
ex0724
Format
A data frame with 45 observations on the following 5 variables.
Year year of observation
Denmark male birth rate of Denmark for given year
Netherlands male birth rate of The Netherlands for given year
Canada male birth rate of Canada for given year
USA male birth rate of the United States for given year
Source
Ramsey, F.L. and Schafer, D.W. (2002). The Statistical Sleuth: A Course in Methods of Data
Analysis (2nd ed), Duxbury.
References
Davis, D.L., Gottlieb, M.B. and Stampnitzky, J.R. (1998). Reduced ratio of male to female births
in several industrial countries, Journal of the American Medical Association 279(13): 1018–1023.
Examples
str(ex0724)

ex0725

The Big Bang II

Description
These data are measured distances and recession velocities for 10 clusters of nebulae, much farther
from earth than the nebulae reported in case0701.
Usage
ex0727

ex0726

99

Format
A data frame with 10 observations on the following 2 variables.
Distance distance from earth (in million parsec)
Velocity recession velocity (in kilometres per second)
Source
Ramsey, F.L. and Schafer, D.W. (2013). The Statistical Sleuth: A Course in Methods of Data
Analysis (3rd ed), Cengage Learning.
References
Hubble, E. and Humason, M. (1931). The Velocity–Distance Relation Among Extra–calactic Nebulae, Astrophysics Journal 74: 43–50.
See Also
case0701
Examples
str(ex0725)

ex0726

Orign of the Term Regression

Description
These data are heights of 933 adults and their parents, as measured by Karl Pearson in 1885.
Usage
ex0726
Format
A data frame with 933 observations on the following 5 variables.
Gender a factor with levels "female" and "male"
Family an identification number for family, 1, 2,. . . , 205
Height adult height of the child, inches
Father height of the child’s father, inches
Mother height of the child’s mother, inches
Source
Ramsey, F.L. and Schafer, D.W. (2013). The Statistical Sleuth: A Course in Methods of Data
Analysis (3rd ed), Cengage Learning.

100

ex0727

References
Hubble, E. and Humason, M. (1931). The Velocity–Distance Relation Among Extra–calactic Nebulae, Astrophysics Journal 74: 43–50.
Examples
str(ex0725)

ex0727

Male Displays

Description
Black wheatears are small birds in Spain and Morocco. Males of the species demonstrate an exaggerated sexual display by carrying many heavy stones to nesting cavities. This 35–gram bird
transports, on average, 3.1 kg of stones per nesting season! Different males carry somewhat different sized stones, prompting a study on whether larger stones may be a signal of higher health status.
Soler et al. calculated the average stone mass (g) carried by each of 21 male black wheatears, along
with T-cell response measurements reflecting their immune systems’ strengths.
Usage
ex0727
Format
A data frame with 21 observations on the following 2 variables.
Mass average mass of stones carried by bird (in g)
Tcell T-cell response measurement (in mm)
Source
Ramsey, F.L. and Schafer, D.W. (2013). The Statistical Sleuth: A Course in Methods of Data
Analysis (3rd ed), Cengage Learning.
References
Soler, M., Martín-Vivaldi, M., Marín J. and Møller, A. (1999). Weight lifting and health status in
the black wheatears, Behavioral Ecology 10(3): 281–286.
Examples
str(ex0727)

ex0728

ex0728

101

Brain Activity in Violin and String Players

Description
Studies over the past two decades have shown that activity can effect the reorganisation of the human
central nervous system. For example, it is known that the part of the brain associated with activity
of a finger or limb is taken over for other purposes in individuals whose limb or finger has been lost.
In one study, psychologists used magnetic source imaging (MSI) to measure neuronal activity in the
brains of nine string players (six violinists, two cellists and one guitarist) and six controls who had
never played a musical instrument, when the thumb and fifth finger of the left hand were exposed
to mild stimulation. The researchers felt that stringed instrument players, who use the fingers of
their left hand extensively, might show different behaviour—as a result of this extensive physical
activity—than individuals who did not play stringed instruments.
Usage
ex0728
Format
A data frame with 15 observations on the following 2 variables.
Years years that the individual has been playing
Activity neuronal activity index
Source
Ramsey, F.L. and Schafer, D.W. (2013). The Statistical Sleuth: A Course in Methods of Data
Analysis (3rd ed), Cengage Learning.
References
Elbert, T., Pantev, C., Wienbruch, C., Rockstroh, B. and Taub E. (1995). Increased cortical representation of the fingers of the left hand in string players, Science 270(5234): 305–307.
Examples
str(ex0728)

ex0729

Sampling Bias in Exit Polls

Description
These data are the number of percentage points by which exit polls over estimated the actual vote
for candidate John Kerry in the 2004 U.S. presidential election, grouped according to the distance
of the exit poll interviewer from the door of the polling location. How strong is the evidence that
the mean Kerry overestimate increases with increasing distance of interviewer from the door (thus
lending evidence to the theory that supporters of the other candidate, George W Bush, were more
inclined to avoid exit pollsters)?

102

ex0730

Usage
ex0729
Format
A data frame with 6 observations on the following 2 variables.
OverEstimate number of percentage points by which the exit poll estimate exceeded the actual
percentage voting for Kerry (in all precincts with a similar distance of interviewer from the
door
Distance distance of the interviewer from the door of the polling location, in feet
Source
Ramsey, F.L. and Schafer, D.W. (2013). The Statistical Sleuth: A Course in Methods of Data
Analysis (3rd ed), Cengage Learning.
References
Evaluation of Edison/Mitofsky Election System 2004 prepared by Edison Media Research and
Mitofsky International for the National Election Pool (NEP), January 15, 2005. http://abcnews.
go.com/images/Politics/EvaluationofEdisonMitofskyElectionSystem.pdf
See Also
ex0730
Examples
str(ex0729)

ex0730

Sampling Bias in Exit Polls 2

Description
These data are the average proportion of voters refusing to be interviewed by exit pollsters in the
2004 U.S. presidential election, grouped gby age of the interviewer, and the approximate age of the
interviewer. What evidence do the data provide that the mean refusal rate decreased with incrasing
age of interviewer?
Usage
ex0730
Format
A data frame with 6 observations on the following 2 variables.
Age age of the exit poll interviewer, years
Refusal average proportion of voters refusing to be interviewed

ex0816

103

Source
Ramsey, F.L. and Schafer, D.W. (2013). The Statistical Sleuth: A Course in Methods of Data
Analysis (3rd ed), Cengage Learning.
References
Evaluation of Edison/Mitofsky Election System 2004 prepared by Edison Media Research and
Mitofsky International for the National Election Pool (NEP), January 15, 2005. http://abcnews.
go.com/images/Politics/EvaluationofEdisonMitofskyElectionSystem.pdf
See Also
ex0729
Examples
str(ex0730)

ex0816

Meat Processing

Description
The data in case0702 are a subset of the complete data on postmortum pH in 12 steer carcasses.
Usage
ex0816
Format
A data frame with 12 observations on the following 2 variables.
Time time after slaughter (hours)
pH pH level in postmortem muscle
Source
Ramsey, F.L. and Schafer, D.W. (2013). The Statistical Sleuth: A Course in Methods of Data
Analysis (3rd ed), Cengage Learning.
References
Schwenke, J.R. and Milliken, G.A. (1991). On the Calibration Problem Extended to Nonlinear
Models, Biometrics 47(2): 563–574.
See Also
case0702
Examples
str(ex0816)

104

ex0820

ex0817

Biological Pest Control

Description
In a study of the effectiveness of biological control of the exotic weed tansy ragwort, researchers
manipulated the exposure to the ragwort flea beetle on 15 plots that had been planted with a high
density of ragwort. Harvesting the plots the next season, they measured the average dry mass of
ragwort remaining (grams/plant) and the flea beetle load (beetles/gram of ragwort dry mass) to see
if the ragwort plants in plots with high flea beetle loads were smaller as a result of herbivory by the
beetles.
Usage
ex0817
Format
A data frame with 15 observations on the following 2 variables.
Load flee beetle load (in beetles/gram of ragwort dry mass)
Mass dry mass of ragwort weed
Source
Ramsey, F.L. and Schafer, D.W. (2013). The Statistical Sleuth: A Course in Methods of Data
Analysis (3rd ed), Cengage Learning.
References
McEvoy, P. and Cox, C. (1991). Successful Biological Control of Ragwort, Senecio jacobaea, by
introducing insects in Oregon, Ecological Applications 1(4): 430–442.
Examples
str(ex0817)

ex0820

Quantifying Evidence for Outlierness

Description
The data are Democratic and Republican vote counts, by (a) absentee ballot and (b) voting machine,
for 22 elections in Philadelphia’s senatorial districts between 1982 and 1993.
Usage
ex0820

ex0820

105

Format
A data frame with 22 observations on the following 2 variables.
Year Year of election
District a factor with levels "D1", "D2", "D3", "D4", "D5", "D7", and "D8"
DemAbsenteeVotes Number of absentee ballots indicating a vote for the Democratic candidate
RepubAbsenteeVotes Number of absentee ballots indicating a vote for the Republican candidate
DemMachineVotes Number of machine-counted ballots indicating a vote for the Democratic candidate
RepubMachineVotes Number of machine-coutned ballots indicating a vote for the Republican
candidate
DemPctOfAbsenteeVotes Percentage of absentee ballots indicating a vote for the Democratic candidate
DemPctOfMachineVotes Percentage of machine-counted ballots indicating a vote for the Democratic candidate
Disputed a factor taking on the value "yes" for the disputed election and "no" for all other elections
Details
In a special election to fill a Pennsylvania State Senate seat in 1993, the Democrat, William Stinson,
received 19,127 machine–counted votes and the Republican, Bruce Marks, received 19,691. In
addition, there were 1,391 absentee ballots for Stinson and 366 absentee ballots for Marks, so that
the total tally showed Stinson the winner by 461 votes. The large disparity between the machine–
counted and absentee votes, and the resulting reversal of the outcome due to the absentee ballots
caused some concern about possible illegal influence on the absentee votes. To see whether the
discrepancy in absentee votes was larger than could be explained by chance, an econometrician
considered the data given in this data frame (read from a graph in The New York Times, 11 April
1994).
Source
Ramsey, F.L. and Schafer, D.W. (2013). The Statistical Sleuth: A Course in Methods of Data
Analysis (3rd ed), Cengage Learning.
References
Ashenfelter, O (1994). Report on Expected Absentee Ballots. Department of Economics, Princeton
University. See also Simon Jackman (2011). pscl: Classes and Methods for R Developed in the
Political Science Computational Laboratory, Stanford University. Department of Political Science,
Stanford University. Stanford, California. R package version 1.03.10. http://pscl.stanford.edu/
Examples
str(ex0820)

106

ex0823

ex0822

Ecosystem Decay

Description
Data are the number of butterfly species in 16 islands of forest of various sizes in otherwise cleared
areas in Brazil.
Usage
ex0822
Format
A data frame with 16 observations on the following 2 variables.
Area area (ha) of forest patch
Species number of butterfly species
Source
Ramsey, F.L. and Schafer, D.W. (2013). The Statistical Sleuth: A Course in Methods of Data
Analysis (3rd ed), Cengage Learning.
References
Lovejoy, T.E., Rankin, J.M., Bierregaard, Jr., R.O., Brown, Jr., K.S., Emmons, L.H. and van der
Voort, M. (1984). Ecosystem decay of Amazon forest remnants in Nitecki, M.H. (ed.) Extinctions,
University of Chicago Press.
Examples
str(ex0822)

ex0823

Wine Consumption and Heart Disease

Description
The data are the average wine consumption rates (in liters per person per year) and number of
ischemic heart disease deaths (per 1000 men aged 55 to 64 years) for 18 industrialized countries.
Usage
data(ex0823)

ex0824

107

Format
A data frame with 18 observations on the following 3 variables.
Country a character vector indicating the country
Wine consumption of wine (liters per person per year)
Mortality heart disease mortality rate (deaths per 1,000)
Source
Ramsey, F.L. and Schafer, D.W. (2013). The Statistical Sleuth: A Course in Methods of Data
Analysis (3rd ed), Cengage Learning.
References
St. Leger A.S., Cochrane, A.L. and Moore, F. (1979). Factors Associated with Cardiac Mortality in
Developed Countries with Particular Reference to the Consumption of Wine, Lancet: 1017–1020.
Examples
str(ex0823)

ex0824

Respiratory Rates for Children

Description
A high respiratory rate is a potential diagnostic indicator of respiratory infection in children. To
judge whether a respiratory rate is “high” however, a physician must have a clear picture of the
distribution of normal rates. To this end, Italian researchers measured the respiratory rates of 618
children between the ages of 15 days and 3 years.
Usage
ex0824
Format
A data frame with 618 observations on the following 2 variables.
Age age in months of child
Rate respiratory rate (breaths per minute)
Source
Ramsey, F.L. and Schafer, D.W. (2013). The Statistical Sleuth: A Course in Methods of Data
Analysis (3rd ed), Cengage Learning.
References
Rusconi, F., Castagneto, M., Porta, N., Gagliardi, L., Leo, G., Pellegatta, A., Razon, S. and Braga,
M. (1994). Reference Values for Respiratory Rate in the First 3 Years of Life, Pediatrics 94(3):
350–355.

108

ex0826

Examples
str(ex0824)

ex0825

The Dramatic U.S. Presidential Election of 2000

Description
Data set shows the number of votes for Buchanan and Bush in all 67 counties in Florida during the
U.S. presidential election of November 7, 2000.
Usage
ex0825
Format
A data frame with 67 observations on the following 3 variables.
County a character vector indicating the county
Buchanan2000 votes cast for P. Buchanan
Bush2000 votes cast for G.W. Bush
Source
Ramsey, F.L. and Schafer, D.W. (2013). The Statistical Sleuth: A Course in Methods of Data
Analysis (3rd ed), Cengage Learning.
See Also
ex1222
Examples
str(ex0825)

ex0826

Kleiber’s Law

Description
The data are the average mass, metabolic rate, and lifespan for 95 species of mammals. Kleiber’s
law states that the metabolic rate of an animal species, on average, is proportional to its mass raised
to the power of 3/4.
Usage
ex0826

ex0828

109

Format
A data frame with 95 observations on the following 5 variables.
CommonName the common name of the mammal species
Species the scientific name of the mammal species
Mass the average body mass in kg
Metab the average metabolic rate in kJ per day
Life the average lifespan in years
Source
Ramsey, F.L. and Schafer, D.W. (2013). The Statistical Sleuth: A Course in Methods of Data
Analysis (3rd ed), Cengage Learning.
Examples
str(ex0826)

ex0828

IQ, Education, and Future Income

Description
These data are armed Forces Qualifying Test (AFQT) score percentiles, years of education, and
annual income in 2005 for a subset of a random sample of 2,584 Americans selected in 1979 who
were working in 2005 and re-interviewed in 2006.
Usage
ex0828
Format
A data frame with 2,584 observations on the following 4 variables.
Subject the subject identification number
AFQT percentile score on the AFQT test
Educ years of education achieved by 2005
Income2005 annual income in 2005
Source
Ramsey, F.L. and Schafer, D.W. (2013). The Statistical Sleuth: A Course in Methods of Data
Analysis (3rd ed), Cengage Learning.
References
National Longitudinal Survey of Youth, U.S. Bureau of Labor Statistics, http://www.bls.gov/
nls/home.htm (May 8, 2008).

110

ex0829

See Also
ex0222, ex0330, ex0331, ex0524, ex0525, ex0923, ex1033, ex1223
Examples
str(ex0828)

ex0829

Autism Rates

Description
These data are the prevalence of autism per 10,000 ten-year old children in the United States in
1992, 1994, 1996, 1998, and 2000.

Usage
ex0829
Format
A data frame with 5 observations on the following 2 variables.
Year year
Prevalence the number of autism cases per 10,000 ten-year old children

Source
Ramsey, F.L. and Schafer, D.W. (2013). The Statistical Sleuth: A Course in Methods of Data
Analysis (3rd ed), Cengage Learning.

References
Newschaffer, C. J., Falb, M. D. and Gurney, J. G. (2005) National Autism Prevalence Trends From
United States Special Education Data, Pediatrics 115: e277–e282.

Examples
str(ex0829)

ex0914

ex0914

111

Pace of Life and Heart Disease

Description
In four regions of the US (Northeast, Midwest, South and West), in three different sized metropolitan regions, researchers measured indicators of pace of life.
Usage
ex0914
Format
A data frame with 36 observations on the following 4 variables.
Bank bank clerk speed
Walk pedestrian walking speed
Talk postal clerk talking speed
Heart age adjusted death rate due to heart disease
Source
Ramsey, F.L. and Schafer, D.W. (2013). The Statistical Sleuth: A Course in Methods of Data
Analysis (3rd ed), Cengage Learning.
References
Levine, R.V. (1990). The Pace of Life, American Scientist 78: 450–459.
Examples
str(ex0914)

ex0915

Rainfall and Corn Yield

Description
Data on corn yield and rainfall in six U.S. corn–producing states (Iowa, Nebraska, Illinois, Indiana,
Missouri and Ohio), recorded for each year from 1890 to 1927.
Usage
ex0915

112

ex0918

Format
A data frame with 38 observations on the following 3 variables.
Year year of observation (1890–1927)
Yield average corn yield for the six states (in bu/acre)
Rainfall average rainfall in the six states (in in/year)
Source
Ramsey, F.L. and Schafer, D.W. (2013). The Statistical Sleuth: A Course in Methods of Data
Analysis (3rd ed), Cengage Learning.
References
Ezekiel, M. and Fox, K.A. (1959). Methods of Correlation and Regression Analysis, John Wiley &
Sons, New York.
Examples
str(ex0915)

ex0918

Speed of Evolution

Description
Researchers studied the development of a fly (Drosophila subobscura) that had been accidentally
introduced from the Old World into North America around 1980.
Usage
ex0918
Format
A data frame with 21 observations on the following 8 variables.
Continent a factor with levels "NA" and "EU"
Latitude latitude (degrees)
Females average wing size (103 ×log mm) of female flies on log scale
SE_Females standard error of wing size (103 ×log mm) of female flies on log scale
Males average wing size (103 ×log mm) of male flies on log scale
SE_Males standard error of wing size (103 ×log mm) of male flies on log scale
Ratio average basal length to wing size ratios of female flies
SE_Ratio standard error of average basal length to wing size ratio of female flies
Source
Ramsey, F.L. and Schafer, D.W. (2013). The Statistical Sleuth: A Course in Methods of Data
Analysis (3rd ed), Cengage Learning.

ex0920

113

References
Huey, R.B., Gilchrist, G.W., Carlson, M.L., Berrigan, D. and Serra, L. (2000). Rapid Evolution of
a Geographic Cline in Size in an Introduced Fly, Science 287(5451): 308–309.
Examples
str(ex0918)

ex0920

Winning Speeds at the Kentucky Derby

Description
Data set contains the year of the Kentucky Derby, the winning horse, the condition of the track and
the average speed of the winner for years 1896–2011.
Usage
ex0920
Format
A data frame with 116 observations on the following 8 variables.
Year year of Kentucky Derby
Winner a character vector with the name of the winning horse
Starters number of horses that started the race
NetToWinner the net winnings of the winner, in U.S. dollars
Time the winning time in seconds
Speed the winning average speed, n miles per hour
Track a factor indicating track condition with levels "Fast", "Good", "Dusty", "Slow", "Heavy",
"Muddy", and "Sloppy"
Conditions a factor with with 2 levels of track condition, with levels "Fast" and "Slow"
Source
Ramsey, F.L. and Schafer, D.W. (2013). The Statistical Sleuth: A Course in Methods of Data
Analysis (3rd ed), Cengage Learning.
References
Kentucky Derby: Kentucky Derby Racing Results.
Examples
str(ex0920)

114

ex0921

ex0921

Ingestion Rates of Deposit Feeders

Description
The data are the typical dry weight in mg, the typical ingestion rate (weight of food intake per day
for one animal) in mg/day, and the percentage of the food that is composed of organic matter for
19 species of deposit feeders. The goal is to see whether the distribution of species’ ingestion rates
depends on the percentage of organic matter in the food, after accounting for the effect of species
weight and to describe the association.
Usage
ex0922
Format
A data frame with 19 observations on the following 4 variables.
Species a character variable with the name of the species
Weight the dry weight of the species, in mg
Ingestion ingestion rate in mg per day
Organic percentage of organic matter in the food
Source
Ramsey, F.L. and Schafer, D.W. (2013). The Statistical Sleuth: A Course in Methods of Data
Analysis (3rd ed), Cengage Learning.
References
Cammen, L. M. (1980) Ingestion Rate: An Empirical Model for Aquatic Deposit Feeders and
Detritivores, Oecologia 44: 303–310.
See Also
ex1125
Examples
str(ex0921)

ex0923

ex0923

115

Comparing Male and Female Incomes, Accounting for Education and
IQ

Description
These data are a subset of the National Longitudinal Study of Youth data, with annual incomes in
2005, intelligence test scores (AFQT) measured in 1981, and years of education completed by 2006
for 1,306 males and 1,278 females who were between the ages of 14 and 22 when selected for the
survey in 1979, who were available for re-interview in 2006, and who had paying jobs in 2005.
Is there any evidence that the mean salary for males exceeds the mean salary for females with the
same years of education and AFQT scores? By how many dollars or by what percent is the male
mean larger?
Usage
ex0923
Format
A data frame with 2,584 observations on the following 5 variables.
Subject the subject identification number
Gender a factor with levels "female" and "male"
AFQT percentile score on the AFQT intelligence test
Educ years of education achieved by 2005
Income2005 annual income in 2005
Source
Ramsey, F.L. and Schafer, D.W. (2013). The Statistical Sleuth: A Course in Methods of Data
Analysis (3rd ed), Cengage Learning.
References
National Longitudinal Survey of Youth, U.S. Bureau of Labor Statistics, http://www.bls.gov/
nls/home.htm (May 8, 2008).
See Also
ex0222, ex0330, ex0331, ex0524, ex0525, ex0828, ex1033, ex1223
Examples
str(ex0923)

116

ex1026

ex1014

Toxic Effects of Copper and Zinc

Description
Researchers randomly allocated 25 beakers containing minnow larvae to receive one of 25 treatment
combinations of 5 levels of zinc and 5 levels of copper.
Usage
ex1014
Format
A data frame with 25 observations on the following 3 variables.
Copper amount of copper received (in ppm)
Zinc amount of zinc received (in ppm)
Protein protein in minnow larvae exposed to copper and zinc (µg/larva)
Source
Ramsey, F.L. and Schafer, D.W. (2013). The Statistical Sleuth: A Course in Methods of Data
Analysis (3rd ed), Cengage Learning.
References
Ryan, D.A., Hubert, J.J., Carter, E.M., Sprague, J.B. and Parrott, J. (1992). A Reduced-Rank
Multivariate Regression Approach to Aquatic Joint Toxicity Experiments, Biometrics 48(1): 155–
162.
Examples
str(ex1014)

ex1026

Thinning of Ozone Layer

Description
Depletion of the ozone layer allows the most damaging ultraviolet radiation to reach the Earth’s
surface. To measure the relationship, researchers sampled the ocean column at various depths at 17
locations around Antarctica during the austral spring of 1990.
Usage
ex1026

ex1027

117

Format
A data frame with 17 observations on the following 3 variables.
Inhibit percent inhibition of primary phytoplankton production in water
UVB UVB exposure
Surface a factor with levels "Deep" and "Surface"
Source
Ramsey, F.L. and Schafer, D.W. (2013). The Statistical Sleuth: A Course in Methods of Data
Analysis (3rd ed), Cengage Learning.
References
Smith, R.C., Prézelin, B.B., Baker, K.S., Bidigare, R.R., Boucher, N.P., Coley, T., Karentz, D., MacIntyre, S., Matlick, H.A., Menzies, D., Ondrusek, M., Wan, Z. and Waters, K.J. (1992). Ozone Depletion: Ultraviolet Radiation and Phytoplankton Biology in Antarctic Waters, Science 255(5047):
952–959.
Examples
str(ex1026)

ex1027

Factors Affecting Extinction

Description
Data are measurements on breeding pairs of land-bird species collected from 16 islands around
Britain over the course of several decades. For each species, the data set contains an average time
of extinction on those islands where it appeared, the average number of nesting pairs, the size of the
species and the migratory status of the species.
Usage
ex1027
Format
A data frame with 62 observations on the following 5 variables.
Species a character vector indicating the species
Time average extinction time in years
Pairs average number of nesting pairs
Size a factor with levels "L" and "S"
Status a factor with levels "M" and "R"
Source
Ramsey, F.L. and Schafer, D.W. (2013). The Statistical Sleuth: A Course in Methods of Data
Analysis (3rd ed), Cengage Learning.

118

ex1028

References
Pimm, S.L., Jones, H.L., and Diamond, J. (1988). On the Risk of Extinction, American Naturalist
132(6): 757–785.
Examples
str(ex1027)

ex1028

El Nino and Hurricanes

Description
Data set with the numbers of Atlantic Basin tropical storms and hurricanes for each year from 1950–
1997. The variable storm index is an index of overall intensity of hurricane season. Also listed are
whether the year was a cold, warm or neutral El Nino year and a variable indicating whether West
Africa was wet or dry that year.
Usage
ex1028
Format
A data frame with 48 observations on the following 7 variables.
Year year
ElNino a factor with levels "cold", "neutral" and "warm"
Temperature numeric variable with values -1 if ElNino is "cold", 0 if "neutral" and 1 if "warm"
WestAfrica numeric variable indicating whether West Africa was wet (1) or dry (0)
Storms number of storms
Hurricanes number of hurricanes
StormIndex index of overall intensity of hurricane season
Source
Ramsey, F.L. and Schafer, D.W. (2013). The Statistical Sleuth: A Course in Methods of Data
Analysis (3rd ed), Cengage Learning.
References
Data were gathered by William Gray of Colorado State University and reported on USA Today
weather page: http://www.usatoday.com/weather/whurnum.htm
Examples
str(ex1028)

ex1029

ex1029

119

Wage and Race

Description
Data set contains weekly wages in 1987 for a sample of 25,632 males between the age of 18 and 70
who worked full-time along with their years of education, years of experience, indicator variable
for whether they were black, indicator variable for whether they worked in or near a city, and a code
for the region in the US where they worked.
Usage
ex1029
Format
A data frame with 25,437 observations on the following 6 variables.
Region a factor with levels "Midwest", "Northeast", "South" and "West"
MetropolitanStatus a factor with levels "MetopolitanArea" and "NotMetropolitanArea"
Exper experience in years
Educ education in years
Race a factor with levels "Black" and "NotBlack"
WeeklyEarnings weekly wage in dollars
Source
Ramsey, F.L. and Schafer, D.W. (2013). The Statistical Sleuth: A Course in Methods of Data
Analysis (3rd ed), Cengage Learning.
References
Bierens, H.J. and Ginther, D.K. (2001). Integrated Conditional Moment Testing of Quantile Regression Models, Empirical Economics 26(1): 307–324; see
http://econ.la.psu.edu/~hbierens/QUANTILE.PDF
http://econ.la.psu.edu/~hbierens/MEDIAN.HTM
Examples
str(ex1029)

120

ex1030

ex1030

Wage and Race 2011

Description
A data set with weekly earnings for 4,952 males between the age of 18 and 70 sampled in the
March 2011 Current Population Survey (CPS). These males are a subset who had reported earnings
and who responded as having race as either “Only White” or “Only Black.” Also recorded are the
region of the country (with four categories: Northeast, Midwest, South, and West), the metropolitan
status of the men’s employment (with three categories: Metropolitan, Not Metropolitan, and Not
Identified), age, education category (with 16 categories ranging from “Less than first grade” to
“doctorate Degree”), and education code, which is a numerical value that corresponds roughly
to increasing levels of education (and so may be useful for plotting). What evidence do the data
provide that the distributions of weekly earnings differ in the populations of white and black workers
after accounting for the other variables? By how many dollars or by what percent does the White
population mean (or median) exceed the Black population mean (or median)?
Usage
ex1030
Format
A data frame with 4,952 observations on the following 7 variables.
Region a factor with levels "Midwest", "Northeast", "South" and "West"
MetropolitanStatus a factor with levels "Metopolitan", "Not Metropolitan " and "Not Identified"
Age age in years
EducationCategory a factor with 16 levels: "SomeCollegeButNoDegree", "AssocDegAcadem",
"NinthGrade", "BachelorsDegree", "TenthGrade", "HighSchoolDiploma",
"AssocDegOccupVocat", "DoctorateDegree", "TwelthButNoDiploma",
"LessThanFirstGrade", "EleventhGrade", "ProfSchoolDegree",
"FifthorSixthGrade","SeventhOrEighthGrade", "FirstSecondThirdOrFourthGrade"
EducationCode a numerical variable indicating the approximate ordering of EducationCategory,
with higher numbers indicating more education
Race a factor with levels "Black" and "White"
WeeklyEarnings weekly wage in dollars
Source
Ramsey, F.L. and Schafer, D.W. (2013). The Statistical Sleuth: A Course in Methods of Data
Analysis (3rd ed), Cengage Learning.
References
U.S. Bureau of Labor Statistics and U.S. Bureau of the Census: Current Population Survey, March
2011 http://www.bls.gov/cps
Examples
str(ex1030)

ex1031

ex1031

121

Who Looks After the Kids

Description
A data set with Clutch Volume (cubic milimeters) and adult Body Mass (kg) in six different groups
of animals: modern maternal-care bird species (Mat), modern paternal-care bird species (Pat), modern biparental-care bird species (BiPI), modern maternal-care crocodiles (Croc), non-avian maniraptoran dinosaurs thought to be ancestors of modern birds (Mani), and other non-avian dinosaurs
(Othr). The question of interest was which group of modern creatures most closely matches the
relationship in the maniraptoran dinosaurs.
Usage
ex1031
Format
A data frame with 443 observations on the following 6 variables.
CommonName the common name of the species
Genus species genus
Species species name
Group a factor with 6 levels corresponding to the 6 groups of animals: "BiP", "Croc", "Mani",
"Mat", "Othr", and "Pat"
BodyMass the average body mass of individuals in the species (kg)
ClutchVolume the total volume of all eggs in a clutch (average value for the species)
Source
Ramsey, F.L. and Schafer, D.W. (2013). The Statistical Sleuth: A Course in Methods of Data
Analysis (3rd ed), Cengage Learning.
References
Varricchio, D. J., Moore, J.r., Erickson, G.M., Norell, M.A., Jackon, F.D. and Borkowski, J.J. (2008)
Avian Paternal Care Had Dinosaur Origin Science 322: 1826–1828
See Also
ex1923
Examples
str(ex1031)

122

ex1033

ex1033

IQ Score and Income

Description
This is a subset of the National Longitudinal Study of Youth (NLSY79) data, with annual incomes
in 2005 (in U.S. dollars, as Recorded in a 2006 interview); scores on the Word Knowledge, Paragraph Comprehension, Arithmetic Reasoning, and Mathematics Knowledge portions of the Armed
Forces Vocational Aptitude Battery (ASVAB) of tests taken in 1981; and the percentile score of the
Armed Forces Qualifying Test (AFQT), which is a linear combination of the four component tests
mentioned above (but note that AFQT reported here is the percentile, which is not a linear combination of the four component scores). Which of the five test scores seem to be the most important
predictors of 2005 income? Is the AFQT sufficient by itself?
Usage
ex1033
Format
A data frame with 2,584 observations on the following 7 variables.
Subject the subject identification number
Arith score on the Arithmetic Reasoning test in 1981
Word score on the Word Knowledge Test in 1981
Parag score on the Paragraph Comprehension test in 1981
Math score on the Mathematics Knowledge test in 1981
AFQT percentile score on the AFQT intelligence test in 1981
Income2005 annual income in 2005
Source
Ramsey, F.L. and Schafer, D.W. (2013). The Statistical Sleuth: A Course in Methods of Data
Analysis (3rd ed), Cengage Learning.
References
National Longitudinal Survey of Youth, U.S. Bureau of Labor Statistics, http://www.bls.gov/
nls/home.htm (May 8, 2008).
See Also
ex0222, ex0330, ex0331, ex0524, ex0525, ex0828, ex0923, ex1223
Examples
str(ex1033)

ex1111

ex1111

123

Chernobyl Fallout

Description
The data are are the cesium concentrations (in Bq/kg) in soil and in mushrooms at 17 wooded
locations in Umbria, Central Italy, from August 1986 to November 1989. Researchers wished to
investigate the cesium transfer from contaminated soil to plants after the Chernobyl nuclear power
plant accident in April 1986 by describing the distribution of the mushroom concentration as a
function of soil concentration.
Usage
ex1111
Format
A data frame with 17 observations on the following 2 variables.
Mushroom the cesium concentration in mushrooms, Bq/kg
Soil the cesium concentration in soil, Bq/kg
Source
Ramsey, F.L. and Schafer, D.W. (2013). The Statistical Sleuth: A Course in Methods of Data
Analysis (3rd ed), Cengage Learning.
Examples
str(ex1111)

ex1120

Was Tyrannosaurus Rex Warm-Blooded?

Description
Data are the isotopic composition of structural bone carbonate (X) and the isotopic composition
of the coexisting calcite cements (Y ) in 18 bone samples from a specimen of the dinosaur Tyrannosaurus rex. Evidence that the mean of Y is positively associated with X was used in an argument
that the metabolic rate of this dinosaur resembled warm-blooded more than cold-blooded animals.
Usage
ex1120
Format
A data frame with 18 observations on the following 2 variables.
Carbonate isotopic composition of bone carbonate
Calcite isotopic composition of calcite cements

124

ex1122

Source
Ramsey, F.L. and Schafer, D.W. (2013). The Statistical Sleuth: A Course in Methods of Data
Analysis (3rd ed), Cengage Learning.
References
Barrick, R.E. and Showers, W.J. (1994). Thermophysiology of Tyrannosaurus rex: Evidence from
Oxygen Isotopes, Science 265(5169): 222–224.
See Also
ex0523
Examples
str(ex1120)

ex1122

Deforestation and Debt

Description
It has been theorized that developing countries cut down their forests to pay off foreign debt. Data
are debt, deforestation, and population from 11 Latin American nations.
Usage
ex1122
Format
A data frame with 11 observations on the following 4 variables.
Country a character vector indicating the country
Debt debt (millions of dollars)
Deforest deforestation (thousands of ha)
Pop population (thousands of people)
Source
Ramsey, F.L. and Schafer, D.W. (2013). The Statistical Sleuth: A Course in Methods of Data
Analysis (3rd ed), Cengage Learning.
References
Gullison, R.R. and Losos, E.C. (1992). The Role of Foreign Debt in Deforestation in Latin America,
Conservation Biology 7(1): 140–7.
Examples
str(ex1122)

ex1123

ex1123

125

Air Pollution and Mortality

Description
Does pollution kill people? Data in one early study designed to explore this issue from 5 Standard
Metropolitan Statistical Areas in the U.S between 1959–1961.
Usage
ex1123
Format
A data frame with 60 observations on the following 7 variables.
City a character vector indicating the city
Mort total age-adjusted mortality from all causes
Precip mean annual precipitation (inches)
Educ median number of school years completed for persons 25 years or older
NonWhite percentage of population that is nonwhite
NOX relative pollution potential of oxides of nitrogen
SO2 relative pollution potential of sulfur dioxide
Source
Ramsey, F.L. and Schafer, D.W. (2013). The Statistical Sleuth: A Course in Methods of Data
Analysis (3rd ed), Cengage Learning.
References
McDonald, G.C. and Ayers, J.A. (1978). Some Applications of the “Chernoff Faces”: A Technique
for Graphically Representing Multivariate Data in Wang, P.C.C. (ed.) Graphical Representation of
Multivariate Data, Academic Press.
See Also
ex1217
Examples
str(ex1123)

126

ex1125

ex1124

Natal Dispersal Distances of Mammals

Description
An assessment of the factors affecting dispersal distances is important for understanding population
spread, recolonization and gene flow which are central issues for conservation of many vertebrate
species. Researchers gathered data on body weight, diet type and maximum natal dispersal distance
for various animals.
Usage
ex1124
Format
A data frame with 64 observations on the following 4 variables.
Species a character vector indicating the species
BodyMass bodymass (kg)
MaxDist maximum dispersal distance (km)
Type a factor with levels "C", "H" and "O" indicating carnivore, herbivore, or omnivore
Source
Ramsey, F.L. and Schafer, D.W. (2013). The Statistical Sleuth: A Course in Methods of Data
Analysis (3rd ed), Cengage Learning.
References
Sutherland, G.D., Harestad, A.S., Price, K. and Lertzman, K.P. (2000). Scaling of Natal Dispersal
Distances in Terrestrial Birds and Mammals, Conservation Ecology 4(1): 16.
Examples
str(ex1124)

ex1125

Ingestion Rates of Deposit Feeders

Description
The data are the typical dry weight in mg, the typical ingestion rate (weight of food intake per day
for one animal) in mg/day, and the percentage of the food that is composed of organic matter for
22 species of deposit feeders. The goal is to see whether the distribution of species’ ingestion rates
depends on the percentage of organic matter in the food, after accounting for the effect of species
weight and to describe the association. The last three species happen to be Bivalves, so may behave
differently from the others.

ex1217

127

Usage
ex1125
Format
A data frame with 22 observations on the following 5 variables.
Species a character variable with the name of the species
Weight the dry weight of the species, in mg
Ingestion ingestion rate in mg per day
Organic percentage of organic matter in the food
Bivalve a factor with levels "no" and "yes" to indicate whether a species is a bivalve
Source
Ramsey, F.L. and Schafer, D.W. (2013). The Statistical Sleuth: A Course in Methods of Data
Analysis (3rd ed), Cengage Learning.
References
Cammen, L. M. (1980) Ingestion Rate: An Empirical Model for Aquatic Deposit Feeders and
Detritivores, Oecologia 44: 303–310.
See Also
ex0921
Examples
str(ex1125)

ex1217

Pollution and Mortality

Description
Complete data set for problem introduced in ex1123. Data from early study designed to explore the
relationship between air pollution and mortality.
Usage
ex1217

128

ex1217

Format
A data frame with 60 observations on the following 17 variables.
CITY a character vector indicating the city
Mortality total age-adjusted mortality from all causes
Precip mean annual precipitation (inches)
Humidity percent relative humidity (annual average at 1:00pm)
JanTemp mean January temperature (degrees F)
JulyTemp mean July temperature (degrees F)
Over65 percentage of the population aged 65 years or over
House population per household
Educ median number of school years completed for persons 25 years or older
Sound percentage of the housing that is sound with all facilities
Density population density (in persons per square mile of urbanized area)
NonWhite percentage of population that is nonwhite
WhiteCol percentage of employment in white collar occupations
Poor percentage of households with annual income under $3,000 in 1960
HC relative pollution potential of hydrocarbons
NOX relative pollution potential of oxides of nitrogen
SO2 relative pollution potential of sulfur dioxide
Source
Ramsey, F.L. and Schafer, D.W. (2013). The Statistical Sleuth: A Course in Methods of Data
Analysis (3rd ed), Cengage Learning.
References
McDonald, G.C. and Ayers, J.A. (1978). Some Applications of the “Chernoff Faces”: A Technique
for Graphically Representing Multivariate Data in Wang, P.C.C. (ed.) Graphical Representation of
Multivariate Data, Academic Press.
See Also
ex1123
Examples
str(ex1217)

ex1220

ex1220

129

Galapagos Islands

Description
The number of species on an island is known to be related to the island’s area. Of interest is what
other variables are also related to the number of species, after island area is accounted for, and
whether the answer differs for native and non native species.

Usage
ex1220
Format
A data frame with 30 observations on the following 8 variables.
Island a character vector indicating the island
Total total number of observed species
Native number of native species
Area area (km2 )
Elev elevation (m)
DistNear distance from nearest island (km)
DistSc distance from Santa Cruz (km)
AreaNear area of nearest island (km2 )

Source
Ramsey, F.L. and Schafer, D.W. (2013). The Statistical Sleuth: A Course in Methods of Data
Analysis (3rd ed), Cengage Learning.

References
Johnson, M.P. and Raven, P.H. (1973). Species Number and Endemism: The Galapagos Archipelago
Revisited, Science 179(4076): 893–895.

Examples
str(ex1220)

130

ex1221

ex1221

Predicting Desert Wildflower Blooms

Description
These data are monthly rainfalls from September to March and the subjectively rated quality of the
following spring wildflower display for each of a number of years at each of four desert locations
in the southwestern United States (Upland Sonoran Desert near Tucson, the lower Colorado River
Valley section of the Sonoran Desert, the Baja California region of the Sonoran Desert, and the
Mojave Desert). The quality of the display was judged subjectively with ordered rating categories
of poor, fair, good, great, and spectacular. The variable Score is numerical variable corresponding
to these ordered categories. A goal is to find an equation for predicting quality of wildflower blooms
from the rainfall variables.
Usage
ex1221
Format
A data frame with 122 observations on the following 12 variables.
Year year of observed wildflower season
Region a factor variable with 4 levels: "baja", "colorado", "mojave", and "upland"
Sep the September rainfall, in inches
Oct the October rainfall, in inches
Nov the November rainfall, in inches
Dec the December rainfall, in inches
Jan the January rainfall, in inches
Feb the February rainfall, in inches
Mar the March rainfall, in inches
Total the total rainfall from September through March, in inches
Rating a factor with a subjective assessment of the quality of wildflower bloom with levels "FAIR",
"GOOD", "GREAT", "POOR", and "SPECTACULAR"
Score a numerical variable corresponding to the order of rating categories, with Poor=0, Fair=1,
Good=2, Great=3, and Spectacular=4
Source
Ramsey, F.L. and Schafer, D.W. (2013). The Statistical Sleuth: A Course in Methods of Data
Analysis (3rd ed), Cengage Learning.
References
Arizona-Sonora Desert Museum, “Wildflower Flourishes and Flops: a 50–Year History,” www.
desertmuseum.org/programs/flw_wildflwrbloom.html (July 25, 2011).
Examples
str(ex1221)

ex1222

ex1222

131

Bush Gore Ballot Controversy

Description
This data set contains the vote counts by county in Florida for Buchanan and for four other presidential candidates in 2000, along with the total vote counts in 2000, the presidential vote counts
for three presidential candidates in 1996, the vote count for Buchanan in his only other campaign
in Florida—the 1996 Republican primary, the registration in Buchanan’s Reform party and the total
political party registration in the county.
Usage
ex1222
Format
A data frame with 67 observations on the following 13 variables.
County a character vector indicating the county
Buchanan2000 votes cast for Buchanan in 2000 presidential election
Gore2000 votes cast for Gore in 2000 presidential election
Bush2000 votes cast for Bush in 2000 presidential election
Nader2000 votes cast for Nader in 2000 presidential election
Browne2000 votes cast for Browne in 2000 presidential election
Total2000 total vostes cast in 2000 presidential election
Clinton96 votes cast for Clinton in 1996 presidential election
Dole96 votes cast for Dole in 1996 presidential election
Perot96 votes cast for Perot in 1996 presidential election
Buchanan96p votes cast for Buchanan in 1996 Republican primary
ReformReg the registration in Buchanan’s Reform party
TotalReg the total political party registration
Source
Ramsey, F.L. and Schafer, D.W. (2013). The Statistical Sleuth: A Course in Methods of Data
Analysis (3rd ed), Cengage Learning.
See Also
ex0825
Examples
str(ex1222)

132

ex1223

ex1223

IQ Score and Income

Description
This is a subset of 2,584 individuals from the 1979 National Longitudinal Study of Youth (NLSY79)
survey who were re-interviewed in 2006, who had paying jobs in 2005, and who had complete
values for the variables listed below. A goal is to see whether intelligence (as measured by the
ASVAB intelligence test score, AFQT, and its Components, Word, Parag, Math, and Arith) is a
better predictor of 2005 income than education and socioeconomic status.
Usage
ex1223
Format
A data frame with 2,584 observations on the following 32 variables.
Subject the subject identification number
Imagazine a variable taking on the value 1 if anyone in the respondent’s household regularly read
magazines in 1979, otherwise 0
Inewspaper a variable taking on the value 1 if anyone in the respondent’s household regularly read
newspapers in 1979, otherwise 0
Ilibrary a variable taking on the value 1 if anyone in the respondent’s household had a library card
in 1979, otherwise 0
MotherEd mother’s years of education
FatherEd father’s years of education
FamilyIncome78 family’s total net income in 1978
Race 1 = Hispanic, 2 = Black, 3 = Not Hispanic or Black
Gender a factor with levels "female" and "male"
Educ years of education completed by 2006
Science score on the General Science test in 1981
Arith score on the Arithmetic Reasoning test in 1981
Word score on the Word Knowledge Test in 1981
Parag score on the Paragraph Comprehension test in 1981
Numer score on the Numerical Operations test in 1981
Coding score on the Coding Speed test in 1981
Auto score on the Automotive and Shop test in 1981
Math score on the Mathematics Knowledge test in 1981
Mechanic score on the Electronics Information test in 1981
Elec score on the Paragraph Comprehension test in 1981
AFQT percentile score on the AFQT intelligence test in 1981
Income2005 total annual income in 2005

ex1225

133

Esteem1 self reported answer to 1st self esteem question, 2005
Esteem2 self reported answer to 2md self esteem question, 2005
Esteem3 self reported answer to 3rd self esteem question, 2005
Esteem4 self reported answer to 4th self esteem question, 2005
Esteem5 self reported answer to 5th self esteem question, 2005
Esteem6 self reported answer to 6th self esteem question, 2005
Esteem7 self reported answer to 7th self esteem question, 2005
Esteem8 self reported answer to 8th self esteem question, 2005
Esteem9 self reported answer to 9th self esteem question, 2005
Esteem10 self reported answer to 10th self esteem question, 2005
Source
Ramsey, F.L. and Schafer, D.W. (2013). The Statistical Sleuth: A Course in Methods of Data
Analysis (3rd ed), Cengage Learning.
References
National Longitudinal Survey of Youth, U.S. Bureau of Labor Statistics, http://www.bls.gov/
nls/home.htm (May 8, 2008).
See Also
ex0222, ex0330, ex0331, ex0524, ex0525, ex0828, ex0923, ex1033
Examples
str(ex1223)

ex1225

Gender Differences in Wages

Description
These data are weekly earnings for 9,835 Americans surveyed in the March 2011 Current Population Survey (CPS). What evidence is there from these data that males tend to receive higher earnings
than females with the same values of the other variables? By how many dollars or by what percent
does the male distribution exceed the female distribution?
Usage
ex1225

134

ex1317

Format
A data frame with 9,835 observations on the following 9 variables.
Region a factor with levels "Midwest", "Northeast", "South", and "West"
MetropolitanStatus a a factor with levels "Metropolitan", "Not Identified", and
"Not Metropolitan"
Age age in years
Sex a factor with levels "Female" and "Male"
MaritalStatus a factor with levels "Married" and "NotMarried"
EdCode a numerical variable representing educational attainment, with higher numbers corresponding to higher educational categories
Education a factor with 16 levels of educational category
JobClass a a factor with levels "FedGov", "LocalGov", "Private",and "StateGov"
WeeklyEarnings weekly wages in U.S. dollars
Source
Ramsey, F.L. and Schafer, D.W. (2013). The Statistical Sleuth: A Course in Methods of Data
Analysis (3rd ed), Cengage Learning.
References
U.S. Bureau of Labor Statistics and U.S. Bureau of the Census: Current Population Survey, March
2011 http://www.bls.gov/cps/data.htm July 25, 2011.
Examples
str(ex1225)

ex1317

Dinosaur Extinctions—An Observational Study

Description
About 65 million years ago, the dinosaurs suffered a mass extinction virtually overnight (in geologic
time). Among many clues, one that all scientists regard as crucial is a layer of iridium-rich dust that
was deposited over much of the earth at that time. The theory is that an event like a volcanic eruption
or meteor impact caused a massive dust cloud that blanketed the earth for years killing off animals
and their food sources. Dataset has Iridium depths by type of deposit.
Usage
ex1317
Format
A data frame with 28 observations on the following 3 variables.
Iridium Iridium in samples (ppt)
Strata a factor with levels "Limestone" and "Shale"
DepthCat a factor with six levels: "1", "2", . . . , "6"

ex1319

135

Source
Ramsey, F.L. and Schafer, D.W. (2013). The Statistical Sleuth: A Course in Methods of Data
Analysis (3rd ed), Cengage Learning.
References
Alvarez, W. and Asaro, F. (1990). What Caused the Mass Extinction? An Extraterrestrial Impact,
Scientific American 263(4): 76–84.
Courtillot, E. (1990). What Caused the Mass Extinction? A Volcanic Eruption. Scientific American
263(4): 85–92.
Examples
str(ex1317)

ex1319

Nature—Nurture

Description
A 1989 study investigated the effect of heredity and environment on intelligence. Data are the IQ
scores for adopted children whose biological and adoptive parents were categorized either in the
highest or the lowest socioeconomic status category.
Usage
ex1319
Format
A data frame with 38 observations on the following 3 variables.
IQ IQ scores of adopted children
Adoptive a factor with levels "High" and "Low"; the socioeconomic status of the adoptive parents
Biological a factor with levels "High" and "Low"; the socioeconomic status of the biological parents
Source
Ramsey, F.L. and Schafer, D.W. (2013). The Statistical Sleuth: A Course in Methods of Data
Analysis (3rd ed), Cengage Learning.
References
Capron, C. and Duyme, M. (1991). Children’s IQ’s and SES of Biological and Adoptive Parents in
a Balanced Cross-fostering Study, European Bulletin of Cognitive Psychology 11(3): 323–348.
See Also
ex1605

136

ex1320

Examples
str(ex1319)

ex1320

Gender Differences in Performance on Mathematics Achievement
Tests

Description
Data set on 861 ACT Assessment Mathematics Usage Test scores from 1987. The test was given to
a sample of high school seniors who met one of three profiles of high school mathematics course
work: (a) Algebra I only; (b) two Algebra courses and Geometry; and (c) two Algebra courses,
Geometry, Trigonometry, Advanced Mathematics and Beginning Calculus.
These data were generated from summary statistics for one particular form of the test as reported
by Doolittle (1989).
Usage
ex1320
Format
A data frame with 861 observations on the following 3 variables.
Sex a factor with levels "female" and "male"
Background a factor with levels "a", "b" and "c"
Score ACT mathematics test score
Source
Ramsey, F.L. and Schafer, D.W. (2013). The Statistical Sleuth: A Course in Methods of Data
Analysis (3rd ed), Cengage Learning.
References
Doolittle, A.E. (1989). Gender Differences in Performance on Mathematics Achievement Items,
Applied Measurement in Education 2(2): 161–177.
Examples
str(ex1320)

ex1321

ex1321

137

Pygmalion

Description
A data set simulated to match the summary statistics and conclusions from Rosenthal and Jacobson’s Pygmalion study on elementary school students. The researchers assigned students at random
to a pygmalion or control treatment group. They supplied information to the teachers of those in the
pygmalion group with the false information that an intelligence test had indicated that the student
was likely to excel. The researchers wished to see if the change in intelligence test scores for the
students tended to be larger for those students labeled as likely to excel.

Usage
ex1321
Format
A data frame with 320 observations on the following 5 variables.
Student a student identification number
Grade the student’s grade, 1 through 6
Class a factor with 17 levels "1a", "1b", and so on, to indicate the 17 distinct teacher/classrooms.
Treatment a factor with levels "pygmalion" and "control" corresponding to whether the researchers had told the teacher that the student was “likely to succeed” or not
Gain the intelligence test score taken at the end of the school year minus the intelligence test score
taken at the begging of the school year

Source
Ramsey, F.L. and Schafer, D.W. (2013). The Statistical Sleuth: A Course in Methods of Data
Analysis (3rd ed), Cengage Learning.

References
Rosenthal, R. and Jacobson, L. 1968, Pygmalion in the Classroom: Teacher Expectation and Pupil’s
Intellectual Development, Holt, Rinehart, and Winston, Inc.

Examples
str(ex1321)

138

ex1416

ex1416

Blood Brain Barrier

Description
Researchers designed an experiment to investigate how delivery of brain cancer antibody is influenced by tumor size, antibody molecular weight, blood-brain barrier disruption, and delivery route.
Usage
ex1416
Format
A data frame with 36 observations on the following 6 variables.
Agent a factor with levels "AIB", "DEX7" and "MTX"
Treatment a factor with levels "BD" and "NS"
Route a factor with levels "IA" and "IV"
DaysPost days after inoculation
BAT concentration of antibody in the part of the brain around the tumor
LH concentration of antibody in the unaffected part of the brain
Source
Ramsey, F.L. and Schafer, D.W. (2013). The Statistical Sleuth: A Course in Methods of Data
Analysis (3rd ed), Cengage Learning.
References
Barnett, P.A., Roman-Goldstain, S., Ramsey, F., McCormick, C.I., Sexton, G., Szumowski, J. and
Neuwelt, E.A. (1995). Differential Permeability and Quantitative MR Imaging of a Human Lung
Carcinoma Brain Xenograft in the Nude Rat, American Journal of Pathology 146(2): 436–449.
See Also
case1102, ex1417
Examples
str(ex1416)

ex1417

ex1417

139

Second Replicate of the Barrier Disruption Study

Description
Researchers designed an experiment to investigate how delivery of brain cancer antibody is influenced by tumor size, antibody molecular weight, blood-brain barrier disruption, and delivery route.
The data for the first replicate of this study is in ex1416. This is the second replicate for the study.
Usage
ex1417
Format
A data frame with 36 observations on the following 6 variables.
Agent a factor with levels "AIB", "DEX70" and "MTX"
Treatment a factor with levels "BD" and "NS"
Route a factor with levels "IA" and "IV"
DaysPost days after inoculation
BAT concentration of antibody in the part of the brain around the tumor
LH concentration of antibody in the unaffected part of the brain
Source
Ramsey, F.L. and Schafer, D.W. (2013). The Statistical Sleuth: A Course in Methods of Data
Analysis (3rd ed), Cengage Learning.
References
Barnett, P.A., Roman-Goldstain, S., Ramsey, F., McCormick, C.I., Sexton, G., Szumowski, J. and
Neuwelt, E.A. (1995). Differential Permeability and Quantitative MR Imaging of a Human Lung
Carcinoma Brain Xenograft in the Nude Rat, American Journal of Pathology 146(2): 436–449.
See Also
case1102, ex1416
Examples
str(ex1417)

140

ex1419

ex1419

Clever Hans Effect

Description
These data were simulated to match the summary statistics and conclusions of Rosenthal and Fode’s
Clever Hans experiment. Each of 12 students trained rats to run a maze. The data set contains their
number of successful runs out of 50 on each of 5 days. It also shows two summarizing statistics
for each student: the overall success rate on all 5 days and the slope in the least squares regression
of daily success rate (number of successes in a day divided by 50) on day. Also included are
the student’s response to the prior expectation of success question and the student’s response to
a post- experiment question about how relaxed they felt handling their rats (with higher values
corresponding to more relaxed). The treatment variable shows whether or not the students were
supplied with the fictitious information about whether their rats were bright or not.
Usage
ex1419
Format
A data frame with 12 observations on the following 12 variables.
Student a student identification number
PriorExp the student’s prior expectation of rat-training success, on a scale from -10 to 10
Block a numerical variable for pairs of students grouped according to their values of PriorExp
Treatment a factor with levels "bright" and "dull" corresponding to whether students were told
(falsely) that their rats were bright or not
Day1 the number of successful rat mazed runs on day 1
Day2 the number of successful rat mazed runs on day 2
Day3 the number of successful rat mazed runs on day 3
Day4 the number of successful rat mazed runs on day 4
Day5 the number of successful rat mazed runs on day 5
Relax degree of relaxation students felt in handling their rats, on a scale from 0 to 10
Success the total proportion of successful maze runs in 5 days
Slope the slope in the least squares regression of mean daily success as a function of day, estimated
for each student individually
Source
Ramsey, F.L. and Schafer, D.W. (2013). The Statistical Sleuth: A Course in Methods of Data
Analysis (3rd ed), Cengage Learning.
References
Rosenthal, R. and Fode, K.L. (1963) The Effect of Experimenter Bias on the Performance of the
Albino Rat Behavioral Science 8:3: 183–189.

ex1420

141

See Also
ex2120
Examples
str(ex1419)

ex1420

Diet Wars

Description
These data are the weight losses of subjects randomly assigned to one of three diets, and these
additional covariates sex, initial age, and body mass index. Is there any evidence from these data that
the mean weight loss differs for the different diets, after accounting for the effect of the covariates?
How big are the difference?
Usage
ex1420
Format
A data frame with 272 observations on the following 6 variables.
Subject a subject identification number
Diet a factor with levels "Low-Carbohydrate", "Low-Fat"and "Mediterranean"
Sex a factor with levels "F" and "M"
Age subject’s age in years
BMI body mass index in kg/squared meter
WtLoss24 weight at the end of the 24 month study minus initial weight, in kg
Source
Ramsey, F.L. and Schafer, D.W. (2013). The Statistical Sleuth: A Course in Methods of Data
Analysis (3rd ed), Cengage Learning.
See Also
ex0623, ex1921, ex1922
Examples
str(ex1420)

142

ex1509

ex1507

Global Warming, Southern Hemisphere

Description
The data are the temperatures (in degrees Celsius) averaged for the southern hemisphere over a
full year, for years 1850 to 2010. The 161-year average temperature has been subtracted, so each
observation is the temperature difference from the series average.
Usage
ex1507
Format
A data frame with 161 observations on the following 2 variables.
Year year in which yearly average temperature was computed, from 1850 to 2010
Temperature southern hemisphere temperature minus the 161-year average (degrees Celsius)
Source
Ramsey, F.L. and Schafer, D.W. (2013). The Statistical Sleuth: A Course in Methods of Data
Analysis (3rd ed), Cengage Learning.
References
Jones, P.D., D. E. Parker, T. J. Osborn, and K. R. Briffa, (2011) Global and Hemispheric Temperature Anomalies and and Marine Instrumental Records, CDIAC, http://cdiac.ornl.gov/
trends/temp/jonescru/jones.html, Aug 4, 2011
Examples
str(ex1507)

ex1509

Sunspots

Description
The data are the annual sunspot counts in each year from 1700 to 2010.
Usage
ex1507
Format
A data frame with 311 observations on the following 2 variables.
Year year in which sunspots were counted, from 1700 to 2010
Sunspots the number of sunspots observed in a year

ex1514

143

Source
Ramsey, F.L. and Schafer, D.W. (2013). The Statistical Sleuth: A Course in Methods of Data
Analysis (3rd ed), Cengage Learning.
References
SIDC-Solar Influences Dta Center, http://sidc.oma.be/sunspot-data/ (July 15, 2011).
Examples
str(ex1509)

ex1514

Melanoma and Sunspot Activity—An Observational Study

Description
Several factors suggest that the incidence of melanoma is related to solar radiation. These data
are the age-adjusted melanoma incidence among males in the Connecticut Tumor Registry and the
sunspot activity, 1936–1972 .
Usage
ex1514
Format
A data frame with 37 observations on the following 3 variables.
Year year
Melanoma male melanoma incidence in number of cases per 100,000 population
Sunspot sunspot relative number
Source
Ramsey, F.L. and Schafer, D.W. (2013). The Statistical Sleuth: A Course in Methods of Data
Analysis (3rd ed), Cengage Learning.
References
Houghton, A., Munster, E.W. and Viola, M.V. (1978). Increased Incidence of Malignant Melanoma
After Peaks of Sunspot Activity, Lancet: 759–760.
Examples
str(ex1514)

144

ex1516

ex1515

Lynx Trappings and Sunspots

Description
Data on the annual numbers of lynx trapped in the Mackenzie River district of northwest Canada
from 1821–1934.
Usage
ex1515
Format
A data frame with 114 observations on the following 3 variables.
Year year
Lynx number of lynx trapped
Sunspots number of sunspots
Source
Ramsey, F.L. and Schafer, D.W. (2013). The Statistical Sleuth: A Course in Methods of Data
Analysis (3rd ed), Cengage Learning.
References
Elston, C. and Nicholson, M. (1942). The Ten Year Cycle in Numbers of the Lynx in Canada,
Journal of Animal Ecology 11(2): 215–244.
Examples
str(ex1515)

ex1516

Trends in Firearm and Motor Vehicle Deaths in the U.S.

Description
Data shows the number of deaths due to firearms and the number of deaths due to motor vehicle
accidents in the United States between 1968 and 1993.
Usage
ex1516

ex1517

145

Format
A data frame with 26 observations on the following 3 variables.
Year year
FirearmDeaths deaths due to firearms (in thousands per year)
MotorVehicleDeaths deaths due to motor vehicles (in thousands per year)
Source
Ramsey, F.L. and Schafer, D.W. (2013). The Statistical Sleuth: A Course in Methods of Data
Analysis (3rd ed), Cengage Learning.
References
Data read from a Centers for Disease Control and Prevention graph reported in The Oregonian, June
17, 1997.
Examples
str(ex1516)

ex1517

S&P 500

Description
The Standard and Poor’s 500 stock index (S&P 500) is a benchmark of stock market performance,
based on 400 industrial firms, 40 financial stocks, 40 utilities, and 20 transportation stocks. These
data include the value of a $1 investment in 1871 at the end of each year from 1871 to 1999,
according to the S&P 500, assuming all dividends are reinvested. Describe the distribution of the
S&P value as a function of year.
Usage
ex1517
Format
A data frame with 129 observations on the following 2 variables.
Year year
S.P500Return Value of Stock at the end of the year
Source
Ramsey, F.L. and Schafer, D.W. (2013). The Statistical Sleuth: A Course in Methods of Data
Analysis (3rd ed), Cengage Learning.
Examples
str(ex1517)

146

ex1519

ex1518

Effectiveness of Measles Vaccine

Description
The data are the number of measles cases reported in the United States for each year from 1950 to
2008. A goal is to explore the effect of the introduction of the measles vaccine in 1963 on the series
mean.
Usage
ex1518
Format
A data frame with 59 observations on the following 3 variables.
Year year
Cases number of measles cases
Vaccine a factor with levels "no" and "yes" indicating whether the measles vaccine had been
licensed or not (yes for every year starting with 1963)
Source
Ramsey, F.L. and Schafer, D.W. (2013). The Statistical Sleuth: A Course in Methods of Data
Analysis (3rd ed), Cengage Learning.
References
Center for Disease Control,
http://www.cdc.gov/vaccines/pubs/pinkbook/downloads/appendices/G/cases-deaths.pdf
retrieved on July 23, 2009
Examples
str(ex1518)

ex1519

El Nino and the Southern Oscillation

Description
The data are the Sea Surface Temperatures (SST) and Southern Oscillation Index (SOI) measurements from 1950 to 2010.
Usage
ex1519

ex1605

147

Format
A data frame with 732 observations on the following 4 variables.
Year year
Month a numerical variable for month, with 1 = January
SOI the Southern Oscillation Index)
SST the Sea Surfact Temperature)
Source
Ramsey, F.L. and Schafer, D.W. (2013). The Statistical Sleuth: A Course in Methods of Data
Analysis (3rd ed), Cengage Learning.
References
U.S. National Oceanographic and Atmospheric Administration (http://www.cpc.ncep.noaa.gov/
data/indices/).
See Also
case1502
Examples
str(ex1519)

ex1605

Nature—Nurture

Description
Data are a subset from an observational, longitudinal, study on adopted children. Is child’s intelligence related to intelligence of the biological mother and the intelligence of the adoptive mother?
Usage
ex1605
Format
A data frame with 62 observations on the following 6 variables.
FMED adoptive (foster) mother’s years of education
TMIQ biological mother’s score on IQ test
Age2IQ IQ of child at age 2
Age4IQ IQ of child at age 4
Age8IQ IQ of child at age 8
Age13IQ IQ of child at age 13

148

ex1611

Source
Ramsey, F.L. and Schafer, D.W. (2013). The Statistical Sleuth: A Course in Methods of Data
Analysis (3rd ed), Cengage Learning.
References
Skodak, M. and Skeels, H.M. (1949). A Final Follow-up Study of One Hundred Adopted Children,
Journal of Genetic Psychology 75: 85–125.
See Also
ex1319
Examples
str(ex1605)

ex1611

Religious Competition

Description
Adam Smith, in Wealth of Nations, observed that even religious monopolies become weak when
they are not challenged by competition. Data to illustrate this point is from 21 countries in which
the percentages of Catholics in the populations varied from a low 1.2% to a high 97.6%.
Usage
ex1611
Format
A data frame with 21 observations on the following 4 variables.
Country a character vector indicating the country
PctCatholic percent Catholics in the population
PriestParishRatio priest to parishioner ratio
PctIndigenous percent clergy indigenous
Source
Ramsey, F.L. and Schafer, D.W. (2013). The Statistical Sleuth: A Course in Methods of Data
Analysis (3rd ed), Cengage Learning.
References
Gill, A.J. (1994). Rendering unto Caesar? Religious Competition and Catholic Political Strategy in
Latin America, 1962–79, American Journal of Political Science 38(2): 403–425.
Examples
str(ex1611)

ex1612

ex1612

149

Wastewater

Description
Samples of effluent were divided and sent to two laboratories for testing. Data are measurements of
biochemical oxygen demand and suspended solid measurements obtained for 2 sample splits from
the two laboratories.
Usage
ex1612
Format
A data frame with 11 observations on the following 4 variables.
ComBOD biochemical oxygen demand measurements from commercial laboratory
ComSS suspended solids measurements from commercial laboratory
StaBOD biochemical oxygen demand measurements from state laboratory
StaSS suspended solids measurements from state laboratory
Source
Ramsey, F.L. and Schafer, D.W. (2013). The Statistical Sleuth: A Course in Methods of Data
Analysis (3rd ed), Cengage Learning.
References
Johnson, R.A. and Wichern, D.W. (1988). Applied Multivariate Statistical Analysis, Prentice-Hall.
Examples
str(ex1612)

ex1613

Flea Beetle Distinction

Description
Data are the measurements from two very similar species of flea beetle.
Usage
ex1613

150

ex1614

Format
A data frame with 36 observations on the following 4 variables.
Specimen specimen identification number
Jnt1 measurement of first joint in micrometers
Jnt2 measurement of second joint in micrometers
Species a factor with levels "conc" and "heik"
Source
Ramsey, F.L. and Schafer, D.W. (2013). The Statistical Sleuth: A Course in Methods of Data
Analysis (3rd ed), Cengage Learning.
References
Lubischew, A.A. (1962). On the Use of Discriminant Functions in Taxonomy, Biometrics 18: 455–
477.
Examples
str(ex1613)

ex1614

Pschoimmunology

Description
Recent studies in the field of psychoimmunology suggest a link exists between behavioral events
and the functioning of one’s immune system. Data shows the results of a study on 12 subjects who
were monitored during three distinct activities. The first activity consisted of neutral activity such
as reporting tasks. During the second activity, subjects listened to audiotape exercises relating to
images of heaviness, warmth in the body, relaxation, suggestions to remember happy events, etc.
The third activity included a nonaudio tape follow up stimulus consisting of continued relaxation as
in activity 2 and a verbal discussion of the positive aspects of the audiotape.
Usage
ex1614
Format
A data frame with 12 observations on the following 3 variables.
Subject subject identification number
PhaseA Interleukin-1 levels (counts per minute) from blood samples taken during activity A
PhaseB Interleukin-1 levels (counts per minute) from blood samples taken during activity B
PhaseC Interleukin-1 levels (counts per minute) from blood samples taken during activity C

ex1615

151

Source
Ramsey, F.L. and Schafer, D.W. (2013). The Statistical Sleuth: A Course in Methods of Data
Analysis (3rd ed), Cengage Learning.
References
Keppel, W. (1993). Effects of Behavioral Stimuli on Plasma Interleukin-1 Activity in Humans at
Rest, Journal of Clinical Psychology 49(6): 777–785.
Examples
str(ex1614)

ex1615

Trends in SAT Scores

Description
Data shows a partial listing of a data set with ratios of average math to average verbal SAT scores
in the United States and the District of Columbia for 1989 and 1996–1999.
Usage
ex1615
Format
A data frame with 51 observations on the following 6 variables.
State a character vector indicating the state
M.V.89 average MATH SAT scores divided by average VERBAL SAT score in 1989
M.V.96 average MATH SAT scores divided by average VERBAL SAT score in 1996
M.V.97 average MATH SAT scores divided by average VERBAL SAT score in 1997
M.V.98 average MATH SAT scores divided by average VERBAL SAT score in 1998
M.V.99 average MATH SAT scores divided by average VERBAL SAT score in 1999
Source
Ramsey, F.L. and Schafer, D.W. (2013). The Statistical Sleuth: A Course in Methods of Data
Analysis (3rd ed), Cengage Learning.
Examples
str(ex1615)

152

ex1708

ex1620

Differential Gene Expression with RNA Sequencing

Description
In an experiment to identify genes of the plant Arabidopsis that react to a particular pathogen,
researchers used RNA sequencing to produce gene profiles for a number of plants not subjected to
the pathogen and several plants subjected to the pathogen. Tests comparing the distribution of gene
expression in the two groups were performed for each gene individually. The data are the p-values
from all these tests. The goal is to use a identify a set of genes that differentially express in the two
groups, subject to some specified value for expected false discovery rate, such as 5%.
Usage
ex1620
Format
A data frame with 20,245 observations on the following 3 variables.
Gene an identification number for genes
GeneName a character variable with the name of the gene
pValue the p-value from a test that the mean expression level for the gene differs in the two groups
(ignoring multiple testing)
Source
Ramsey, F.L. and Schafer, D.W. (2013). The Statistical Sleuth: A Course in Methods of Data
Analysis (3rd ed), Cengage Learning.
References
Chang, J, Department of Botany and Plant Pathology, Oregon State University, personnal communication.
Examples
str(ex1620)

ex1708

Pig Fat

Description
Actual pig fat and measurements of pig fat from magnetic resonance images at 13 locations for 12
pigs.
Usage
ex1708

ex1715

153

Format
A data frame with 12 observations on the following 14 variables.
Fat actual pig fat (in percent)
M1 magnetic resonance image at location 1
M2 magnetic resonance image at location 2
M3 magnetic resonance image at location 3
M4 magnetic resonance image at location 4
M5 magnetic resonance image at location 5
M6 magnetic resonance image at location 6
M7 magnetic resonance image at location 7
M8 magnetic resonance image at location 8
M9 magnetic resonance image at location 9
M10 magnetic resonance image at location 10
M11 magnetic resonance image at location 11
M12 magnetic resonance image at location 12
M13 magnetic resonance image at location 13
Source
Ramsey, F.L. and Schafer, D.W. (2013). The Statistical Sleuth: A Course in Methods of Data
Analysis (3rd ed), Cengage Learning.
References
Glasbey, C.A and Flowler, P.A. (1992). Regression Models Fitted Using Conditional Independence
to Estimate Pig Fatness from Magnetic Resonance Images, The Statistician 41(2): 179–184.
Examples
str(ex1708)

ex1715

Church Distinctiveness

Description
Data show measures that differ among denominations of American Protestant and Catholic churches.
Usage
ex1715

154

ex1716

Format
A data frame with 18 observations on the following 6 variables.
Denomination a character vector indicating the church denomination
Distinct distinctiveness (strictness of discipline on a seven point scale)
Attend average percentage of weeks that individuals attended a church meeting (% weekly)
NonChurch average number of secular organisations to which members belong
PctStrong average percentage of members that describe themselves as being strong church members (%)
AnnInc average income of members (US$)
Source
Ramsey, F.L. and Schafer, D.W. (2013). The Statistical Sleuth: A Course in Methods of Data
Analysis (3rd ed), Cengage Learning.
References
Iannaccone, L.R. (1994). Why Strict Churches Are Strong, American Journal of Sociology 99(5):
1180–1211.
Examples
str(ex1715)

ex1716

Insurance

Description
In the 1970’s the U.S. Commission on Civil Rights investigated charges that insurance companies
were attempting to redefine Chicago “neighborhoods” in order to cancel existing homeowner insurance policies or refuse to issue new ones. Dataset has data on homeowner and residential fire
insurance policy issuances from 47 zip codes in the Chicago area.
Usage
ex1716
Format
A data frame with 47 observations on the following 8 variables.
ZIP last 2 digits of zip code
Fire fires per 1000 housing units
Theft thefts per 1000 population
Age percentage of housing units built prior to 1940
Income median family income
Race percentage minority
Vol number of new policies per 100 housing units
Invol number of FAIR plan policies and renewals per 100 housing units

ex1914

155

Source
Ramsey, F.L. and Schafer, D.W. (2013). The Statistical Sleuth: A Course in Methods of Data
Analysis (3rd ed), Cengage Learning.
References
Andrews, D.F. and Herzberg, A.M. (1985). Data: A Collection of Problems from many Fields for
the Student and Research Worker, Springer-Verlag.
Examples
str(ex1716)

ex1914

Mantel-Haenszel Test for Censored survival Times: Lymphoma and
Radiation Data

Description
Survival times for two groups of lymphoma patients.
Usage
ex1914
Format
A data frame with 34 observations on the following 4 variables.
Months months after diagnosis
Group a factor with levels "no" and "radiation"
Survived number of patients known to survive beyond this month
Died number of patients known to die after this many months
Source
Ramsey, F.L. and Schafer, D.W. (2013). The Statistical Sleuth: A Course in Methods of Data
Analysis (3rd ed), Cengage Learning.
References
Neuwelt, E.A., Goldman, D.L., Dahlborg, S.A., Crossen, J., Ramsey, F., Roman-Goldstein, S.,
Braziel, R. and Dana, B. (1991). Primary CNS Lymphoma Treated with Osmotic Blood-brain
Barrier Disruption: Prolonged Survival and Preservation of Cognitive Function, Journal of Clinical
Oncology 9(9): 1580–1590.
Examples
str(ex1914)

156

ex1917

ex1916

Vitamin C and Colds

Description
Fictitious data set based on results of an experiment where subjects were randomly divided into
two groups and given a placebo or vitamin c to take during the cold season. At the end of the cold
season, the subjects were interviewed by a physician who determined whether they had or had not
suffered a cold during the period. Skeptics interviewed the 800 subjects to determine who knew and
who did not know to which group they had been assigned. Vitamin C has a bitter taste and those
familiar with it could recognize whether their pills contained it.
Usage
ex1916
Format
A data frame with 4 observations on the following 4 variables.
Knew a factor with levels "no" and "yes"
Treatment a factor with levels "placebo" and "vitC"
Cold number of people who got a cold
NoCold number of people who did not get a cold
Source
Ramsey, F.L. and Schafer, D.W. (2013). The Statistical Sleuth: A Course in Methods of Data
Analysis (3rd ed), Cengage Learning.
Examples
str(ex1916)

ex1917

Alcohol Consumption and Breast Cancer—A Retrospective Study

Description
Dataset from a study which investigated the added risk of breast cancer due to alcohol consumption.
A sample of confirmed breast cancer patients were compared with a sample of cancer free women
who were close in age and from the same neighborhood as the cases. Data was collected on the
alcohol consumption and body mass of both sets of women.
Usage
ex1917

ex1918

157

Format
A data frame with 6 observations on the following 4 variables.
BodyMass a factor with levels "high", "low" and "medium"
Drinking a factor with levels "high" and "low"
Cases number of women with breast cancer
Controls number of women without breast cancer
Source
Ramsey, F.L. and Schafer, D.W. (2013). The Statistical Sleuth: A Course in Methods of Data
Analysis (3rd ed), Cengage Learning.
References
Rosenberg, L., Palmer, J.R., Miller, D.R., Clarke, E.A. and Shapiro, S. (1990). A Case-Control
Study of Alcoholic Beverage Consumption and Breast Cancer, American Journal of Epidemiology
131(1): 6–14.
Examples
str(ex1917)

ex1918

The Donner Party

Description
In 1846 the Donner party became stranded while crossing the Sierra Nevada Mountains near Lake
Tahoe. The data frame has the counts for male and female survivors for six age groups.
Usage
ex1918
Format
A data frame with 12 observations on the following 4 variables.
AgeCat a numerical code corresponding to six age categories, with 1 = "15-19", 2 = "20-29", 3
= "30-39", 4 = "40-49", 5 = "50-59" and 6 = "60-69"
Sex a factor with levels "female" and "male"
Lived number that lived
Died number that died
Source
Ramsey, F.L. and Schafer, D.W. (2013). The Statistical Sleuth: A Course in Methods of Data
Analysis (3rd ed), Cengage Learning.

158

ex1919

References
Grayson, D.K. (1990). Donner Party Deaths: A Demographic Assessment, Journal of Anthropological Research 46: 223–242.
See Also
case2001
Examples
str(ex1918)

ex1919

Tire-Related Fatal Accidents and Ford Sports Utility Vehicles

Description
Data shows the numbers of compact sports utility vehicles involved in fatal accidents in the U.S.
between 1995 and 1999, categorized according to travel speed, make of car (Ford or other), and
cause of accident (tire-related or other).
Usage
ex1919
Format
A data frame with 8 observations on the following 4 variables.
SpeedCat a numerical code corresponding to 4 categories of speed (in miles per hour), with 1 =
"0-40", 2 = "41-55", 3 = "56-65" and 4 = ">65"
Make a factor with levels "Ford" and "Other"
Other cause of accident was other than tire-related
Tire cause of accident was tire-related
Source
Ramsey, F.L. and Schafer, D.W. (2013). The Statistical Sleuth: A Course in Methods of Data
Analysis (3rd ed), Cengage Learning.
See Also
ex2018
Examples
str(ex1919)

ex1921

ex1921

159

Diet Wars II

Description
In the study of different diets for losing weight (ex0623, ex1420 and ex1922), there appear to have
been many more experimental subjects that dropped out from the low carbohydrate diet group than
from the other two diet groups. This data set contains the numbers who did and didn’t drop out in
each diet group. Is there any evidence that the drop out rate differs in the three groups?
Usage
ex1921
Format
A data frame with 3 observations on the following 4 variables.
Diet a factor with levels "LowCarb", "LowFat", and "Medit"
DroppedOut the number of subjects who dropped out of the study
Completed the number of subjects who completed the study
Source
Ramsey, F.L. and Schafer, D.W. (2013). The Statistical Sleuth: A Course in Methods of Data
Analysis (3rd ed), Cengage Learning.
See Also
ex0623, ex1420, ex1922
Examples
str(ex1921)

ex1922

Diet Wars III

Description
For the study of different diets for losing weight (ex0623, ex1420 and ex1921), it is desired to see
whether women were more or less likely to drop out of the study than men (after accounting for the
apparent differential drop out rates associated with diet). This data set includes the numbers that
dropped out and completed the study for each combination of Sex and Diet.
Usage
ex1922

160

ex1923

Format
A data frame with 6 observations on the following 4 variables.
Diet a factor with levels "LowCarb", "LowFat", and "Medit"
Gender a factor with levels "Men" and "Women"
DroppedOut the number of subjects who dropped out of the study
Completed the number of subjects who completed the study
Source
Ramsey, F.L. and Schafer, D.W. (2013). The Statistical Sleuth: A Course in Methods of Data
Analysis (3rd ed), Cengage Learning.
See Also
ex0623, ex1420, ex1921
Examples
str(ex1922)

ex1923

Who Looks After the Kids?

Description
One issue concerning the validity of the clutch volume and parental care study of ex1031 is the
selection of the bird species in the set of currently living animals. Was the selection just as good as
a random sample of species from each of the groups? One way to study this for birds, at least, is to
compare the numbers of species from each of the 29 orders of birds in the study with the known total
number of species in each of the orders. If the selection of birds had been at random, the expected
proportion of species in the study from one particular order, n, is the proportion of all species in
that order (N=9,866) times the total number of species in the sample (414). That is, the expected
number in each sample, if random sampling were used, is (N/9,866)x 414. Calculate the expected
numbers and compare the observed numbers with them using Pearson’s chi-square statistic.
Usage
ex1923
Format
A data frame with 29 observations on the following 3 variables.
Order a character variable with the name of the order
N the known number of species in the order
n the number of sampled species from the order
Source
Ramsey, F.L. and Schafer, D.W. (2013). The Statistical Sleuth: A Course in Methods of Data
Analysis (3rd ed), Cengage Learning.

ex2011

161

See Also
ex1031
Examples
str(ex1923)

ex2011

Space Shuttle

Description
This data frame contains the launch temperatures (degrees Fahrenheit) and an indicator of O-ring
failures for 24 space shuttle launches prior to the space shuttle Challenger disaster of January 28,
1986.

Usage
ex2011
Format
A data frame with 24 observations on the following 2 variables.
Temperature Launch temperature (in degrees Fahrenheit)
Failure Indicator of O-ring failure

Source
Ramsey, F.L. and Schafer, D.W. (2013). The Statistical Sleuth: A Course in Methods of Data
Analysis (3rd ed), Cengage Learning.

See Also
case0401, ex2223
Examples
str(ex2011)

162

ex2012

ex2012

Muscular Dystrophy

Description
Duchenne Muscular Dystrophy (DMD) is a genetically transmitted disease, passed from a mother
to her children. Boys with the disease usually die at a young age; but affected girls usually do
not suffer symptoms, may unknowingly carry the disease and may pass it to their offspring. It is
believed that about 1 in 3,300 women are DMD carriers. A woman might suspect she is a carrier
when a related male child develops the disease. Doctors must rely on some kind of test to detect
the presence of the disease. This data frame contains data on two enzymes in the blood, creatine
kinase (CK) and hemopexin (H) for 38 known DMD carriers and 82 women who are not carriers. It
is desired to use these data to obtain an equation for indicating whether a women is a likely carrier.

Usage
ex2012

Format
A data frame with 120 observations on the following 3 variables.
Group Indicator whether the woman has DMD ("Case") or not ("Control")
CK Creatine kinase reading
H Hemopexin reading

Source
Ramsey, F.L. and Schafer, D.W. (2013). The Statistical Sleuth: A Course in Methods of Data
Analysis (3rd ed), Cengage Learning.

References
Andrews, D.F. and Herzberg, A.M. (1985). Data: A Collection of Problems From Many Fields For
The Student And Research Worker, Springer-Verlag, New York.

Examples
str(ex2012)

ex2015

ex2015

163

Spotted Owl Habitat

Description
A study examined the association between nesting locations of the Northern Spotted Owl and availability of mature forests. Wildlife biologists identified 30 nest sites. The researchers selected 30
other sites at random coordinates in the same forest. On the basis of aerial photographs, the percentage of mature forest (older than 80 years) was measured in various rings around each of the 60
sites.
Usage
ex2015
Format
A data frame with 60 observations on the following 8 variables.
Site Site, a factor with levels "Random" and "Nest"
PctRing1 Percentage of mature forest in ring with outer radius 0.91 km
PctRing2 Percentage of mature forest in ring with outer radius 1.18 km
PctRing3 Percentage of mature forest in ring with outer radius 1.40 km
PctRing4 Percentage of mature forest in ring with outer radius 1.60 km
PctRing5 Percentage of mature forest in ring with outer radius 1.77 km
PctRing6 Percentage of mature forest in ring with outer radius 2.41 km
PctRing7 Percentage of mature forest in ring with outer radius 3.38 km
Source
Ramsey, F.L. and Schafer, D.W. (2013). The Statistical Sleuth: A Course in Methods of Data
Analysis (3rd ed), Cengage Learning.
References
Ripple W.J., Johnson, D.H., Thershey, K.T. and Meslow E.C. (1991). Old–growth and Mature
Forests Near Spotted Owl Nests in Western Oregon, Journal of Wildlife Management 55(2): 316–
318.
Examples
str(ex2015)

164

ex2016

ex2016

Bumpus Natural Selection Data

Description
Hermon Bumpus analysed various characteristics of some house sparrows that were found on the
ground after a severe winter storm in 1898. Some of the sparrows survived and some perished. This
data set contains the survival status, age, the length from tip of beak to tip of tail (in mm), the alar
extent (length from tip to tip of the extended wings, in mm), the weight in grams, the length of the
head in mm, the length of the humerus (arm bone, in inches), the length of the femur (thigh bones,
in inches), the length of the tibio–tarsus (leg bone, in inches), the breadth of the skull in inches and
the length of the sternum in inches.
Usage
ex2016
Format
A data frame with 87 observations on the following 11 variables.
Status Survival status, factor with levels "Perished" and "Survived"
AG a numerical code corresponding to two categories of age, with 1 = "adult" and 2 = "juvenile"
TL total length (in mm)
AE alar extent (in mm)
WT weight (in grams)
BH length of beak and head (in mm)
HL length of humerus (in inches)
FL length of femur (in inches)
TT length of tibio–tarsus (in inches)
SK width of skull (in inches)
KL length of keel of sternum (in inches)
Source
Ramsey, F.L. and Schafer, D.W. (2013). The Statistical Sleuth: A Course in Methods of Data
Analysis (3rd ed), Cengage Learning.
See Also
ex0221
Examples
str(ex2016)

ex2017

ex2017

165

Catholic stance

Description
The Catholic church has explicitly opposed authoritarian rule in some (but not all) Latin American
countries. Although such action could be explained as a desire to counter repression or to increase
the quality of life of its parishioners, A.J. Gill supplies evidence that the underlying reason may
be competition from evangelical Protestant denominations. He compiled the data given in this data
frame.

Usage
ex2017

Format
A data frame with 12 observations on the following 5 variables.
Stance Catholic church stance, factor with levels "Pro" and "Anti"
Country Latin American country
PQLI Physical Quality of Life Index in the mid-1970s; Average of live expectancy at age 1, infant
mortality and literacy at age 15+.
Repression Average civil rights score for the period of authoritarian rule until 1979
Competition Percentage increase of competitive religious groups during the period 1900–1970

Source
Ramsey, F.L. and Schafer, D.W. (2013). The Statistical Sleuth: A Course in Methods of Data
Analysis (3rd ed), Cengage Learning.

References
Gill, A.J. (1994). Rendering unto Caesar? Religious Competition and Catholic Strategy in Latin
America, 1962–1979, American Journal of Political Science 38(2): 403–425.

Examples
str(ex2017)

166

ex2018

ex2018

Fatal Car Accidents Involving Tire Failures on Ford Explorers

Description
This data frame contains data on 1995 and later model compact sports utility vehicles involved in
fatal accidents in the United States between 1995 and 1999, excluding those that were struck by
another car and excluding accidents that, according to police reports, involved alcohol.
Usage
ex2018
Format
A data frame with 2,321 observations on the following 4 variables.
Make Type of sports utility vehicle, factor with levels "Other" and "Ford"
VehicleAge Vehicle age (in years); surrogate for age of tires
Passengers Number of passengers
Cause Cause of fatal accident, factor with levels "NotTire" and "Tire"
Details
The Ford Explorer is a popular sports utility vehicle made in the United States and sold throughout
the world. Early in its production concern arose over a potential accident risk associated with tires
of the prescribed size when the vehicle was carrying heavy loads, but the risk was thought to be
acceptable if a low tire pressure was recommended. The problem was apparently exacerbated by
a particular type of Firestone tire that was overly prone to separation, especially in warm temperatures. This type of tire was a common one used on Explorers in model years 1995 and later. By the
end of 1999 more than 30 lawsuits had been filed over accidents that were thought to be associated
with this problem. U.S. federal data on fatal car accidents were analysed at that time, showing that
the odds of a fatal accident being associated with tire failure were three times as great for Explorers
as for other sports utility vehicles.
Additional data from 1999 and additional variables may be used to further explore the odds ratio.
It is of interest to see whether the odds that a fatal accident is tire-related depend on whether the
vehicle is a Ford, after accounting for age of the car and number of passengers. Since the Ford tire
problem may be due to the load carried, there is some interest in seeing whether the odds associated
with a Ford depend on the number of passengers.
Source
Ramsey, F.L. and Schafer, D.W. (2013). The Statistical Sleuth: A Course in Methods of Data
Analysis (3rd ed), Cengage Learning.
See Also
ex1919
Examples
str(ex2018)

ex2019

ex2019

167

Missile Defenses

Description
Following a successful test of an anti-ballistic missile (ABM) in July 1999, many prominent U.S.
politicians called for the early deployment of a full ABM system. The scientific community was
less enthusiastic about the efficacy of such a system. This data set contains the success or failure of
ABM tests between March 1983 and May 1995. Do these data suggest any improvement in ABM
test success probability over time?
Usage
ex2019
Format
A data frame with 17 observations on the following 3 variables.
Date date of an ABM test
Months number of months after March 1983
Result a factor with levels "Failure" and "Success"
Source
Ramsey, F.L. and Schafer, D.W. (2013). The Statistical Sleuth: A Course in Methods of Data
Analysis (3rd ed), Cengage Learning.
References
Lewis, G. N., Postol, T. A. and Pike, J. (1999) Why National Missile Defense Won’t Work, Scientific
American 281(2): 36–41.
Examples
str(ex2019)

ex2113

Vitamin C and Colds

Description
These data are from a randomized experiment to asses the effect of large doses of vitamin C on the
incidence of colds.
Usage
ex2113

168

ex2115

Format
A data frame with 4 observations on the following 4 variables.
Dose the daily dose of vitamin C, in g
Number the number of subjects given that dose of vitamin C
WithoutIllness the number of subjects who did not become ill
ProportionWithout the proportion of subjects who did not become ill
Source
Ramsey, F.L. and Schafer, D.W. (2013). The Statistical Sleuth: A Course in Methods of Data
Analysis (3rd ed), Cengage Learning.
References
Anderson, T.W., Suranyi,G., and Beaton, G.H. (1974) The Effect on Winter Illness of Large Doses
of Vitamin C Canadian Medical Association Journal 111 31–36.
Examples
str(ex2113)

ex2115

Belief Accessibility

Description
The study the effect of context questions prior to target questions, researchers conducted a poll involving 1,054 subjects selected randomly from the Chicago phone directory. To include possibly
unlisted phones, selected numbers were randomly altered in the last position. This data frame contains the responses to one of the questions asked concerning continuing U.S. aid to the Nicaraguan
Contra rebels. Eight different versions of the interview were given, representing all possible combinations of three factors at each of two levels. The experimental factors were Context, Mode and
Level.
Context refers to the type of context questions preceding the question about Nicaraguan aid. Some
subjects received a context question about Vietnam, designed to elicit reticence about having the
U.S. become involved in another foreign war in a third–world country. The other context question
was about Cuba, designed to elicit anti–communist sentiments.
Mode refers to whether the target question immediately followed the context question or whether
there were other questions scattered in between.
Level refers to two versions of the context question. In the "high" level the question was worded
to elicit a higher level of agreement than in the "low" level wording.
Usage
ex2115

ex2115

169

Format
A data frame with 8 observations on the following 7 variables.
Context Factor referring to the context of the question preceding the target question about U.S. aid
to the Nicaraguan Contra rebels
Mode Factor with levels "not" and "scattered", "scattered" is used if the target question was
not asked directly after the context question
Level Factor with levels "low" and "high", refers to the wording of the question
Number Number of people interviewed
InFavor Number of people in favor of Contra Aid
NotInFavor Number of people not in favor of Contra Aid
PercentInFavor Percentage in favour of Contra aid
Details
Increasingly, politicians look to public opinion surveys to shape their public stances. Does this
represent the ultimate in democracy? Or are seemingly scientific polls being rigged by the manner of
questioning? Psychologists believe that opinions—expressed as answers to questions—are usually
generated at the time the question is asked. Answers are based on a quick sampling of relevant
beliefs held by the subject, rather than a systematic canvas of all such beliefs. Furthermore, this
sampling of beliefs tends to overrepresent whatever beliefs happen to be most accessible at the time
the question is asked. This aspect of delivering opinions can be abused by the pollster. Here, for
example, is one sequence of questions:
(1) “Do you believe the Bill of Rights protects personal freedom?”
(2) “Are you in favor of a ban on handguns?”
Here is another:
(1) “Do you think something should be done to reduce violent crime?”
(2) “Are you in favor of a ban on handguns?”
The proportion of yes answers to question 2 may be quite different depending on which question 1
is asked first.
Source
Ramsey, F.L. and Schafer, D.W. (2013). The Statistical Sleuth: A Course in Methods of Data
Analysis (3rd ed), Cengage Learning.
References
Tourangeau, R., Rasinski, K.A., Bradburn, N. and D’Andrade, R. (1989). Belief Accessibility and
Context Effects in Attitude Measurement, Journal of Experimental Social Psychology 25: 401–421.
Examples
str(ex2115)

170

ex2117

ex2116

Aflatoxicol and Liver Tumors in Trout

Description
An experiment at the Marine/Freshwater Biomedical Sciences Center at Oregon State University
investigated the carcinogenic effects of aflatoxicol, a metabolite of Aflatoxin B1, which is a toxic
by-product produced by a mold that infects cottonseed meal, peanuts and grains. Twenty tanks of
rainbow trout embryos were exposed to one of five doses of Aflatoxicol for one hour. The data
represent the numbers of fish in each tank and the numbers of these that had liver tumours after one
year.
Usage
ex2116
Format
A data frame with 20 observations on the following 3 variables.
Dose Dose (in ppm)
Tumor Number of trout with liver tumours
Total Number of trout in tank
Source
Ramsey, F.L. and Schafer, D.W. (2013). The Statistical Sleuth: A Course in Methods of Data
Analysis (3rd ed), Cengage Learning.
Examples
str(ex2116)

ex2117

Effect of Stress During Conception on Odds of a Male Birth

Description
The probability of a male birth in humans is about .51. It has previously been noticed that lower
proportions of male births are observed when offspring is conceived at times of exposure to smog,
floods or earthquakes. Danish researchers hypothesised that sources of stress associated with severe
life events may also have some bearing on the sex ratio. To investigate this theory they obtained the
sexes of all 3,072 children who were born in Denmark between 1 January 1980 and 31 December
1992 to women who experienced the following kind of severe life events in the year of the birth or
the year prior to the birth: death or admission to hospital for cancer or heart attack of their partner or
of their other children. They also obtained sexes on a sample of 20,337 births to mothers who did not
experience these life stress episodes. This data frame contains the data that were collected. Noticed
that for one group the exposure is listed as taking place during the first trimester of pregnancy. The
rationale for this is that the stress associated with the cancer or heart attack of a family member may
well have started before the recorded time of death or hospital admission.

ex2118

171

Usage
ex2117
Format
A data frame with 5 observations on the following 4 variables.
Group Indicator for groups to which mothers belong
Time Indicator for time at which severe life event occurred
Number Number of births
PctBoys Percentage of boys born
Source
Ramsey, F.L. and Schafer, D.W. (2013). The Statistical Sleuth: A Course in Methods of Data
Analysis (3rd ed), Cengage Learning.
References
Hansen, D., Møller, H. and Olsen, J. (1999). Severe Periconceptional Life Events and the Sex
Ratio in Offspring: Follow Up Study based on Five National Registers, British Medical Journal
319(7209): 548–549.
Examples
str(ex2117)

ex2118

HIV and Circumcision

Description
Researchers in Kenya identified a cohort of more that 1,000 prostitutes who were known to be a
major reservoir of sexually transmitted diseases in 1985. It was determined that more than 85% of
them were infected with human immunodeficiency virus (HIV) in February, 1986. The researchers
identified men who acquired a sexually-transmitted disease from this group of women after the men
sought treatment at a free clinic. The data frame contains data on the subset of those men who did
not test positive for HIV on their first visit and who agreed to participate in the study. The men are
categorised according to whether they later tested positive for HIV during the study period, whether
they had one or multiple sexual contacts with the prostitutes and whether they were circumcised.
Usage
ex2118

172

ex2119

Format
A data frame with 4 observations on the following 5 variables.
Contact Whether men had single or multiple contact with prostitutes
Circumcised Whether the men are circumcised, factor with levels "No" and "Yes"
HIV Number of men that tested positive for HIV
Number Number of men
NoHIV Number of men that did not test positive for HIV (should be Number-HIV)
Source
Ramsey, F.L. and Schafer, D.W. (2013). The Statistical Sleuth: A Course in Methods of Data
Analysis (3rd ed), Cengage Learning.
References
Cameron, D.W., D’Costa, L.J., Maitha, G.M., Cheang, M., Piot, P., Simonsen, J.N., Ronald, A.R.,
Gakinya, M.N., Ndinya-Achola, J.O., Brunham, R.C. and Plummer, F. A. (1989). Female to Male
Transmission of Human Immunodeficiency Virus Type I: Risk Factors for Seroconversion in Men,
The Lancet 334(8660): 403–407.
Examples
str(ex2118)

ex2119

Meta–Analysis of Breast Cancer and Lactation Studies

Description
This data frame gives the results of 10 separate case–control studies on the association of breast
cancer and whether a woman had breast–fed children.
Usage
ex2119
Format
A data frame with 20 observations on the following 4 variables.
Study Factor indicating the study from which data was taken
Lactate Whether women had breast–fed children (lactated)
Cancer Number of women with breast cancer
NoCancer Number of women without breast cancer

ex2120

173

Details
Meta–analysis refers to the analysis of analyses. When the main results of studies can be cast into
2×2 tables of counts, it is natural to combine individual odds ratios with a logistic regression model
that includes a factor to account for different odds from the different studies. In addition, the odds
ratio itself might differ slightly among studies because of different effects on different populations
or different research techniques. One approach for dealing with this is to suppose an underlying
common odds ratio and to model between–study variability as extra–binomial variation.
Source
Ramsey, F.L. and Schafer, D.W. (2013). The Statistical Sleuth: A Course in Methods of Data
Analysis (3rd ed), Cengage Learning.
References
Data gathered from various sources by Karolyn Kolassa as part of a Master’s project, Oregon State
University.
Examples
str(ex2119)

ex2120

Clever Hans Effect

Description
These data were simulated to match the summary statistics and conclusions of Rosenthal and Fode’s
Clever Hans experiment. Each of 12 students trained rats to run a maze. The data set contains their
number of successful runs out of 50 on each of 5 days, the student’s prior expectation of success
(on a scale from -10 to 10), and a variable indicating treatment–whether or not the students were
supplied with the fictitious information that their rights were bright.
Usage
2120
Format
A data frame with 60 observations on the following 5 variables.
Student a student identification number
PriorExp the student’s prior expectation of rat-training success, on a scale from -10 to 10
Treatment a factor with levels "bright" and "dull" corresponding to whether students were told
(falsely) that their rats were bright or not
Day day of the study, ranging from 1 to 5
Success the number of successful maze runs on a day, out of 50
Source
Ramsey, F.L. and Schafer, D.W. (2013). The Statistical Sleuth: A Course in Methods of Data
Analysis (3rd ed), Cengage Learning.

174

ex2216

References
Rosenthal, R. and Fode, K.L. (1963) The Effect of Experimenter Bias on the Performance of the
Albino Rat Behavioral Science 8:3: 183–189.
See Also
ex1419
Examples
str(ex2120)

ex2216

Murder–Suicides by Deliberate Plane Crash

Description
Some sociologist suspect that highly publicised suicides may trigger additional suicides. In one
investigation of this hypothesis, D.P. Phillips collected information about 17 airplane crashes that
were known (because of notes left behind) to be murder–suicides. For each of these crashes, Phillips
reported an index of the news coverage (circulation of nine newspapers devoting space to the crash
multiplied by length of coverage) and the number of multiple-fatality plane crashes during the week
following the publicised crash. This data frame contains the collected data.
Usage
ex2216
Format
A data frame with 17 observations on the following 2 variables.
Index Index for the amount of newspaper coverage given the murder–suicide
Crashes Multiple-fatality crashes in the week following a murder–suicide by plane crash
Source
Ramsey, F.L. and Schafer, D.W. (2013). The Statistical Sleuth: A Course in Methods of Data
Analysis (3rd ed), Cengage Learning.
References
Phillips, D.P. (1978). Airplane Accident Fatalities Increase Just After Newspaper Stories About
Murder and Suicide, Science 201: 748–750.
Examples
str(ex2216)

ex2220

ex2220

175

Cancer Deaths of Atomic Bomb Survivors

Description
The data are the number of cancer deaths among survivors of the atomic bombs dropped on Japan
during World War II, categorized by time (years) after the bomb that death occurred and the amount
of radiation exposure that the survivors received from the blast. Also listed in each cell is the personyears at risk, in 100’s. This is the sum total of all years spent by all persons in the category. The data
can be analyzed by supposing the number of cancer deaths in each cell is Poisson with mean = risk
x rate, where risk is the person-years at risk and rate is the rate of cancer deaths per person per year.
How does the rate depend on the radiation exposure, after accounting for years after exposure?
Usage
ex2220
Format
A data frame with 42 observations on the following 4 variables.
Exposure radiation exposure, in rads
YearsAfter years after the exposure
AtRisk number of survivors in the group
Deaths number of survivors in the group who died of Cancer
Source
Ramsey, F.L. and Schafer, D.W. (2013). The Statistical Sleuth: A Course in Methods of Data
Analysis (3rd ed), Cengage Learning.
References
Pierce, D.A., personal communication
Examples
str(ex2220)

ex2222

Emulating Jane Austen’s Writing Style

Description
When she died in 1817, the English novelist Jane Austen had not yet finished the novel Sanditon,
but she did leave notes on how she intended to conclude the book. The novel was completed by
a ghost writer, who attempted to emulate Austen’s style. In 1978, a researcher reported counts of
some words found in chapters of books written by Austen and in chapters written by the emulator.
These data are given in this data frame.

176

ex2223

Usage
ex2222
Format
A data frame with 24 observations on the following 3 variables.
Count Number of occurrences of a word in various chapters of books written by Jane Austen and
the ghost writer
Book Title of books used
Word Words used
Source
Ramsey, F.L. and Schafer, D.W. (2013). The Statistical Sleuth: A Course in Methods of Data
Analysis (3rd ed), Cengage Learning.
References
Morton, A.Q. (1978). Literary Detection: How to Prove Authorship and Fraud in Literature and
Documents, Charles Scribner’s Sons, New York.
Examples
str(ex2222)

ex2223

Space Shuttle O-Ring Failures

Description
On January 27, 1986, the night before the space shuttle Challenger exploded, an engineer recommended to the National Aeronautics and Space Administration (NASA) that the shuttle not be
launched in the cold weather. The forecasted temperature for the Challenger launch was 31 degrees
Fahrenheit—the coldest launch ever. After an intense 3-hour telephone conference, officials decided to proceed with the launch. This data frame contains the launch temperatures and the number
of O-ring problems in 24 shuttle launches prior to the Challenger.
Usage
ex2223
Format
A data frame with 24 observations on the following 2 variables.
Temp Launch temperatures (in degrees Fahrenheit)
Incidents Numbers of O-ring incidents

ex2224

177

Source
Ramsey, F.L. and Schafer, D.W. (2013). The Statistical Sleuth: A Course in Methods of Data
Analysis (3rd ed), Cengage Learning.
See Also
case0401, ex2011
Examples
str(ex2223)

ex2224

Valve Failure in Nuclear Reactors

Description
This data frame contains data on characteristics and numbers of failures observed in valve types
from one pressurised water reactor.
Usage
ex2224
Format
A data frame with 90 observations on the following 7 variables.
System a numerical code corresponding to 5 categories of system (1 = containment, 2 = nuclear, 3
= power conversion, 4 = safety, 5 = process auxiliary)
Operator a numerical code corresponding to 4 different operator types (1 = air, 2 = solenoid, 3 =
motor=driven, 4 = manual)
Valve a numerical code corresponding to 6 different valve types (1 = ball, 2 = butterfly, 3 = diaphram, 4 = gate, 5 = globe, 6 = directional control)
Size a numerical code corresponding to 3 head size categories (1 = less than 2 inches, 2 = 2–10
inches, 3 = 10–30 inches)
Mode a numerical code corresponding to two categories of operation mode (1 = normally closed,
2 = normally open)
Failures Number of failures observed
Time Lengths of observation time
Source
Ramsey, F.L. and Schafer, D.W. (2013). The Statistical Sleuth: A Course in Methods of Data
Analysis (3rd ed), Cengage Learning.
References
Moore, L.M. and Beckman, R.J. (1988). Appropriate One-Sided Tolerance Bounds on the Number
of Failures using Poisson Regression, Technometrics 30: 283–290.

178

ex2226

Examples
str(ex2224)

ex2225

Body Size and Reproductive Success in a Population of Male Bullfrogs

Description
As an example of field observation in evidence of theories of sexual selection, S.J. Arnold and M.J.
Wade presented the following data set on size and number of mates observed in 38 bullfrogs.
Usage
ex2225
Format
A data frame with 38 observations on the following 2 variables.
BodySize Body size (in mm)
Mates Number of mates
Source
Ramsey, F.L. and Schafer, D.W. (2013). The Statistical Sleuth: A Course in Methods of Data
Analysis (3rd ed), Cengage Learning.
References
Arnold, S.J. and Wade, M.J. (1984). On the Measurement of Natural and Sexual Selection: Aplications, Evolution 38: 720–734.
Examples
str(ex2225)

ex2226

Number of Moons

Description
Apparently, larger planets have more moons, but is it the volume (as indicated by diameter) or mass
that are more relevant, or is it both? These data include the diameter, mass, distance from the sun,
and number of moons for 13 planets, gas giants, and dwarf planets in our solar system. Which size
variable best explains mean number of moons (possible after accounting for distance from sun).
(Consider negative binomial regression.)
Usage
ex2226

ex2414

179

Format
A data frame with 13 observations on the following 5 variables.
Name a character variable with the name of the planet, gas giant, or dwarf planet)
Distance distance from sun, relative to earth’s
Diameter diameter of the planet, relative to earth’s
Mass mass, relative to earth’s
Moons number of moons
Source
Ramsey, F.L. and Schafer, D.W. (2013). The Statistical Sleuth: A Course in Methods of Data
Analysis (3rd ed), Cengage Learning.
References
Wikipedia: http://en.wikipedia.org/wiki/Planet August 10, 2011
See Also
ex0721
Examples
str(ex2226)

ex2414

Amphibian Crisis and UV-B

Description
Data frame contains the percentage of unsuccessful hatching from enclosures containing 150 eggs
each in a study to investigate whether UV-B is responsible for low hatch rates.
Usage
ex2414
Format
A data frame with 71 observations on the following 4 variables.
Percent percentage of frog eggs failing to hatch
Treatment factor variable with levels "NoFilter", "UV-BTransmitting" and "UV-BBlocking"
Location factor variable with levels "ThreeCreeks", "SparksLake", "SmallLake" and "LostLake"
Photolyase Photolyase activity
Source
Ramsey, F.L. and Schafer, D.W. (2013). The Statistical Sleuth: A Course in Methods of Data
Analysis (3rd ed), Cengage Learning.

180

Sleuth3Manual

References
Blaustein, A.R., Hoffman, P.D., Hokit, D.G., Kiesecker, J.M., Walls, S.C. and Hays, J.B. (1994).
UV Repair and Resistance to Solar UV-B in Amphibian Eggs: A Link to Population Declines?
Proceedings of the National Academy of Science, USA 91: 1791–1795.
Examples
str(ex2414)

Sleuth3Manual

Manual of the R Sleuth3 package

Description
If the option “pdfviewer” is set, this command will display the PDF version of the help pages.
Usage
Sleuth3Manual()
Author(s)
Berwin A Turlach 
References
Ramsey, F.L. and Schafer, D.W. (2013). The Statistical Sleuth: A Course in Methods of Data
Analysis (3rd ed), Cengage Learning.
Examples
## Not run: Sleuth3Manual()

Index
case2102, 70
case2201, 71
case2202, 73
ex0112, 74
ex0116, 75
ex0125, 76
ex0126, 76
ex0127, 77
ex0211, 78
ex0218, 79
ex0221, 80
ex0222, 81
ex0223, 82
ex0321, 83
ex0323, 83
ex0327, 84
ex0330, 85
ex0331, 86
ex0332, 86
ex0333, 87
ex0428, 88
ex0429, 89
ex0430, 89
ex0431, 90
ex0432, 91
ex0518, 91
ex0523, 92
ex0524, 93
ex0525, 94
ex0623, 95
ex0624, 95
ex0721, 96
ex0722, 97
ex0724, 98
ex0725, 98
ex0726, 99
ex0727, 100
ex0728, 101
ex0729, 101
ex0730, 102
ex0816, 103
ex0817, 104
ex0820, 104

∗Topic datasets
case0101, 5
case0102, 6
case0201, 7
case0202, 8
case0301, 9
case0302, 11
case0401, 12
case0402, 13
case0501, 14
case0502, 16
case0601, 17
case0602, 19
case0701, 20
case0702, 21
case0801, 23
case0802, 24
case0901, 25
case0902, 26
case1001, 28
case1002, 29
case1101, 31
case1102, 33
case1201, 35
case1202, 37
case1301, 39
case1302, 40
case1401, 42
case1402, 43
case1501, 45
case1502, 47
case1601, 48
case1602, 50
case1701, 52
case1702, 54
case1801, 57
case1802, 59
case1803, 60
case1901, 61
case1902, 62
case2001, 64
case2002, 66
case2101, 68
181

182

INDEX
ex0822, 106
ex0823, 106
ex0824, 107
ex0825, 108
ex0826, 108
ex0828, 109
ex0829, 110
ex0914, 111
ex0915, 111
ex0918, 112
ex0920, 113
ex0921, 114
ex0923, 115
ex1014, 116
ex1026, 116
ex1027, 117
ex1028, 118
ex1029, 119
ex1030, 120
ex1031, 121
ex1033, 122
ex1111, 123
ex1120, 123
ex1122, 124
ex1123, 125
ex1124, 126
ex1125, 126
ex1217, 127
ex1220, 129
ex1221, 130
ex1222, 131
ex1223, 132
ex1225, 133
ex1317, 134
ex1319, 135
ex1320, 136
ex1321, 137
ex1416, 138
ex1417, 139
ex1419, 140
ex1420, 141
ex1507, 142
ex1509, 142
ex1514, 143
ex1515, 144
ex1516, 144
ex1517, 145
ex1518, 146
ex1519, 146
ex1605, 147
ex1611, 148
ex1612, 149

ex1613, 149
ex1614, 150
ex1615, 151
ex1620, 152
ex1708, 152
ex1715, 153
ex1716, 154
ex1914, 155
ex1916, 156
ex1917, 156
ex1918, 157
ex1919, 158
ex1921, 159
ex1922, 159
ex1923, 160
ex2011, 161
ex2012, 162
ex2015, 163
ex2016, 164
ex2017, 165
ex2018, 166
ex2019, 167
ex2113, 167
ex2115, 168
ex2116, 170
ex2117, 170
ex2118, 171
ex2119, 172
ex2120, 173
ex2216, 174
ex2220, 175
ex2222, 175
ex2223, 176
ex2224, 177
ex2225, 178
ex2226, 178
ex2414, 179
∗Topic documentation
Sleuth3Manual, 180
∗Topic package
Sleuth3-package, 5
case0101, 5
case0102, 6, 37
case0201, 7, 79
case0202, 8
case0301, 9
case0302, 11
case0401, 12, 161, 177
case0402, 13
case0501, 14
case0502, 16
case0601, 17

INDEX
case0602, 19
case0701, 20, 98, 99
case0702, 21, 103
case0801, 23
case0802, 24
case0901, 25
case0902, 26, 88
case1001, 28
case1002, 29
case1101, 31
case1102, 33, 138, 139
case1201, 35
case1202, 6, 37
case1301, 39
case1302, 40
case1401, 42
case1402, 43
case1501, 45
case1502, 47, 147
case1601, 48
case1602, 50
case1701, 52
case1702, 54
case1801, 57
case1802, 59
case1803, 60
case1901, 61
case1902, 62
case2001, 64, 158
case2002, 66
case2101, 68
case2102, 70
case2201, 71
case2202, 73
ex0112, 74
ex0116, 75
ex0125, 76
ex0126, 76, 78
ex0127, 77, 77
ex0211, 78
ex0218, 8, 79
ex0221, 80, 164
ex0222, 81, 85, 86, 93, 94, 110, 115, 122, 133
ex0223, 82
ex0321, 83
ex0323, 83
ex0327, 84
ex0330, 81, 85, 86, 93, 94, 110, 115, 122, 133
ex0331, 81, 85, 86, 93, 94, 110, 115, 122, 133
ex0332, 86
ex0333, 27, 87
ex0428, 88

183
ex0429, 89
ex0430, 89
ex0431, 90
ex0432, 91
ex0518, 91
ex0523, 92, 124
ex0524, 81, 85, 86, 93, 94, 110, 115, 122, 133
ex0525, 81, 85, 86, 93, 94, 110, 115, 122, 133
ex0623, 95, 141, 159, 160
ex0624, 95
ex0721, 96, 179
ex0722, 97
ex0724, 98
ex0725, 21, 98
ex0726, 99
ex0727, 100
ex0728, 101
ex0729, 101, 103
ex0730, 102, 102
ex0816, 22, 103
ex0817, 104
ex0820, 104
ex0822, 106
ex0823, 106
ex0824, 107
ex0825, 108, 131
ex0826, 108
ex0828, 81, 85, 86, 93, 94, 109, 115, 122, 133
ex0829, 110
ex0914, 111
ex0915, 111
ex0918, 112
ex0920, 113
ex0921, 114, 127
ex0923, 81, 85, 86, 93, 94, 110, 115, 122, 133
ex1014, 116
ex1026, 116
ex1027, 117
ex1028, 118
ex1029, 119
ex1030, 120
ex1031, 121, 160, 161
ex1033, 81, 85, 86, 93, 94, 110, 115, 122, 133
ex1111, 123
ex1120, 92, 123
ex1122, 124
ex1123, 125, 127, 128
ex1124, 126
ex1125, 114, 126
ex1217, 125, 127
ex1220, 129
ex1221, 130

184
ex1222, 108, 131
ex1223, 81, 85, 86, 93, 94, 110, 115, 122, 132
ex1225, 133
ex1317, 134
ex1319, 135, 148
ex1320, 136
ex1321, 137
ex1416, 33, 138, 139
ex1417, 33, 138, 139
ex1419, 140, 174
ex1420, 95, 141, 159, 160
ex1507, 142
ex1509, 142
ex1514, 143
ex1515, 144
ex1516, 144
ex1517, 145
ex1518, 146
ex1519, 48, 146
ex1605, 135, 147
ex1611, 148
ex1612, 149
ex1613, 149
ex1614, 150
ex1615, 151
ex1620, 152
ex1708, 152
ex1715, 153
ex1716, 154
ex1914, 155
ex1916, 156
ex1917, 156
ex1918, 65, 157
ex1919, 158, 166
ex1921, 95, 141, 159, 159, 160
ex1922, 95, 141, 159, 159
ex1923, 121, 160
ex2011, 12, 161, 177
ex2012, 162
ex2015, 163
ex2016, 80, 164
ex2017, 165
ex2018, 158, 166
ex2019, 167
ex2113, 167
ex2115, 168
ex2116, 170
ex2117, 170
ex2118, 171
ex2119, 172
ex2120, 141, 173
ex2216, 174

INDEX
ex2220, 175
ex2222, 175
ex2223, 12, 161, 176
ex2224, 177
ex2225, 178
ex2226, 97, 178
ex2414, 179
Sleuth3 (Sleuth3-package), 5
Sleuth3-package, 5
Sleuth3Manual, 180



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