Sleuth2 Manual

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Package ‘Sleuth2’
January 24, 2019
Title Data Sets from Ramsey and Schafer's ``Statistical Sleuth (2nd Ed)''
Version 2.0-5
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, Kate Aloisio
and Ruobing Zhang, with corrections by Randall Pruim
Description Data sets from Ramsey, F.L. and Schafer, D.W. (2002), ``The
Statistical Sleuth: A Course in Methods of Data Analysis (2nd
ed)'', Duxbury.
Maintainer Berwin A Turlach 
LazyData yes
Depends R (>= 3.5.0)
Suggests lattice, knitr, MASS, agricolae, car, gmodels, leaps, mosaic
VignetteBuilder knitr
License GPL (>= 2)
URL http://r-forge.r-project.org/projects/sleuth2/

R topics documented:
Sleuth2-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
case1902
case2001
case2002
case2101
case2102
case2201
case2202
ex0112 . .
ex0116 . .
ex0211 . .
ex0221 . .
ex0222 . .
ex0223 . .
ex0321 . .
ex0323 . .
ex0327 . .
ex0328 . .
ex0331 . .
ex0332 . .
ex0333 . .
ex0428 . .
ex0429 . .
ex0430 . .
ex0431 . .
ex0432 . .
ex0518 . .
ex0523 . .
ex0524 . .
ex0621 . .
ex0622 . .
ex0723 . .
ex0724 . .
ex0726 . .

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17
18
19
19
20
21
22
23
24
25
26
27
28
29
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31
32
33
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38
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61

R topics documented:
ex0727 .
ex0728 .
ex0729 .
ex0730 .
ex0816 .
ex0817 .
ex0818 .
ex0820 .
ex0822 .
ex0823 .
ex0824 .
ex0825 .
ex0914 .
ex0915 .
ex0918 .
ex0920 .
ex1014 .
ex1026 .
ex1027 .
ex1028 .
ex1029 .
ex1115 .
ex1120 .
ex1122 .
ex1123 .
ex1124 .
ex1217 .
ex1220 .
ex1221 .
ex1222 .
ex1317 .
ex1319 .
ex1320 .
ex1414 .
ex1415 .
ex1417 .
ex1509 .
ex1512 .
ex1513 .
ex1514 .
ex1515 .
ex1605 .
ex1611 .
ex1612 .
ex1613 .
ex1614 .
ex1615 .
ex1708 .
ex1713 .
ex1714 .
ex1914 .
ex1916 .

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4

Sleuth2-package
ex1917 . . . . .
ex1918 . . . . .
ex1919 . . . . .
ex2011 . . . . .
ex2012 . . . . .
ex2015 . . . . .
ex2016 . . . . .
ex2017 . . . . .
ex2018 . . . . .
ex2115 . . . . .
ex2116 . . . . .
ex2117 . . . . .
ex2118 . . . . .
ex2119 . . . . .
ex22.20 . . . .
ex2216 . . . . .
ex2222 . . . . .
ex2223 . . . . .
ex2224 . . . . .
ex2225 . . . . .
ex2414 . . . . .
Sleuth2Manual

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Index

Sleuth2-package

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101
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111
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115
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117
118
118
119
120

The R Sleuth2 package

Description
Data sets from Ramsey and Schafer’s "Statistical Sleuth (2nd ed)"
Details
This package contains a variety of datasets. For a complete list, use library(help="Sleuth2") or
Sleuth2Manual().
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

case0101

5

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 one 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
Source
Ramsey, F.L. and Schafer, D.W. (2002). The Statistical Sleuth: A Course in Methods of Data
Analysis (2nd ed), Duxbury.
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
str(case0101)
boxplot(Score~Treatment, 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

6

case0201

Format
A data frame with 93 observations on the following 2 variables.
Salary starting salaries (in US$)
Sex sex of the clerical employee
Source
Ramsey, F.L. and Schafer, D.W. (2002). The Statistical Sleuth: A Course in Methods of Data
Analysis (2nd ed), Duxbury.
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
Examples
str(case0102)
boxplot(Salary~Sex, case0102)

case0201

Bumpus’s Data on Natural Selection (Humerus)

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 humerus (arm bone) lengths
for the 24 adult male sparrows that perished and for the 35 adult males that survived.
Usage
case0201
Format
A data frame with 59 observations on the following 2 variables.
Humerus Humerus length of adult male sparrows (in inches)
Status factor variable indicating whether the sparrow perished or survived in a winter storm
Source
Ramsey, F.L. and Schafer, D.W. (2002). The Statistical Sleuth: A Course in Methods of Data
Analysis (2nd ed), Duxbury.

case0202

7

See Also
ex0221, ex2016
Examples
str(case0201)
with(subset(case0201, Status=="Perished"), stem(Humerus, scale=10))
with(subset(case0201, Status=="Survived"), stem(Humerus))

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 cm$^3$) of several
regions and subregions of the twins’ brains.
Usage
case0202
Format
A data frame with 15 observations on the following 2 variables.
Unaffect volume of left hippocampus of unaffected twin (in cm3 )
Affected volume of left hippocampus of affected twin (in cm3 )
Source
Ramsey, F.L. and Schafer, D.W. (2002). The Statistical Sleuth: A Course in Methods of Data
Analysis (2nd ed), Duxbury.
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
str(case0202)
with(case0202, stem(Unaffect-Affected, scale=2))

8

case0301

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

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. (2002). The Statistical Sleuth: A Course in Methods of Data
Analysis (2nd ed), Duxbury.

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
str(case0301)
boxplot(Rainfall ~ Treatment, case0301)
boxplot(log(Rainfall) ~ Treatment, case0301)
library(lattice)
bwplot(Treatment ~ log(Rainfall), case0301)
bwplot(log(Rainfall) ~ Treatment, case0301)

case0302

case0302

9

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. (2002). The Statistical Sleuth: A Course in Methods of Data
Analysis (2nd ed), Duxbury.
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
str(case0302)
boxplot(Dioxin ~ Veteran, case0302)
t.test(Dioxin ~ Veteran, case0302)
## To examine results with largest dioxin omitted
t.test(Dioxin ~ Veteran, case0302, subset=(Dioxin < 40))

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.

10

case0402

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. (2002). The Statistical Sleuth: A Course in Methods of Data
Analysis (2nd ed), Duxbury.
References
Feynman, R.P. (1988). What do You Care What Other People Think? W. W. Norton.
See Also
ex2011, ex2223
Examples
str(case0401)
stem(subset(case0401, Launch=="Cool", Incidents, drop=TRUE))
stem(subset(case0401, Launch=="Warm", Incidents, drop=TRUE))

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
Treatmt factor variable with two levels—"Modified" and "Conventional"
Censor 1 if the individual did not complete the problem in 5 minutes, 0 if they did

case0501

11

Source
Ramsey, F.L. and Schafer, D.W. (2002). The Statistical Sleuth: A Course in Methods of Data
Analysis (2nd ed), Duxbury.
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)
stem(subset(case0402, Treatmt=="Conventional", Time, drop=TRUE))
stem(subset(case0402, Treatmt=="Modified", Time, drop=TRUE))
wilcox.test(Time ~ Treatmt, 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.
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. (2002). The Statistical Sleuth: A Course in Methods of Data
Analysis (2nd ed), Duxbury.

12

case0502

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)
boxplot(Lifetime~Diet, width=c(rep(.8,6)), data=case0501,
xlab="Diet", ylab="Lifetime in months")
summary(subset(case0501, Diet=="NP", Lifetime))

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 a factor with levels "Spock's", "A", "B", "C", "D", "E" and "F"
Source
Ramsey, F.L. and Schafer, D.W. (2002). The Statistical Sleuth: A Course in Methods of Data
Analysis (2nd ed), Duxbury.
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.

case0601

13

Examples
str(case0502)
boxplot(Percent~Judge, data=case0502,
xlab="Judge",ylab="Percentage of Women")
percent.spocks <- subset(case0502, Judge == "Spock's", Percent)
percent.others <- subset(case0502, Judge != "Spock's", Percent)
t.test( percent.spocks,percent.others)
summary(aov(Percent~Judge, case0502, subset = Judge != "Spock's"))
#as in Display 5.10
summary(aov(Percent~Judge, 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"
Source
Ramsey, F.L. and Schafer, D.W. (2002). The Statistical Sleuth: A Course in Methods of Data
Analysis (2nd ed), Duxbury.
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)
boxplot(Score~Handicap, data=case0601, ylab="Score")
aov.handicap <- aov(Score ~ Handicap, case0601)
summary(aov.handicap)
TukeyHSD(aov.handicap)

14

case0602
#Calculate confidence interval for linear combination
#(wheelchair+crutches)/2 - (amputee+hearing)/2 as in Display 6.4
mean.handicaps <- with(case0601, tapply(Score, Handicap, mean))
var.handicaps <- with(case0601, tapply(Score, Handicap, var))
n <- 14
s.pooled <- sqrt(sum((n-1)*var.handicaps)/sum((n-1)*5))
## either
cr.wh <- mean.handicaps["Wheelchair"] + mean.handicaps["Crutches"]
am.he <- mean.handicaps["Amputee"] + mean.handicaps["Hearing"]
g <- cr.wh/2 - am.he/2
## or
contr <- c(0, -1, 1, -1, 1)/2
g <- sum(contr * mean.handicaps)
se.g <- s.pooled * sqrt(sum(contr^2)/n)
t.65 <- qt(.975, 65)
## ci
g + c(-1,1) * t.65 * se.g

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.
Proportion The proportion of courtship time spent by 84 females with the yellow-sword males
Pair Factor variable with 6 levels—"Pair 1", "Pair 2", "Pair 3", "Pair 4", "Pair 5" and
"Pair 6"
Length Body size of the males
Source
Ramsey, F.L. and Schafer, D.W. (2002). The Statistical Sleuth: A Course in Methods of Data
Analysis (2nd ed), Duxbury.

case0701

15

References
Basolo, A.L. (1990). Female Preference Predates the Evolution of the Sword in Swordtail Fish,
Science 250: 808–810.
Examples
str(case0602)
boxplot(Proportion~Pair, case0602, ylab="Proportion")
#as in Display 6.5
summary(aov(Proportion~Pair, case0602))
n.fish <- with(case0602, tapply(Proportion, Pair, length))
av.fish <- with(case0602, tapply(Proportion, Pair, mean))
sd.fish <- with(case0602, tapply(Proportion, Pair, sd))
male.body.size <- with(case0602, tapply(Length, Pair, unique))
mean.body <- mean(male.body.size)
table.fish <- data.frame(n.fish, round(av.fish*100,2),
round(sd.fish*100,2), male.body.size,
2*(male.body.size-mean.body))
names(table.fish) <- c("n", "average", "sd", "male.body.size", "coefficient")
s.pooled <- with(table.fish, round(sqrt(sum(sd^2*(n-1))/sum(n-1)),2))
g <- with(table.fish, sum(average*coefficient))
se.g <- with(table.fish, round(s.pooled*sqrt(sum(coefficient^2/n)),2))
g/se.g

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. (2002). The Statistical Sleuth: A Course in Methods of Data
Analysis (2nd ed), Duxbury.
References
Hubble, E. (1929). A Relation Between Distance and Radial Velocity Among Extragalactic Nebulae, Proceedings of the National Academy of Science 15: 168–173.

16

case0702

See Also
ex0727
Examples
str(case0701)
plot(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.
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. (2002). The Statistical Sleuth: A Course in Methods of Data
Analysis (2nd ed), Duxbury.
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)
plot(case0702)

case0801

case0801

17

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. (2002). The Statistical Sleuth: A Course in Methods of Data
Analysis (2nd ed), Duxbury.
Examples
str(case0801)
plot(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)

18

case0901

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

Examples
str(case0802)
plot(log(Time)~Voltage, 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.

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
Intens light intensity (in µmol/m2 /sec)
Source
Ramsey, F.L. and Schafer, D.W. (2002). The Statistical Sleuth: A Course in Methods of Data
Analysis (2nd ed), Duxbury.

Examples
str(case0901)
plot(Flowers~Intens, case0901, pch= ifelse(Time=="Early", 19, 21))

case0902

case0902

19

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.
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. (2002). The Statistical Sleuth: A Course in Methods of Data
Analysis (2nd ed), Duxbury.
See Also
case0902
Examples
str(case0902)
pairs(log(Brain)~log(Body)+log(Litter)+Gestation, 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

20

case1002

Format
A data frame with 7 observations on the following 2 variables.
Distance horizontal distances (in punti)
Height initial height (in punti)
Source
Ramsey, F.L. and Schafer, D.W. (2002). The Statistical Sleuth: A Course in Methods of Data
Analysis (2nd ed), Duxbury.
Examples
str(case1001)
plot(Distance ~ Height, 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
Format
A data frame with 20 observations on the following 4 variables.
Species species
Mass mass (in grams)
Type a factor with 3 levels indicating the type of flying vertebrate
Energy in–flight energy expenditure (in W)
Source
Ramsey, F.L. and Schafer, D.W. (2002). The Statistical Sleuth: A Course in Methods of Data
Analysis (2nd ed), Duxbury.
References
Speakman, J.R. and Racey, P.A. (1991). No cost of Echolocation for Bats in Flight, Nature 350:
421–423.

case1101

21

Examples
str(case1002)
plot(log(Energy)~log(Mass), case1002,
pch = ifelse(Type=="echolocating bats", 19,
ifelse(Type=="non-echolocating birds", 21, 24)))
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"))
library(lattice)
yticks <- c(1,2,5,10,20,50)
xticks <- c(10,20,50,100,200,500)
xyplot(Energy ~ Mass, case1002, groups=Type,
scales = list(log=TRUE, y=list(at=yticks), x=list(at=xticks)),
ylab = "Energy Expenditure (W) (log scale)",
xlab = "Body Mass (g) (log scale)",
auto.key = list(x = 0.2, y = 0.9, corner = c(0, 1), border = TRUE))

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. (2002). The Statistical Sleuth: A Course in Methods of Data
Analysis (2nd ed), Duxbury.

22

case1102

Examples
str(case1101)
plot(Metabol~Gastric, case1101,
pch=ifelse(Sex=="Female", 19, 21),
col=ifelse(Alcohol=="Alcoholic", "red", "green"))
legend(1,12, pch=c(19,21,19,21), col=c("green","green", "red", "red"),
c("Non-alcoholic Females", "Non-alcoholic Males",
"Alcoholic Females", "Alcoholic Males"))
library(lattice)
xyplot(Metabol~Gastric|Sex*Alcohol, case1101)
xyplot(Metabol~Gastric, case1101, groups=Sex:Alcohol,
auto.key=list(x=0.2, y=0.8, corner=c(0,0), border=TRUE))

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)
Treat 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. (2002). The Statistical Sleuth: A Course in Methods of Data
Analysis (2nd ed), Duxbury.

case1201

23

Examples
str(case1102)
plot(Brain/Liver ~ Time, case1102, log="xy", pch=ifelse(Treat=="BD", 19,21))
legend(10,0.1, pch=c(19,21), c("Saline control", "Barrier disruption"))

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. (2002). The Statistical Sleuth: A Course in Methods of Data
Analysis (2nd ed), Duxbury.
Examples
str(case1201)
pairs(SAT~Rank+Years+Income+Public+Expend, case1201)

24

case1202

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. (2002). The Statistical Sleuth: A Course in Methods of Data
Analysis (2nd ed), Duxbury.
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
Examples
str(case1202)
pairs(Sal77~Bsal+Senior+Age+Exper, case1202)

case1301

case1301

25

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. (2002). The Statistical Sleuth: A Course in Methods of Data
Analysis (2nd ed), Duxbury.
References
Olson, A. (1993). Evolutionary and Ecological Interactions Affecting Seaweeds, Ph.D. Thesis.
Oregon State University.
Examples
str(case1301)
# full two-way model with interactions
fitfull <- aov(Cover ~ Treat*Block, case1301)
# Residual plot indicates a transformation might help
plot(fitfull)
# Log of seaweed "regeneration ratio"
y <- with(case1301, log(Cover/(100-Cover)))
# Full two-way model with interactions
fitfull <- aov(y~Treat*Block, case1301)
# No problems indicated by residual plot
plot(fitfull)
# Note that interactions are not statistically significant
anova(fitfull)
# Additive model (no interactions)

26

case1302
fitadditive <- aov(y ~ Treat + Block, case1301)
# Make
lmp 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. (2002). The Statistical Sleuth: A Course in Methods of Data
Analysis (2nd ed), Duxbury.
See Also
ex2018
Examples
str(ex1919)

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

104

ex2012

Format
A data frame with 24 observations on the following 2 variables.
Temp Launch temperature (in degrees Fahrenheit)
Failure Indicator of O-ring failure
Source
Ramsey, F.L. and Schafer, D.W. (2002). The Statistical Sleuth: A Course in Methods of Data
Analysis (2nd ed), Duxbury.
See Also
case0401, ex2223
Examples
str(ex2011)

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. (2002). The Statistical Sleuth: A Course in Methods of Data
Analysis (2nd ed), Duxbury.

ex2015

105

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

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. (2002). The Statistical Sleuth: A Course in Methods of Data
Analysis (2nd ed), Duxbury.
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)

106

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 Age, factor with levels "adult" and "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. (2002). The Statistical Sleuth: A Course in Methods of Data
Analysis (2nd ed), Duxbury.
See Also
case0201, ex0221
Examples
str(ex2016)

ex2017

ex2017

107

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+.
Repress Average civil rights score for the period of authoritarian rule until 1979
Compete Percentage increase of competitive religious groups during the period 1900–1970

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

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)

108

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 2321 observations on the following 4 variables.
Make Type of sports utility vehicle, factor with levels "Other" and "Ford"
Vehicle.age Vehicle age (in years); surrogate for age of tires
Passengers Number of passengers
Cause Cause of fatal accident, factor with levels "Not_Tire" 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. (2002). The Statistical Sleuth: A Course in Methods of Data
Analysis (2nd ed), Duxbury.
See Also
ex1919
Examples
str(ex2018)

ex2115

ex2115

109

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
Format
A data frame with 8 observations on the following 5 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
M Number of people interviewed
Percent 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?”

110

ex2116
(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. (2002). The Statistical Sleuth: A Course in Methods of Data
Analysis (2nd ed), Duxbury.
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)

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. (2002). The Statistical Sleuth: A Course in Methods of Data
Analysis (2nd ed), Duxbury.

ex2117

111

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.
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. (2002). The Statistical Sleuth: A Course in Methods of Data
Analysis (2nd ed), Duxbury.
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)

112

ex2118

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
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. (2002). The Statistical Sleuth: A Course in Methods of Data
Analysis (2nd ed), Duxbury.
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

ex2119

113

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
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. (2002). The Statistical Sleuth: A Course in Methods of Data
Analysis (2nd ed), Duxbury.
References
Data gathered from various sources by Karolyn Kolassa as part of a Master’s project, Oregon State
University.
Examples
str(ex2119)

114

ex2216

ex22.20

Cancer Death of Atomic Bomb Survivors

Description
The data in this data frame are the number of cancer deaths among survivors of the atomic bombs
dropped on Japan during World War II, categorised by time (years) after the bomb that death occurred and the amount of radiation exposure that the survivors received from the blast (Data from
D.A. Pierce, personal communication.) Also listed in each cell is the person-years at risk, in 100s.
This is the sum total of all years spent by all persons in the category.
Usage
ex22.20
Format
A data frame with 42 observations on the following 4 variables.
Exposure Estimated exposure to radiation (in rads)
Years Years after exposure, factor with 7 levels
Deaths Number of cancer deaths
Risk Person-years at risk (in 100s)
Source
Ramsey, F.L. and Schafer, D.W. (2002). The Statistical Sleuth: A Course in Methods of Data
Analysis (2nd ed), Duxbury.
Examples
str(ex22.20)

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

ex2222

115

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. (2002). The Statistical Sleuth: A Course in Methods of Data
Analysis (2nd ed), Duxbury.
References
Phillips, D.P. (1978). Airplane Accident Fatalities Increase Just After Newspaper Stories About
Murder and Suicide, Science 201: 748–750.
Examples
str(ex2216)

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.
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. (2002). The Statistical Sleuth: A Course in Methods of Data
Analysis (2nd ed), Duxbury.

116

ex2223

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)
Incident Numbers of O-ring incidents
Source
Ramsey, F.L. and Schafer, D.W. (2002). The Statistical Sleuth: A Course in Methods of Data
Analysis (2nd ed), Duxbury.
See Also
case0401, ex2011
Examples
str(ex2223)

ex2224

ex2224

117

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 System, factor with 5 levels
Operator Operator type, factor with 4 levels
Valve Valve type, factor with 6 levels
Size Head size, factor with 3 levels (less than 2 inches, 2–10 inches and 10–30 inches)
Mode Operation mode, factor with 2 levels
Failures Number of failures observed
Time Lengths of observation time

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

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.

Examples
str(ex2224)

118

ex2414

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. (2002). The Statistical Sleuth: A Course in Methods of Data
Analysis (2nd ed), Duxbury.
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)

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

Sleuth2Manual

119

Format
A data frame with 71 observations on the following 4 variables.
Percent percentage of frog eggs failing to hatch
Treat factor variable with levels "NoFilter", "UV-BTransmitting" and "UV-BBlocking"
Location factor variable with levels "ThreeCreeks", "SparksLake", "SmallLake" and "LostLake"
Phtolyas Photolyase activity
Source
Ramsey, F.L. and Schafer, D.W. (2002). The Statistical Sleuth: A Course in Methods of Data
Analysis (2nd ed), Duxbury.
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)

Sleuth2Manual

Manual of the R Sleuth2 package

Description
If the option “pdfviewer” is set, this command will display the PDF version of the help pages.
Usage
Sleuth2Manual()
Author(s)
Berwin A Turlach 
References
Ramsey, F.L. and Schafer, D.W. (2002). The Statistical Sleuth: A Course in Methods of Data
Analysis (2nd ed), Duxbury.
Examples
## Not run: Sleuth2Manual()

Index
ex0116, 43
ex0211, 44
ex0221, 44
ex0222, 45
ex0223, 46
ex0321, 46
ex0323, 47
ex0327, 48
ex0328, 49
ex0331, 49
ex0332, 50
ex0333, 51
ex0428, 51
ex0429, 52
ex0430, 53
ex0431, 53
ex0432, 54
ex0518, 55
ex0523, 55
ex0524, 56
ex0621, 57
ex0622, 57
ex0723, 59
ex0724, 60
ex0726, 61
ex0727, 61
ex0728, 62
ex0729, 63
ex0730, 63
ex0816, 64
ex0817, 65
ex0818, 66
ex0820, 66
ex0822, 67
ex0823, 68
ex0824, 69
ex0825, 69
ex0914, 70
ex0915, 71
ex0918, 71
ex0920, 72
ex1014, 73
ex1026, 74

∗Topic datasets
case0101, 5
case0102, 5
case0201, 6
case0202, 7
case0301, 8
case0302, 9
case0401, 9
case0402, 10
case0501, 11
case0502, 12
case0601, 13
case0602, 14
case0701, 15
case0702, 16
case0801, 17
case0802, 17
case0901, 18
case0902, 19
case1001, 19
case1002, 20
case1101, 21
case1102, 22
case1201, 23
case1202, 24
case1301, 25
case1302, 26
case1401, 27
case1402, 28
case1501, 29
case1502, 30
case1601, 31
case1602, 32
case1701, 33
case1702, 34
case1902, 35
case2001, 37
case2002, 38
case2101, 39
case2102, 40
case2201, 41
case2202, 41
ex0112, 42
120

INDEX
ex1027, 74
ex1028, 75
ex1029, 76
ex1115, 77
ex1120, 77
ex1122, 78
ex1123, 79
ex1124, 80
ex1217, 80
ex1220, 82
ex1221, 83
ex1222, 84
ex1317, 85
ex1319, 85
ex1320, 86
ex1414, 87
ex1415, 88
ex1417, 89
ex1509, 89
ex1512, 90
ex1513, 91
ex1514, 91
ex1515, 92
ex1605, 93
ex1611, 94
ex1612, 94
ex1613, 95
ex1614, 96
ex1615, 96
ex1708, 97
ex1713, 98
ex1714, 99
ex1914, 100
ex1916, 100
ex1917, 101
ex1918, 102
ex1919, 103
ex2011, 103
ex2012, 104
ex2015, 105
ex2016, 106
ex2017, 107
ex2018, 108
ex2115, 109
ex2116, 110
ex2117, 111
ex2118, 112
ex2119, 113
ex22.20, 114
ex2216, 114
ex2222, 115
ex2223, 116

121
ex2224, 117
ex2225, 118
ex2414, 118
∗Topic documentation
Sleuth2Manual, 119
∗Topic package
Sleuth2-package, 4
case0101, 5
case0102, 5, 24
case0201, 6, 45, 106
case0202, 7
case0301, 8
case0302, 9
case0401, 9, 104, 116
case0402, 10
case0501, 11
case0502, 12
case0601, 13
case0602, 14
case0701, 15, 61, 62
case0702, 16, 64, 65
case0801, 17
case0802, 17
case0901, 18
case0902, 19, 19, 51
case1001, 19
case1002, 20
case1101, 21
case1102, 22
case1201, 23
case1202, 6, 24
case1301, 25
case1302, 26
case1401, 27
case1402, 28
case1501, 29
case1502, 30
case1601, 31
case1602, 32
case1701, 33
case1702, 34
case1902, 35
case2001, 37, 102
case2002, 38
case2101, 39
case2102, 40
case2201, 41
case2202, 41
ex0112, 42
ex0116, 43
ex0211, 44

122
ex0221, 7, 44, 106
ex0222, 45
ex0223, 46
ex0321, 46
ex0323, 47
ex0327, 48
ex0328, 49
ex0331, 49
ex0332, 50
ex0333, 51
ex0428, 51
ex0429, 52
ex0430, 53
ex0431, 53
ex0432, 54
ex0518, 55
ex0523, 55, 78
ex0524, 56
ex0621, 57
ex0622, 57
ex0723, 59
ex0724, 60
ex0726, 61
ex0727, 16, 61
ex0728, 62
ex0729, 63
ex0730, 63
ex0816, 16, 64
ex0817, 65
ex0818, 66
ex0820, 66, 77
ex0822, 67
ex0823, 68
ex0824, 69
ex0825, 69, 84
ex0914, 70
ex0915, 71
ex0918, 71
ex0920, 72
ex1014, 73
ex1026, 74
ex1027, 74
ex1028, 75
ex1029, 76
ex1115, 67, 77
ex1120, 56, 77
ex1122, 78
ex1123, 79, 80, 81
ex1124, 80
ex1217, 79, 80
ex1220, 82
ex1221, 83

INDEX
ex1222, 70, 84
ex1317, 85
ex1319, 85, 93
ex1320, 86
ex1414, 87, 88
ex1415, 87, 88
ex1417, 89
ex1509, 89
ex1512, 90
ex1513, 91
ex1514, 91
ex1515, 92
ex1605, 86, 93
ex1611, 94
ex1612, 94
ex1613, 95
ex1614, 96
ex1615, 96
ex1708, 97
ex1713, 98
ex1714, 99
ex1914, 100
ex1916, 100
ex1917, 101
ex1918, 37, 102
ex1919, 103, 108
ex2011, 10, 103, 116
ex2012, 104
ex2015, 105
ex2016, 7, 45, 106
ex2017, 107
ex2018, 103, 108
ex2115, 109
ex2116, 110
ex2117, 111
ex2118, 112
ex2119, 113
ex22.20, 114
ex2216, 114
ex2222, 115
ex2223, 10, 104, 116
ex2224, 117
ex2225, 118
ex2414, 118
Sleuth2 (Sleuth2-package), 4
Sleuth2-package, 4
Sleuth2Manual, 119
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