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R/datasets.R
In fastR: Foundations and Applications of Statistics Using R
# substitution to correct auto-generated file markup s:list("\([^)]*\)"):\1:g
#' Michael Jordan personal scoring
#'
#' The number of points scored by Michael Jordan in each game of the 1986-87
#' regular season.
#'
#'
#' @name Jordan8687
#' @rdname Jordan8687
#' @docType data
#' @format A data frame with 82 observations on the following 2 variables.
#' \itemize{ \item{Game}{a numeric vector} \item{Points}{a
#' numeric vector} }
#' @keywords datasets
#' @examples
#'
#' if (require(mosaicData)) {
#' data(Jordan8687)
#' xqqmath(~Points, data=Jordan8687)
#' }
#'
NULL
#' ACT scores and GPA
#'
#' ACT scores and college GPA for a small sample of college students.
#'
#'
#' @name actgpa
#' @rdname actgpa
#' @docType data
#' @format A data frame with 26 observations on the following 2 variables.
#' \itemize{ \item{ACT}{ a numeric vector} \item{GPA}{ a numeric
#' vector} }
#' @keywords datasets
#' @examples
#'
#' if (require(mosaicData)) {
#' xyplot(GPA ~ ACT, data=actgpa)
#' }
#'
NULL
#' Airline On-Time Arrival Data
#'
#' Flights categorized by destination city, airline, and whether or not the
#' flight was on time.
#'
#'
#' @name airlineArrival
#' @rdname airlineArrival
#' @docType data
#' @format A data frame with 11000 observations on the following 3 variables.
#' \itemize{ \item{Airport}{ a factor with levels \code{LosAngeles}
#' \code{Phoenix} \code{SanDiego} \code{SanFrancisco} \code{Seattle}}
#' \item{Result}{ a factor with levels \code{Delayed} \code{OnTime}}
#' \item{Airline}{ a factor with levels \code{Alaska}
#' \code{AmericaWest}} }
#' @references These and similar data appear in many text books under the topic
#' of Simpson's paradox.
#' @source Barnett, Arnold. 1994. ``How numbers can trick you.''
#' \emph{Technology Review}, vol. 97, no. 7, pp. 38--45.
#' @keywords datasets
#' @examples
#'
#' row.perc(xtabs(~Airline+Result, data=airlineArrival))
#' for (city in levels(airlineArrival$Airport)) {
#' cat(paste('\nArriving in ', city,':\n',sep=''))
#' print(row.perc(xtabs(~Airline+Result, airlineArrival,
#' subset=Airport==city)))
#' }
#'
NULL
#' Air pollution measurements
#'
#' Air pollution measurements at three locations.
#'
#'
#' @name airpollution
#' @rdname airpollution
#' @docType data
#' @format A data frame with 6 observations on the following 2 variables.
#' \itemize{ \item{pollution}{ a numeric vector}
#' \item{location}{ a factor with levels \code{Hill Suburb} \code{Plains
#' Suburb} \code{Urban City}} }
#' @source David J. Saville and Graham R. Wood, \emph{Statistical methods: A
#' geometric primer}, Springer, 1996.
#' @keywords datasets
#' @examples
#'
#' data(airpollution)
#' summary(lm(pollution ~ location, data=airpollution))
#'
NULL
#' Ball dropping data
#'
#' Undergraduate students in a physics lab recorded the height from which a
#' ball was dropped and the time it took to reach the floor.
#'
#'
#' @name balldrop
#' @rdname balldrop
#' @docType data
#' @format A data frame with 30 observations on the following 2 variables.
#' \itemize{ \item{height}{ height in meters} \item{time}{ time
#' in seconds} }
#' @source Steve Plath, Calvin College Physics Department
#' @keywords datasets
#' @examples
#'
#' xyplot(time ~ height, data=balldrop)
#'
NULL
#' Major League Batting 2000-2005
#'
#' Major League batting data for the seasons from 2000-2005.
#'
#'
#' @name batting
#' @rdname batting
#' @docType data
#' @format A data frame with 8062 observations on the following 22 variables.
#' \itemize{ \item{player}{ unique identifier for each player}
#' \item{year}{ year} \item{stint}{ for players who were traded
#' mid-season, indicates which portion of the season the data cover}
#' \item{team}{ three-letter code for team} \item{league}{ a
#' factor with levels \code{AA} \code{AL} \code{NL}} \item{G}{ games}
#' \item{AB}{ at bats} \item{R}{ runs} \item{H}{ hits}
#' \item{H2B}{ doubles} \item{H3B}{ triples}
#' \item{HR}{ home runs} \item{RBI}{ runs batted in}
#' \item{SB}{ stolen bases} \item{CS}{ caught stealing}
#' \item{BB}{ bases on balls (walks)} \item{SO}{ strike outs}
#' \item{IBB}{ intentional base on balls} \item{HBP}{ hit by
#' pitch} \item{SH}{ a numeric vector} \item{SF}{ sacrifice fly}
#' \item{GIDP}{ grounded into double play} }
#' @keywords datasets
#' @examples
#'
#' data(batting)
#' histogram(~HR,batting)
#'
NULL
#' Births by Day
#'
#' The number of live births in the United States for each day of the year
#' 1978.
#'
#'
#' @name births78
#' @rdname births78
#' @docType data
#' @format A data frame with 365 observations on the following 3 variables.
#' \itemize{ \item{date}{ date (as a factor)}
#' \item{births}{ number of live births} \item{dayofyear}{ number
#' of days since start of year} }
#' @keywords datasets
#' @examples
#'
#' data(births78)
#' xyplot(births ~ dayofyear, births78)
#'
NULL
#' Buckthorn
#'
#' Data from an experiment to determine the efficacy of various methods of
#' eradicating buckthorn, an invasive woody shrub. Buckthorn plants were
#' chopped down and the stumps treated with various concentrations of
#' glyphosate. The next season, researchers returned to see whether the plant
#' had regrown.
#'
#'
#' @name buckthorn
#' @rdname buckthorn
#' @docType data
#' @format A data frame with 165 observations on the following 3 variables.
#' \itemize{ \item{shoots}{ number of new shoots coming from stump}
#' \item{conc}{ concentration of glyphosate applied}
#' \item{dead}{ weather the stump was considered dead} }
#' @source David Dornbos, Calvin College
#' @keywords datasets
#' @examples
#'
#' data(buckthorn)
#'
NULL
#' Bugs
#'
#' This data frame contains data from an experiment to see if insects are more
#' attracted to some colors than to others. The researchers prepared colored
#' cards with a sticky substance so that insects that landed on them could not
#' escape. The cards were placed in a field of oats in July. Later the
#' researchers returned, collected the cards, and counted the number of cereal
#' leaf beetles trapped on each card.
#'
#'
#' @name bugs
#' @rdname bugs
#' @docType data
#' @format A data frame with 24 observations on the following 2 variables.
#' \itemize{ \item{Color}{ color of card; one of \code{B}(lue)
#' \code{G}(reen) \code{W}(hite) \code{Y}(ellow)} \item{NumTrap}{ number
#' of insects trapped on the card} }
#' @source M. C. Wilson and R. E. Shade, Relative attractiveness of various
#' luminescent colors to the cereal leaf beetle and the meadow spittlebug,
#' \emph{Journal of Economic Entomology} 60 (1967), 578--580.
#'
#' @keywords datasets
#'
#' @examples
#'
#' data(bugs)
#' summary(NumTrap ~ Color, bugs, fun=favstats)
#'
NULL
#' Concrete Compressive Strength Data
#'
#' These data were collected by I-Cheng Yeh to determine how the compressive
#' strength of concrete is related to its ingredients (cement, blast furnace
#' slag, fly ash, water, superplasticizer, coarse aggregate, and fine
#' aggregate) and age.
#'
#'
#' @name concrete
#' @rdname concrete
#' @aliases concreteAll concrete28
#' @docType data
#' @format \code{concreteAll} is a data frame with the following 9 variables.
#' \itemize{ \item{cement}{ amount of cement (kg/m^3)}
#' \item{slag}{ amount of blast furnace slag (kg/m^3)}
#' \item{ash}{ amount of fly ash(kg/m^3)} \item{water}{ amount of
#' water (kg/m^3)} \item{superP}{ amount of superplasticizer (kg/m^3)}
#' \item{coarseAg}{ amount of coarse aggregate (kg/m^3)}
#' \item{fineAg}{ amount of fine aggregate (kg/m^3)}
#' \item{age}{ age of concrete in days }
#' \item{strength}{ compressive strength measured in MPa} }
#' \code{concrete28} is a subset of \code{concreteAll}.
#' @references I-Cheng Yeh (1998), "Modeling of strength of high performance
#' concrete using artificial neural networks," \cite{Cement and Concrete
#' Research}, Vol. 28, No. 12, pp. 1797-1808.
#' @source Data were obtained from the Machine Learning Repository
#' (\url{http://archive.ics.uci.edu/ml/}) where they were deposited by I-Cheng
#' Yeh (\email{icyeh at chu.edu.tw}) who retains the copyright for these data.
#' @keywords datasets
#' @examples
#'
#' data(concreteAll)
#' data(concrete28)
#'
NULL
#' Corn Yield
#'
#' William Gosset analyzed data from an experiment comparing the yield of
#' regular and kiln-dried corn.
#'
#' Gosset (Student) reported on the results of seeding plots with two different
#' kinds of seed. Each type of seed (regular and kiln-dried) was planted in
#' adjacent plots, accounting for 11 pairs of "split" plots.
#'
#' @name corn
#' @rdname corn
#' @docType data
#' @format A data frame with 11 observations on the following 2 variables.
#' \itemize{ \item{reg}{ yield of regular corn (lbs/acre)}
#' \item{kiln}{ yield of kiln-dried corn (lbs/acre)} }
#' @references W.S. Gosset, "The Probable Error of a Mean," Biometrika, 6
#' (1908), pp 1-25.
#' @source These data are also available at DASL, the data and story library
#' (\url{http://lib.stat.cmu.edu/DASL/}).
#' @keywords datasets
#' @examples
#'
#' corn2 <- stack(corn)
#' names(corn2) <- c('yield','treatment')
#' lm(yield ~ treatment, data = corn2)
#' t.test(yield ~ treatment, data=corn2)
#' t.test(corn$reg, corn$kiln)
#'
NULL
#' Cuckoo eggs in other birds' nests
#'
#' Cuckoos are knows to lay their eggs in the nests of other (host) birds. The
#' eggs are then adopted and hatched by the host birds. These data were
#' originally collected by O. M. Latter in 1902 to see how the size of a cuckoo
#' egg is related to the species of the host bird.
#'
#'
#' @name cuckoo
#' @rdname cuckoo
#' @docType data
#' @format A data frame with 120 observations on the following 2 variables.
#' \itemize{ \item{length}{ length of egg (mm)}
#' \item{species}{ a factor with levels \code{hedge sparrow}
#' \code{meadow pipet} \code{pied wagtail} \code{robin} \code{tree pipet}
#' \code{wren}} }
#' @references These data are also available from DASL, the data and story
#' library
#' (\url{http://lib.stat.cmu.edu/DASL/}).
#' @source L.H.C. Tippett, \emph{The Methods of Statistics}, 4th Edition, John
#' Wiley and Sons, Inc., 1952, p. 176.
#' @keywords datasets
#' @examples
#'
#' data(cuckoo)
#' bwplot(length~species,cuckoo)
#'
NULL
#' Death Penalty and Race
#'
#' A famous example of Simpson's paradox.
#'
#'
#' @name deathPenalty
#' @rdname deathPenalty
#' @aliases deathPenalty deathPen
#' @docType data
#' @format A data frame with 326 observations. The factors are coded more
#' succinctly in \code{deathPen}, but otherwise the data are the same.
#' \itemize{ \item{DeathPenalty}{ a factor with levels \code{Yes}
#' \code{No}} \item{Penalty}{ a factor with levels \code{Death}
#' \code{Not}} \item{Victim}{ a factor with levels \code{Black}
#' \code{White} (or \code{Bl} \code{Wh})} \item{Defendant}{ a factor
#' with levels \code{Black} \code{White} (or \code{Bl} \code{Wh})} }
#' @source Radelet, M. (1981). Racial characteristics and imposition of the
#' death penalty. \emph{American Sociological Review}, 46:918--927.
#' @keywords datasets
#' @examples
#'
#' xtabs(~Defendant+Penalty,deathPenalty)
#' xtabs(~Defendant+Victim+Penalty,deathPenalty)
#'
NULL
#' Drag force experiment
#'
#' The data come from an experiment to determine how terminal velocity depends
#' on the mass of the falling object. A helium balloon was rigged with a small
#' basket and just the ballast to make it neutrally buoyant. Mass was then
#' added and the terminal velocity is calculated by measuring the time it took
#' to fall between two sensors once terminal velocity has been reached. Larger
#' masses were drop from higher heights and used sensors more widely spaced.
#'
#'
#' @name drag
#' @rdname drag
#' @docType data
#' @format A data frame with 42 observations on the following 5 variables.
#' \itemize{ \item{time}{ time (in seconds) to travel between two
#' sensors} \item{mass}{ net mass (in kg) of falling object}
#' \item{height}{ distance (in meters) between two sensors}
#' \item{velocity}{ average velocity (in m/s) computed from \code{time}
#' and \code{height}} \item{force.drag}{ calculated drag force (in N,
#' \code{force.drag = mass * 9.8}) using the fact that at terminal velocity,
#' the drag force is equal to the force of gravity} }
#' @source Calvin College physics students under the supervision of Professor
#' Steve Plath.
#' @keywords datasets
#' @examples
#'
#' data(drag)
#' with(drag,force.drag / mass)
#' xyplot(velocity ~ mass, drag)
#'
NULL
#' Endurance and vitamin C
#'
#' The effect of a single 600 mg dose of ascorbic acid versus a sugar placebo
#' on the muscular endurance (as measured by repetitive grip strength trials)
#' of fifteen male volunteers (19-23 years old).
#'
#' Three initial maximal contractions were performed for each subject, with the
#' greatest value indicating maximal grip strength. Muscular endurance was
#' measured by having the subjects squeeze the dynamometer, hold the
#' contraction for three seconds, and repeat continuously until a value of 50%
#' maximum grip strength was achieved for three consecutive contractions.
#' Endurance was defined as the number of repetitions required to go from
#' maximum grip strength to the initial 50% value. Subjects were given frequent
#' positive verbal encouragement in an effort to have them complete as many
#' repetitions as possible.
#'
#' The study was conducted in a double-blind manner with crossover.
#'
#' @name endurance
#' @rdname endurance
#' @docType data
#' @format A data frame with 15 observations on the following 5 variables.
#' \itemize{ \item{Vitamin}{ number of repetitions until reaching 50%
#' maximal grip after taking viatimin} \item{First}{ which treatment was
#' done first, a factor with levels \code{Placebo} \code{Vitamin}}
#' \item{Placebo}{ number of repetitions until reaching 50% maximal grip
#' strength after taking placebo} }
#' @references Keith, R. E., and Merrill, E. (1983). The effects of vitamin C
#' on maximum grip strength and muscular endurance. \emph{Journal of Sports
#' Medicine and Physical Fitness}, 23, 253-256.
#' @source These data are available from OzDASL, the Australasian data and
#' story library (\url{http://www.statsci.org/data/}).
#' @keywords datasets
#' @examples
#'
#' data(endurance)
#' t.test(endurance$Vitamin, endurance$Placebo, paired=TRUE)
#' t.test(log(endurance$Vitamin), log(endurance$Placebo), paired=TRUE)
#' t.test(1/endurance$Vitamin, 1/endurance$Placebo, paired=TRUE)
#' xqqmath( ~ Vitamin - Placebo, data = endurance)
#' xqqmath( ~ log(Vitamin) - log(Placebo), data = endurance)
#' xqqmath( ~ 1/Vitamin - 1/Placebo, data = endurance)
#'
NULL
#' Family smoking
#'
#' A cross-tabulation of whether a student smokes and how many of his or her
#' parents smoke from a study conducted in the 1960's.
#'
#'
#' @name familySmoking
#' @rdname familySmoking
#' @docType data
#' @format A data frame with 5375 observations on the following 2 variables.
#' \itemize{ \item{Student}{ a factor with levels \code{DoesNotSmoke}
#' \code{Smokes}} \item{Parents}{ a factor with levels
#' \code{NeitherSmokes} \code{OneSmokes}} \code{BothSmoke} }
#' @references The data also appear in
#'
#' Brigitte Baldi and David S. Moore, \emph{The Practice of Statistics in the
#' Life Sciences}, Freeman, 2009.
#' @source S. V. Zagona (ed.), \emph{Studies and issues in smoking behavior},
#' University of Arizona Press, 1967.
#' @keywords datasets
#' @examples
#'
#' data(familySmoking)
#' xchisq.test( xtabs(~Parents + Student, familySmoking) )
#'
NULL
#' NCAA football fumbles
#'
#' This data frame gives the number of fumbles by each NCAA FBS team for the
#' first three weeks in November, 2010.
#'
#' The fumble counts listed here are total fumbles, not fumbles lost. Some of
#' these fumbles were recovered by the team that fumbled.
#'
#' @name fumbles
#' @rdname fumbles
#' @docType data
#' @format A data frame with 120 observations on the following 7 variables.
#' \itemize{ \item{team}{ NCAA football team} \item{rank}{ rank
#' based on fumbles per game through games on November 26, 2010}
#' \item{W}{ number of wins through games on November 26, 2010}
#' \item{L}{ number of losses through games on November 26, 2010}
#' \item{week1}{ number of fumbles on November 6, 2010}
#' \item{week2}{ number of fumbles on November 13, 2010}
#' \item{week3}{ number of fumbles on November 20, 2010} }
#' @source
#' \url{http://www.teamrankings.com/college-football/stat/fumbles-per-game}
#' @keywords datasets
#' @examples
#'
#' data(fumbles)
#' m <- max(fumbles$week1)
#' table(factor(fumbles$week1,levels=0:m))
#' favstats( ~ week1, data=fumbles)
#' # compare with Poisson distribution
#' signif( cbind(
#' fumbles=0:m,
#' observedCount=table(factor(fumbles$week1,levels=0:m)),
#' modelCount= 120* dpois(0:m,mean(fumbles$week1)),
#' observedPct=table(factor(fumbles$week1,levels=0:m))/120,
#' modelPct= dpois(0:m,mean(fumbles$week1))
#' ) ,3)
#' showFumbles <- function(x,lambda=mean(x),...) {
#' mx <- max(x)
#' result <- histogram(~x, type="density", xlim=c(-.5,(mx+2.5)),
#' xlab='number of fumbles',
#' panel=function(x,y,...){
#' panel.histogram(x,alpha=0.8,breaks=seq(-0.5,(mx+2.5),by=1,...))
#' panel.points(0:(mx+2),dpois(0:(mx+2),lambda),pch=19,alpha=0.8)
#' }
#' )
#' print(result)
#' return(result)
#' }
#' showFumbles(fumbles$week1)
#' showFumbles(fumbles$week2)
#' showFumbles(fumbles$week3)
#'
NULL
#' FUSION type 2 diabetes study
#'
#' Phenotype and genotype data from the Finland United States Investigation of
#' NIDDM (type 2) Diabetes (FUSION) study.
#'
#'
#' @name pheno
#' @rdname fusion
#' @aliases pheno fusion1 fusion2
#' @docType data
#' @format Data frames with the following variables. \itemize{
#' \item{id}{ subject ID number for matching between data sets}
#' \item{t2d}{ a factor with levels \code{case} \code{control}}
#' \item{bmi}{ body mass index} \item{sex}{ a factor with levels
#' \code{F} \code{M}} \item{age}{ age of subject at time phenotypes were
#' colelcted} \item{smoker}{ a factor with levels \code{former}
#' \code{never} \code{occasional} \code{regular}} \item{chol}{ total
#' cholesterol} \item{waist}{ waist circumference (cm)}
#' \item{weight}{ weight (kg) } \item{height}{ height (cm) }
#' \item{whr}{ waist hip ratio } \item{sbp}{ systolic blood
#' pressure} \item{dbp}{ diastolic blood pressure}
#' \item{marker}{ RS name of SNP} \item{markerID}{ numeric ID for
#' SNP} \item{allele1}{ first allele coded as 1=A, 2=C, 3=G, 4=T}
#' \item{allele2}{ second allele coded as 1=A, 2=C, 3=G, 4=T}
#' \item{genotype}{ both alleles coded as a factor}
#' \item{Adose}{ number of A alleles} \item{Cdose}{ number of C
#' alleles} \item{Gdose}{ number of G alleles}
#' \item{Tdose}{ number of T alleles} }
#' @source Similar to the data presented in
#'
#' Laura J. Scott, Karen L. Mohlke, Lori L. Bonnycastle, Cristen J. Willer, Yun
#' Li, William L. Duren, Michael R. Erdos, Heather M. Stringham, Pe- ter S.
#' Chines, Anne U. Jackson, Ludmila Prokunina-Olsson, Chia-Jen J. Ding, Amy J.
#' Swift, Narisu Narisu, Tianle Hu, Randall Pruim, Rui Xiao, Xiao- Yi Y. Li,
#' Karen N. Conneely, Nancy L. Riebow, Andrew G. Sprau, Maurine Tong, Peggy P.
#' White, Kurt N. Hetrick, Michael W. Barnhart, Craig W. Bark, Janet L.
#' Goldstein, Lee Watkins, Fang Xiang, Jouko Saramies, Thomas A. Buchanan,
#' Richard M. Watanabe, Timo T. Valle, Leena Kinnunen, Goncalo R. Abecasis,
#' Elizabeth W. Pugh, Kimberly F. Doheny, Richard N. Bergman, Jaakko
#' Tuomilehto, Francis S. Collins, and Michael Boehnke, A genome-wide
#' association study of type 2 diabetes in Finns detects multiple
#' susceptibility vari- ants, \emph{Science} (2007).
#' @keywords datasets
#' @examples
#'
#' data(pheno); data(fusion1); data(fusion2)
#' fusion1m <- merge(fusion1, pheno, by="id", all.x=FALSE, all.y=FALSE)
#' xtabs(~t2d + genotype, data=fusion1m)
#' xtabs(~t2d + Gdose, data=fusion1m)
#' chisq.test( xtabs( ~t2d + genotype, data=fusion1m ) )
#' f1.glm <- glm( factor(t2d) ~ Gdose, data=fusion1m, family=binomial)
#' summary(f1.glm)
#'
NULL
#' Golf ball numbers
#'
#' Allan Rossman used to live on a golf course in a spot where dozens of balls
#' would come into his yard every week. He collected the balls and eventually
#' tallied up the numbers on the first 5000 golf balls he collected. Of these
#' 486 bore the number 1, 2, 3, or 4. The remaining 14 golf balls were omitted
#' from the data.
#'
#'
#' @name golfballs
#' @rdname golfballs
#' @docType data
#' @format The format is: num [1:4] 137 138 107 104
#' @source Data collected by Allan Rossman in Carlisle, PA.
#' @keywords datasets
#' @examples
#'
#' data(golfballs)
#' golfballs/sum(golfballs)
#' chisq.test(golfballs, p=rep(.25,4))
#'
NULL
#' GPA, ACT, and SAT scores
#'
#' GPA, ACT, and SAT scores for a sample of students.
#'
#'
#' @name gpa
#' @rdname gpa
#' @docType data
#' @format A data frame with 271 observations on the following 4 variables.
#' \itemize{ \item{satm}{ SAT mathematics score}
#' \item{satv}{ SAT verbal score} \item{act}{ ACT score}
#' \item{gpa}{ college grade point average} }
#' @keywords datasets
#' @examples
#'
#' data(gpa)
#' splom(gpa)
#'
NULL
#' Punting helium- and air-filled footballs
#'
#' Two identical footballs, one air-filled and one helium-filled, were used
#' outdoors on a windless day at The Ohio State University's athletic complex.
#' Each football was kicked 39 times and the two footballs were alternated with
#' each kick. The experimenter recorded the distance traveled by each ball.
#'
#'
#' @name heliumFootballs
#' @rdname heliumFootballs
#' @docType data
#' @format A data frame with 39 observations on the following 3 variables.
#' \itemize{ \item{Trial}{ trial number} \item{Air}{ distance
#' traveled by air-filled football (yards)} \item{Helium}{ distance
#' traveled by helium-filled football (yards)} }
#' @references Lafferty, M. B. (1993), "OSU scientists get a kick out of sports
#' controversy", \emph{The Columbus Dispatch} (November, 21, 1993), B7.
#' @source These data are available from DASL, the data and story library
# (\url{ftp://sunsite.univie.ac.at/mirrors/lib.stat.cmu.edu/DASL/.index.html}).
#' (\url{http://lib.stat.cmu.edu/DASL/}).
#' @keywords datasets
#' @examples
#'
#' data(heliumFootballs)
#' xyplot(Helium~Air, data=heliumFootballs)
#' bwplot(~(Helium-Air), data=heliumFootballs)
#'
NULL
#' Cooling muscles with ice
#'
#' This data set contains the results of an experiment comparing the efficacy
#' of different forms of dry ice application in reducing the temperature of the
#' calf muscle.
#'
#' The 12 subjects in this study came three times, at least four days apart,
#' and received one of three ice treatments (cubed ice, crushed ice, or ice
#' mixed with water). In each case, the ice was prepared in a plastic bag and
#' applied dry to the subjects calf muscle. The temperature measurements were
#' taken on the skin surface and inside the calf muscle (via a 4 cm long probe)
#' every 30 seconds for 20 minutes prior to icing, for 20 minutes during icing,
#' and for 2 hours after the ice had been removed. The temperature
#' measurements are stored in variables that begin with \code{B} (baseline),
#' \code{T} (treatment), or \code{R} (recovery) followed by a numerical code
#' for the elapsed time formed by concatenating the number of minutes and
#' seconds. For example, \code{R1230} contains the temperatures 12 minutes and
#' 30 seconds after the ice had been removed.
#'
#' Variables include \itemize{ \item{Subject}{ identification number}
#' \item{Sex}{ a factor with levels \code{female} \code{male}}
#' \item{Weight}{ weight of subject (kg)} \item{Height}{ height
#' of subject (cm)} \item{Skinfold}{ skinfold thickness}
#' \item{Calf}{ calf diameter (cm)} \item{Age}{ age of subject}
#' \item{Location}{ a factor with levels \code{intramuscular}
#' \code{surface}} \item{Treatment}{ a factor with levels \code{crushed}
#' \code{cubed} \code{wet}} \item{B0}{ baseline temperature at time 0}
#' \item{B30}{ baseline temperature 30 seconds after start}
#' \item{B100}{ baseline temperature 1 minute after start}
#' \item{B1930}{ baseline temperature 19 minutes 30 seconds start}
#' \item{T0}{ treatment temperature at beginning of treatment}
#' \item{T30}{ treatment temperature 30 seconds after start of
#' treatment} \item{T100}{ treatment temperature 1 minute after start of
#' treatment} \item{T1930}{ treatment temperature 19 minutes 30 seconds
#' after start of treatment} \item{R0}{ recovery temperature at start of
#' recovery} \item{R30}{ recovery temperature 30 seconds after start of
#' recovery} \item{R100}{ recovery temperature 1 minute after start of
#' recovery} \item{R12000}{ recovery temperature 120 minutes after start
#' of recovery} }
#'
#' @name ice
#' @rdname ice
#' @docType data
#' @source Dykstra, J. H., Hill, H. M., Miller, M. G., Michael T. J., Cheatham,
#' C. C., and Baker, R.J., Comparisons of cubed ice, crushed ice, and wetted
#' ice on intramuscular and surface temperature changes, \emph{Journal of
#' Athletic Training} 44 (2009), no. 2, 136--141.
#' @keywords datasets
#' @examples
#'
#' data(ice)
#' xyplot(Weight ~ Skinfold, groups=Sex, data=ice, auto.key=TRUE)
#'
NULL
#' Inflation data
#'
#' The article developed four measures of central bank independence and
#' explored their relation to inflation outcomes in developed and developing
#' countries. This datafile deals with two of these measures in 23 nations.
#'
#'
#' @name inflation
#' @rdname inflation
#' @docType data
#' @format A data frame with 23 observations on the following 5 variables.
#' \itemize{ \item{country}{ country where data were collected}
#' \item{ques}{ questionnaire index of independence}
#' \item{inf}{ annual inflation rate, 1980-1989 (percent)}
#' \item{legal}{ legal index of independence}
#' \item{dev}{ developed (1) or developing (2) nation} }
#' @references A. Cukierman, S.B. Webb, and B. Negapi, "Measuring the
#' Independence of Central Banks and Its Effect on Policy Outcomes," World Bank
#' Economic Review, Vol. 6 No. 3 (Sept 1992), 353-398.
#' @source These data are available from OzDASL, the Australasian Data and
#' Story Library (\url{http://www.statsci.org/data/}).
#' @keywords datasets
#' @examples
#'
#' data(inflation)
#'
NULL
#' Goals and popularity factors for school kids
#'
#' Subjects were students in grades 4-6 from three school districts in
#' Michigan. Students were selected from urban, suburban, and rural school
#' districts with approximately 1/3 of their sample coming from each district.
#' Students indicated whether good grades, athletic ability, or popularity was
#' most important to them. They also ranked four factors: grades, sports,
#' looks, and money, in order of their importance for popularity. The
#' questionnaire also asked for gender, grade level, and other demographic
#' information.
#'
#'
#' @name kids
#' @rdname kids
#' @docType data
#' @format A data frame with 478 observations on the following 11 variables.
#' \itemize{ \item{Gender}{ a factor with levels \code{boy}
#' \code{girl}} \item{Grade}{ grade in school}
#' \item{Age}{ student age} \item{Race}{ a factor with levels
#' \code{Other} \code{White}} \item{Urban.Rural}{ a factor with levels
#' \code{Rural} \code{Suburban} \code{Urban}} \item{School}{ a factor
#' with levels \code{Brentwood Elementary} \code{Brentwood Middle} \code{Brown
#' Middle} \code{Elm} \code{Main} \code{Portage} \code{Ridge} \code{Sand}
#' \code{Westdale Middle}} \item{Goals}{ a factor with levels
#' \code{Grades} \code{Popular} \code{Sports}} \item{Grades}{ rank of
#' `make good grades' (1=most important for popularity; 4=least important)}
#' \item{Sports}{ rank of `beging good at sports' (1=most important for
#' popularity; 4=least important)} \item{Looks}{ rank of `beging
#' handsome or pretty' (1=most important for popularity; 4=least important)}
#' \item{Money}{ rank of `having lots of money' (1=most important for
#' popularity; 4=least important)} }
#' @references Chase, M. A., and Dummer, G. M. (1992), "The Role of Sports as a
#' Social Determinant for Children," Research Quarterly for Exercise and Sport,
#' 63, 418-424.
#' @source These data are available at DASL, the data and story library
# (\url{ftp://sunsite.univie.ac.at/mirrors/lib.stat.cmu.edu/DASL/.index.html}).
#' (\url{http://lib.stat.cmu.edu/DASL/}).
#' @keywords datasets
#' @examples
#'
#' data(kids)
#' xtabs(~Goals + Urban.Rural, data=kids)
#' chisq.test(xtabs(~Goals + Urban.Rural, data=kids))
#'
NULL
#' Results from a little survey
#'
#' These data are from a little survey given to a number of students in
#' introductory statistics courses. Several of the items were prepared in
#' multiple versions and distributed randomly to the students.
#'
#'
#' @name littleSurvey
#' @rdname littleSurvey
#' @docType data
#' @format A data frame with 279 observations on the following 20 variables.
#' \itemize{ \item{number}{ a number between 1 and 30}
#' \item{colorVer}{ which version of the 'favorite color' question was
#' on the survey. A factor with levels \code{v1} \code{v2}}
#' \item{color}{ favorite color if among predefined choices. A factor
#' with levels \code{} \code{black} \code{green} \code{other} \code{purple}
#' \code{red}} \item{otherColor}{ favorite color if not among choices
#' above.} \item{animalVer}{ which version of the 'favorite color'
#' question was on the survey. A factor with levels \code{v1} \code{v2}}
#' \item{animal}{ favorite animal if among predefined choices. A factor
#' with levels \code{} \code{elephant} \code{giraffe} \code{lion}
#' \code{other}.} \item{otherAnimal}{ favorite animal if not among the
#' predefined choices.} \item{pulseVer}{ which version of the 'pulse'
#' question was on the survey} \item{pulse}{ self-reported pulse}
#' \item{TVver}{ which of three versions of the TV question was on the
#' survey} \item{tvBox}{ a factor with levels \code{<1} \code{>4}
#' \code{>8} \code{1-2} \code{2-4} \code{4-8} \code{none} \code{other}}
#' \item{tvHours}{ a numeric vector} \item{surpriseVer}{ which of
#' two versions of the 'surprise' question was on the survey}
#' \item{surprise}{ a factor with levels \code{no} \code{yes}}
#' \item{playVer}{ which of two versions of the 'play' question was on
#' the survey} \item{play}{ a factor with levels \code{no} \code{yes}}
#' \item{diseaseVer}{ which of two versions of the 'play' question was
#' on the survey} \item{disease}{ a factor with levels \code{A}
#' \code{B}} \item{homeworkVer}{ which of two versions of the 'homework'
#' question was on the survey} \item{homework}{ a factor with levels
#' \code{A} \code{B}} }
#' @keywords datasets
#' @examples
#'
#' data(littleSurvey)
#' xtabs(~surprise+surpriseVer, data=littleSurvey)
#' xtabs(~disease+diseaseVer, data=littleSurvey)
#'
NULL
#' Test performance and noise
#'
#' In this experiment, hyperactive and control students were given a
#' mathematics test in either a quiet or loud testing environment.
#'
#'
#' @name mathnoise
#' @rdname mathnoise
#' @docType data
#' @format A data frame with 40 observations on the following 3 variables.
#' \itemize{ \item{score}{ score on a mathematics test}
#' \item{noise}{ a factor with levels \code{hi} \code{lo}}
#' \item{group}{ a factor with levels \code{control} \code{hyper}} }
#' @source Sydney S. Zentall and Jandira H. Shaw, Effects of classroom noise on
#' perfor- mance and activity of second-grade hyperactive and control children,
#' \emph{Journal of Educational Psychology} 72 (1980), no. 6, 830.
#' @keywords datasets
#' @examples
#'
#' data(mathnoise)
#' xyplot(score~noise, data=mathnoise, group=group, type='a',
#' auto.key=list(columns=2, lines=TRUE, points=FALSE))
#'
NULL
#' MIAA basketball 2004-2005 season
#'
#' Individual player statistics for the 2004-2005 Michigan Intercollegiate
#' Athletic Association basketball season.
#'
#'
#' @name miaa05
#' @rdname miaa05
#' @docType data
#' @format A data frame with 134 observations on the following 27 variables.
#' \itemize{ \item{Number}{ jersey number}
#' \item{Player}{ player's name} \item{GP}{ games played}
#' \item{GS}{ games started} \item{Min}{ minutes played}
#' \item{AvgMin}{ average minutes played per game}
#' \item{FG}{ field goals made} \item{FGA}{ field goals
#' attempted} \item{FGPct}{ field goal percentage}
#' \item{FG3}{ 3-point field goals made} \item{FG3A}{ 3-point
#' field goals attempted} \item{FG3Pct}{ 3-point field goal percentage}
#' \item{FT}{ free throws made} \item{FTA}{ free throws
#' attempted} \item{FTPct}{ free throw percentage}
#' \item{Off}{ offensive rebounds} \item{Def}{ defensive
#' rebounds} \item{Tot}{ total rebounds } \item{RBG}{ rebounds
#' per game} \item{PF}{ personal fouls} \item{FO}{ games fouled
#' out} \item{A}{ assists} \item{TO}{ turn overs}
#' \item{Blk}{ blocked shots} \item{Stl}{ steals}
#' \item{Pts}{ points scored} \item{PTSG}{ points per game} }
#' @source MIAA sports archives (\url{http://www.miaa.org/})
#' @keywords datasets
#' @examples
#'
#' data(miaa05)
#' histogram(~FTPct, data=miaa05)
#'
NULL
#' Major League Baseball 2004 team data
#'
#' Team batting statistics, runs allowed, and runs scored for the 2004 Major
#' League Baseball season.
#'
#'
#' @name mlb2004
#' @rdname mlb2004
#' @docType data
#' @format A data frame with 30 observations on the following 20 variables.
#' \itemize{ \item{Team}{ team city, a factor}
#' \item{League}{ League, a factor with levels \code{AL} \code{NL}}
#' \item{W}{ number of wins} \item{L}{ number of losses}
#' \item{G}{ number of games} \item{R}{ number of runs scored}
#' \item{OR}{ oppnents' runs -- number of runs allowed}
#' \item{Rdiff}{ run difference -- \code{R - OR}}
#' \item{AB}{ number of at bats} \item{H}{ number of hits}
#' \item{DBL}{ number of doubles} \item{TPL}{ number of triples}
#' \item{HR}{ number of home runs} \item{BB}{ number of walks
#' (bases on balls)} \item{SO}{ number of strike outs}
#' \item{SB}{ number of stolen bases} \item{CS}{ number of times
#' caught stealing} \item{BA}{ batting average}
#' \item{SLG}{ slugging percentage} \item{OBA}{ on base average}
#' }
#' @keywords datasets
#' @examples
#'
#' data(mlb2004)
#' xyplot(W ~ Rdiff, data=mlb2004)
#'
NULL
#' NCAA Division I Basketball Results
#'
#' Results of NCAA basketball games
#'
#'
#' @name ncaa2010
#' @rdname ncaa2010
#' @aliases ncaa2010 ncaa2009 ncaa2008
#' @docType data
#' @format Seven variables describing NCAA Division I basketball games.
#' \itemize{ \item{date}{ date on which game was played}
#' \item{away}{ visiting team} \item{ascore}{ visiting team's
#' score} \item{home}{ home team} \item{hscore}{ home team's
#' score} \item{notes}{ code indicting games played at neutral sites (n
#' or N) or in tournaments (T)} \item{location}{ where game was played}
#' }
#' @source \url{kenpom.com}
#' @keywords datasets
#' @examples
#'
#' data(ncaa2010)
#' # add some additional variables to the data frame
#' ncaa2010$dscore <- ncaa2010$hscore- ncaa2010$ascore
#' ncaa2010$homeTeamWon <- ncaa2010$dscore > 0
#' ncaa2010$numHomeTeamWon <- -1 + 2 * as.numeric(ncaa2010$homeTeamWon)
#' w <- which(ncaa2010$homeTeamWon)
#' ncaa2010$winner <- as.character(ncaa2010$away)
#' ncaa2010$winner[w] <- as.character(ncaa2010$home)[w]
#' ncaa2010$loser <- as.character(ncaa2010$home)
#' ncaa2010$loser[w] <- as.character(ncaa2010$away)[w]
#' ncaa2010$homeTeamWon <- ncaa2010$winner == ncaa2010$home
#' ncaa2010$numHomeTeamWon <- -1 + 2 * as.numeric(ncaa2010$homeTeamWon)
#'
NULL
#' NFL 2007 season
#'
#' Results of National Football League games (2007 season, including playoffs)
#'
#'
#' @name nfl2007
#' @rdname nfl2007
#' @docType data
#' @format A data frame with 267 observations on the following 7 variables.
#' \itemize{ \item{Date}{ date on which game was played}
#' \item{Visitor}{ visiting team} \item{VisitorScore}{ score for
#' visiting team} \item{Home}{ home team} \item{HomeScore}{ score
#' for home team} \item{Line}{ `betting line'}
#' \item{TotalLine}{ 'over/under' line (for combined score of both
#' teams)} }
#' @keywords datasets
#' @examples
#'
#' data(nfl2007); nfl <- nfl2007
#' nfl$dscore <- nfl$HomeScore - nfl$VisitorScore
#' w <- which(nfl$dscore > 0)
#' nfl$winner <- nfl$Visitor; nfl$winner[w] <- nfl$Home[w]
#' nfl$loser <- nfl$Home; nfl$loser[w] <- nfl$Visitor[w]
#' # did the home team win?
#' nfl$homeTeamWon <- nfl$dscore > 0
#' table(nfl$homeTeamWon)
#' table(nfl$dscore > nfl$line)
#'
NULL
#' Noise -- unfinished documentation
#'
#' In order to test the effect of room noise, subjects were given a test under
#' 5 diff sets of conditions: 1) no noise, 2) intermittent low volume, 3)
#' intermittent high volume, 4) continuous low volume, and 5) continuous high
#' volume.
#'
#'
#' @name noise
#' @rdname noise
#' @docType data
#' @format A data frame with 50 observations on the following 5 variables.
#' \itemize{ \item{id}{ subject identifier} \item{score}{ score
#' on the test} \item{condition}{ numeric code for condition}
#' \item{volume}{ a factor with levels \code{high} \code{low}
#' \code{none}} \item{frequency}{ a factor with levels \code{continuous}
#' \code{intermittent} \code{none}} }
#' @keywords datasets
#' @examples
#'
#' data(noise)
#' noise2 <- noise[noise$volume != 'none',]
#' model <- lm(score~volume*frequency, data=noise2)
#' anova(model)
#' xyplot(score~volume,noise2, groups=frequency, type='a',
#' auto.key=list(columns=2, points=FALSE, lines=TRUE))
#'
NULL
#' Palette repair data
#'
#' The palettes data set contains data from a firm that recycles palettes.
#' Palettes from warehouses are bought, repaired, and resold. (Repairing a
#' palette typically involves replacing one or two boards.) The company has
#' four employees who do the repairs. The employer sampled five days for each
#' employee and recorded the number of palettes repaired.
#'
#'
#' @name palettes
#' @rdname palettes
#' @docType data
#' @format A data frame with 20 observations on the following 3 variables.
#' \itemize{ \item{palettes}{ number of palettes repaired}
#' \item{employee}{ a factor with levels \code{A} \code{B} \code{C}
#' \code{D}} \item{day}{ a factor with levels \code{day1} \code{day2}
#' \code{day3} \code{day4} \code{day5}} }
#' @source Michael Stob, Calvin College
#' @keywords datasets
#' @examples
#'
#' data(palettes)
#' # Do the employees differ in the rate at which they repair palettes?
#' pal.lm1 <- lm(palettes~employee,palettes)
#' anova(pal.lm1)
#' # Now using day as a blocking variable
#' pal.lm2 <- lm(palettes~employee+day,palettes)
#' anova(pal.lm2)
#' xyplot(palettes~day, data=palettes,
#' groups=employee,
#' main="Productivity by day and employee",
#' type='b',auto.key=list(columns=4,points=FALSE,lines=TRUE))
#'
NULL
#' Paper airplanes
#'
#' Student-collected data from an experiment investigating the design of paper
#' airplanes.
#'
#' These data were collected by Stewart Fischer and David Tippetts, statistics
#' students at the Queensland University of Technology in a subject taught by
#' Dr. Margaret Mackisack. Here is their description of the data and its
#' collection:
#'
#' The experiment decided upon was to see if by using two different designs of
#' paper aeroplane, how far the plane would travel. In considering this, the
#' question arose, whether different types of paper and different angles of
#' release would have any effect on the distance travelled. Knowing that paper
#' aeroplanes are greatly influenced by wind, we had to find a way to eliminate
#' this factor. We decided to perform the experiment in a hallway of the
#' University, where the effects of wind can be controlled to some extent by
#' closing doors.
#'
#' In order to make the experimental units as homogeneous as possible we
#' allocated one person to a task, so person 1 folded and threw all planes,
#' person 2 calculated the random order assignment, measured all the distances,
#' checked that the angles of flight were right, and checked that the plane
#' release was the same each time.
#'
#' The factors that we considered each had two levels as follows:
#'
#' Paper: A4 size, 80g and 50g
#'
#' Design: High Performance Dual Glider, and Incredibly Simple Glider (patterns
#' attached to original report)
#'
#' Angle of release: Horizontal, or 45 degrees upward.
#'
#' The random order assignment was calculated using the random number function
#' of a calculator. Each combination of factors was assigned a number from one
#' to eight, the random numbers were generated and accordingly the order of the
#' experiment was found.
#'
#' @name paperplanes
#' @rdname paperplanes
#' @docType data
#' @format A data frame with 16 observations on the following 5 variables.
#' \itemize{ \item{distance}{ distance plane traveled (cm)}
#' \item{paper}{ type of paper used} \item{angle}{ a numeric
#' vector} \item{design}{ design of plane (\code{hi performance} or
#' \code{simple})} \item{order}{ order in which planes were thrown} }
#' @references Mackisack, M. S. (1994). What is the use of experiments
#' conducted by statistics students? \emph{Journal of Statistics Education}, 2,
#' no 1.
#' @source These data are also available at OzDASL, the Australasian Data and
#' Story Library (\url{http://www.statsci.org/data/}).
#' @keywords datasets
#' @examples
#'
#' data(paperplanes)
#'
NULL
#' Pendulum data
#'
#' Period and pendulum length for a number of string and mass pendulums
#' constructed by physics students. The same mass was used throughout, but the
#' length of the string was varied from 10cm to 16 m.
#'
#'
#' @name pendulum
#' @rdname pendulum
#' @docType data
#' @format A data frame with 27 observations on the following 3 variables.
#' \itemize{ \item{length}{ length of the pendulum (in meters)}
#' \item{period}{ average time of period (in seconds) over several
#' swings of the pendulum} \item{delta.length}{ an estimate of the
#' accuracy of the length measurement} }
#' @source Calvin College physics students under the direction of Professor
#' Steve Plath.
#' @keywords datasets
#' @examples
#'
#' data(pendulum)
#' xyplot(period ~ length, data=pendulum)
#'
NULL
#' Pets and stress
#'
#' Does having a pet or a friend cause more stress?
#'
#' Fourty-five women, all self-proclaimed dog-lovers, were randomly divided
#' into three groups of subjects. Each performed a stressful task either alone,
#' with a friend present, or with their dog present. The average heart rate
#' during the task was used as a measure of stress.
#'
#' @name petstress
#' @rdname petstress
#' @docType data
#' @format A data frame with 45 observations on the following 2 variables.
#' \itemize{ \item{Group}{ a factor with levels \code{C}ontrol,
#' \code{F}riend, or \code{P}et} \item{Rate}{ average heart rate while
#' performing a stressful task} }
#' @references These data also appear in
#'
#' Brigitte Baldi and David S. Moore, \emph{The Practice of Statistics in the
#' Life Sciences}, Freeman, 2009.
#' @source K. M. Allen, J. Blascovich, J. Tomaka, and R. M. Kelsey, Presence of
#' human friends and pet dogs as moderators of autonomic responses to stress in
#' women, \emph{Journal of Personality and Social Psychology} 61 (1991), no. 4,
#' 582--589.
#' @keywords datasets
#' @examples
#'
#' data(petstress)
#'
NULL
#' Pass the Pigs
#'
#' This data set contains information collected from rolling the pair of pigs
#' (found in the game "Pass the Pigs") 6000 times.
#'
#'
#' In "Pass the Pigs", players roll two pig-shaped rubber dice and earn or lose
#' points depending on the configuration of the rolled pigs. Players compete
#' individually to earn 100 points. On each turn, a player rolls he or she
#' decides to stop or until "pigging out" or
#'
#' The pig configurations and their associated scores are
#'
#' 1 = Dot Up (0)
#'
#' 2 = Dot Down (0)
#'
#' 3 = Trotter (5)
#'
#' 4 = Razorback (5)
#'
#' 5 = Snouter (10)
#'
#' 6 = Leaning Jowler (15)
#'
#' 7 = Pigs are touching one another (-1; lose all points)
#'
#' One pig Dot Up and one Dot Down ends the turn (a "pig out") and results in 0
#' points for the turn. If the pigs touch, the turn is ended and all points
#' for the game must be forfeited. Two pigs in the Dot Up or Dot Down
#' configuration score 1 point. Otherwise, The scores of the two pigs in
#' different configurations are added together. The score is doubled if both
#' both pigs have the same configuration, so, for example, two Snouters are
#' worth 40 rather than 20.
#'
#' The vector \code{pigConfig} is provided to assist in converting from the
#' numeric codes above to pig configurations.
#'
#' @name pigs
#' @rdname pigs
#' @aliases pigs pigConfig
#' @docType data
#' @format A data frame with 6000 observations on the following 6 variables.
#' \itemize{ \item{roll}{ roll number (1-6000)}
#' \item{black}{ numerical code for position of black pig}
#' \item{blackF}{ position of black pig coded as a factor}
#' \item{pink}{ numerical code for position of pink pig}
#' \item{pinkF}{ position of pink pig coded as a factor}
#' \item{score}{ score of the roll} \item{height}{ height from
#' which pigs were rolled (5 or 8 inches)} \item{start}{ starting
#' position of the pigs (0 = both pigs backwards, 1 = one bacwards one
#' forwards, 2 = both forwards)} }
#' @source John C. Kern II, Duquesne University (\email{kern at mathcs.duq.edu})
#' @keywords datasets
#' @examples
#'
#' data(pigs)
#' table(pigConfig[pigs$black])
#'
NULL
#' Major League Baseball 2005 pitching
#'
#' Major League Baseball pitching statistics for the 2005 season.
#'
#'
#' @name pitching2005
#' @rdname pitching2005
#' @docType data
#' @format A data frame with 653 observations on the following 27 variables.
#' \itemize{ \item{playerID}{ unique identifier for each player}
#' \item{yearID}{ year} \item{stint}{ for players who played with
#' multiple teams in the same season, \code{stint} is increased by one each
#' time the player joins a new team} \item{teamID}{ three-letter
#' identifier for team} \item{lgID}{ league team plays in, coded as
#' \code{AL} or \code{NL}} \item{W}{ wins} \item{L}{ losses}
#' \item{G}{ games played in} \item{GS}{ games started}
#' \item{CG}{ complete games} \item{SHO}{ shut outs}
#' \item{SV}{ saves recorded} \item{IPouts}{ outs recorded
#' (innings pitched, measured in outs rather than innings)}
#' \item{H}{ hits allowed} \item{ER}{ earned runs allowed}
#' \item{HR}{ home runs allowed} \item{BB}{ walks (bases on
#' balls) allowed} \item{SO}{ strike outs} \item{BAOpp}{ opposing
#' hitters' batting average} \item{ERA}{ earned run average}
#' \item{IBB}{ intentional walks} \item{WP}{ wild pitches}
#' \item{HBP}{ number of batters hit by pitch} \item{BK}{ balks}
#' \item{BFP}{ batters faced pitching} \item{GF}{ ratio of ground
#' balls to fly balls} \item{R}{ runs allowed} }
#' @keywords datasets
#' @examples
#'
#' data(pitching2005)
#' xyplot(IPouts/3 ~ W, data=pitching2005, ylab="innings pitched", xlab="wins")
#'
NULL
#' Poison data
#'
#' The data give the survival times (in hours) in a 3 x 4 factorial experiment,
#' the factors being (a) three poisons and (b) four treatments. Each
#' combination of the two factors is used for four animals. The allocation to
#' animals is completely randomized.
#'
#'
#' @name poison
#' @rdname poison
#' @docType data
#' @format A data frame with 48 observations on the following 3 variables.
#' \itemize{ \item{Poison}{ type of poison (1, 2, or 3)}
#' \item{Treatment}{ manner of treatment (1, 2, 3, or 4)}
#' \item{Time}{ time until death (hours)} }
#' @references Box, G. E. P., and Cox, D. R. (1964). An analysis of
#' transformations (with Discussion). J. R. Statist. Soc. B, 26, 211-252.
#'
#' Aitkin, M. (1987). Modelling variance heterogeneity in normal regression
#' using GLIM. Appl. Statist., 36, 332-339.
#'
#' Smyth, G. K., and Verbyla, A. P. (1999). Adjusted likelihood methods for
#' modelling dispersion in generalized linear models. Environmetrics 10,
#' 696-709. \url{http://www.statsci.org/smyth/pubs/ties98tr.html}.
#' @source These data are also available from OzDASL, the Australian Data and
#' Story Library (\url{http://www.statsci.org/data/}). (Note: The time measurements
#' of the data at OzDASL are in units of tens of hours.)
#' @keywords datasets
#' @examples
#'
#' data(poison)
#' poison.lm <- lm(Time~factor(Poison) * factor(Treatment), data=poison)
#' xplot(poison.lm,w=c(4,2))
#' anova(poison.lm)
#' # improved fit using a transformation
#' poison.lm2 <- lm(1/Time~factor(Poison) * factor(Treatment), data=poison)
#' xplot(poison.lm2,w=c(4,2))
#' anova(poison.lm)
#'
NULL
#' American football punting
#'
#' Investigators studied physical characteristics and ability in 13 football
#' punters. Each volunteer punted a football ten times. The investigators
#' recorded the average distance for the ten punts, in feet. They also recorded
#' the average hang time (time the ball is in the air before the receiver
#' catches it), and a number of measures of leg strength and flexibility.
#'
#'
#' @name punting
#' @rdname punting
#' @docType data
#' @format A data frame with 13 observations on the following 7 variables.
#' \itemize{ \item{distance}{ mean distance for 10 punts (feet) }
#' \item{hang}{ mean hang time (seconds) }
#' \item{rStrength}{ right leg strength (pounds)}
#' \item{lStrength}{ left leg strength (pounds)}
#' \item{rFlexibility}{ right leg flexibility (degrees)}
#' \item{lFlexibility}{ left leg flexibility (degrees)}
#' \item{oStrength}{ overall leg strength (foot-pounds)} }
#' @references "The relationship between selected physical performance
#' variables and football punting ability" by the Department of Health,
#' Physical Education and Recreation at the Virginia Polytechnic Institute and
#' State University, 1983.
#' @source These data are also available at OzDASL
#' (\url{http://www.statsci.org/data/}).
#' @keywords datasets
#' @examples
#'
#' data(punting)
#' xyplot(hang ~ distance, data=punting)
#'
NULL
#' Rat poison -- unfinished documentation
#'
#' Data from an experiment to see whether flavor and location of rat poison
#' influence the consumption by rats.
#'
#'
#' @name ratpoison
#' @rdname ratpoison
#' @docType data
#' @format A data frame with 20 observations on the following 3 variables.
#' \itemize{ \item{consumption}{ a numeric vector}
#' \item{flavor}{ a factor with levels \code{bread}
#' \code{butter-vanilla} \code{plain} \code{roast beef}}
#' \item{location}{ a factor with levels \code{A} \code{B} \code{C}
#' \code{D} \code{E}} }
#' @keywords datasets
#' @examples
#'
#' data(ratpoison)
#'
NULL
#' Simulated golf ball data
#'
#' A matrix of random golf ball numbers simulated using
#' \code{rmultinom(n=10000,size=486,prob=rep(0.25,4))}.
#'
#'
#' @name rgolfballs
#' @rdname rgolfballs
#' @docType data
#' @keywords datasets
#' @examples
#'
#' data(rgolfballs)
#'
NULL
#' Rubber band launching -- unfinished documentation
#'
#' Results of an experiment comparing a rubber band travels to the amount it
#' was stretched prior to launch.
#'
#'
#' @name rubberband
#' @rdname rubberband
#' @docType data
#' @format A data frame with 16 observations on the following 2 variables.
#' \itemize{ \item{Stretch}{ amount rubber band was stretched before
#' launch} \item{Distance}{ distance rubber band traveled } }
#' @keywords datasets
#' @examples
#'
#' data(rubberband)
#' xyplot(Distance ~ Stretch, data=rubberband, type=c("p","r"))
#'
NULL
#' Maze tracing and scents
#'
#' Subjects were asked to to complete a pencil and paper maze when they were
#' smelling a floral scent and when they were not.
#'
#'
#' @name scent
#' @rdname scent
#' @docType data
#' @format A data frame with 21 observations on the following 12 variables.
#' \itemize{ \item{id}{ ID number} \item{sex}{ a factor with
#' levels \code{F} and\code{M}} \item{smoker}{ a factor with levels
#' \code{N}, \code{Y}} \item{opinion}{ opinion of the odor
#' (\code{indiff}, \code{neg}, or \code{pos}}) \item{age}{ age of
#' subject (in years)} \item{first}{ which treatment was first,
#' \code{scented} or \code{unscented}} \item{u1}{ time (in seconds) in
#' first unscented trial} \item{u2}{ time (in seconds) in second
#' unscented trial} \item{u3}{ time (in seconds) in third unscented
#' trial} \item{s1}{ time (in seconds) in first scented trial}
#' \item{s2}{ time (in seconds) in second scented trial}
#' \item{s3}{ time (in seconds) in third scented trial} }
#' @references Hirsch, A. R., and Johnston, L. H. "Odors and Learning," Smell
#' \& Taste Treatment and Research Foundation, Chicago.
#' @source These data are also available at DASL, the data and story library
# (\url{ftp://sunsite.univie.ac.at/mirrors/lib.stat.cmu.edu/DASL/.index.html}).
#' (\url{http://lib.stat.cmu.edu/DASL/}).
#' @keywords datasets
#' @examples
#'
#' data(scent)
#' summary(scent)
#'
NULL
#' Dwindling soap
#'
#' A bar of soap was weighed after showering to see how much soap was used each
#' shower.
#'
#' According to Rex Boggs:
#'
#' I had a hypothesis that the daily weight of my bar of soap [in grams] in my
#' shower wasn't a linear function, the reason being that the tiny little bar
#' of soap at the end of its life seemed to hang around for just about ever. I
#' wanted to throw it out, but I felt I shouldn't do so until it became
#' unusable. And that seemed to take weeks.
#'
#' Also I had recently bought some digital kitchen scales and felt I needed to
#' use them to justify the cost. I hypothesized that the daily weight of a bar
#' of soap might be dependent upon surface area, and hence would be a quadratic
#' function \dots{} .
#'
#' The data ends at day 22. On day 23 the soap broke into two pieces and one
#' piece went down the plughole.
#'
#' @name soap
#' @rdname soap
#' @docType data
#' @format A data frame with 15 observations on the following 3 variables.
#' \itemize{ \item{Date}{ } \item{Day}{ days since start of soap
#' usage and data collection} \item{Weight}{ weight of bar of soap (in
#' grams) } }
#' @source Data collected by Rex Boggs and available from OzDASL
#' (\url{http://www.statsci.org/data/}).
#' @keywords datasets
#' @examples
#'
#' data(soap)
#' xyplot(Weight~Day, data=soap)
#'
NULL
#' Measuring spheres
#'
#' Measurements of the diameter (in meters) and mass (in kilograms) of a set of
#' steel ball bearings.
#'
#'
#' @name spheres
#' @rdname spheres
#' @docType data
#' @format A data frame with 12 observations on the following 2 variables.
#' \itemize{ \item{diameter}{ diameter of bearing (m)}
#' \item{mass}{ mass of the bearing (kg) } }
#' @references data(spheres)
#' @source These data were collected by Calvin College physics students under
#' the direction of Steve Plath.
#' @keywords datasets
NULL
#' Stepping experiment
#'
#' An experiment was conducted by students at The Ohio State University in the
#' fall of 1993 to explore the nature of the relationship between a person's
#' heart rate and the frequency at which that person stepped up and down on
#' steps of various heights.
#'
#' An experiment was conducted by students at The Ohio State University in the
#' fall of 1993 to explore the nature of the relationship between a person's
#' heart rate and the frequency at which that person stepped up and down on
#' steps of various heights. The response variable, heart rate, was measured in
#' beats per minute. There were two different step heights: 5.75 inches (coded
#' as \code{lo}), and 11.5 inches (coded as \code{hi}). There were three rates
#' of stepping: 14 steps/min. (coded as \code{slow}), 21 steps/min. (coded as
#' \code{medium}), and 28 steps/min. (coded as \code{fast}). This resulted in
#' six possible height/frequency combinations. Each subject performed the
#' activity for three minutes. Subjects were kept on pace by the beat of an
#' electric metronome. One experimenter counted the subject's pulse for 20
#' seconds before and after each trial. The subject always rested between
#' trials until her or his heart rate returned to close to the beginning rate.
#' Another experimenter kept track of the time spent stepping. Each subject was
#' always measured and timed by the same pair of experimenters to reduce
#' variability in the experiment. Each pair of experimenters was treated as a
#' block.
#'
#' @name step
#' @rdname step
#' @docType data
#' @format A data frame with 30 observations on the following 7 variables.
#' \itemize{ \item{order}{ performance order}
#' \item{block}{ number of experimenter block}
#' \item{restHR}{ resting heart rate (beats per minute)}
#' \item{HR}{ final heart rate} \item{height}{ height of step
#' (\code{hi} or \code{lo})} \item{freq}{ whether subject stepped
#' \code{fast}, \code{medium}, or \code{slow}} }
#' @source These data are available at DASL, the data and story library
# (\url{ftp://sunsite.univie.ac.at/mirrors/lib.stat.cmu.edu/DASL/.index.html}).
#' (\url{http://lib.stat.cmu.edu/DASL/}).
#' @keywords datasets
#' @examples
#'
#' data(step)
#' xyplot(HR-restHR ~ freq, data=step, groups=height, type='a')
#' xyplot(HR-restHR ~ height, data=step, groups=freq, type='a')
#'
NULL
#' Stereogram fusion
#'
#' Results of an experiment on the effect of prior information on the time to
#' fuse random dot steregrams. One group (NV) was given either no information
#' or just verbal information about the shape of the embedded object. A second
#' group (group VV) received both verbal information and visual information
#' (e.g., a drawing of the object).
#'
#'
#' @name stereogram
#' @rdname stereogram
#' @docType data
#' @format A data frame with 78 observations on the following 2 variables.
#' \itemize{ \item{Time}{ time until subject was able to fuse a random
#' dot stereogram} \item{Group}{ treatment group: \code{NV}(no visual
#' instructions) \code{VV} (visual instructions)} }
#' @references Frisby, J. P. and Clatworthy, J. L., "Learning to see complex
#' random-dot stereograms," \emph{Perception}, 4, (1975), pp. 173-178.
#'
#' Cleveland, W. S. \emph{Visualizing Data}. 1993.
#' @source These data are available at DASL, the data and story library
# (\url{ftp://sunsite.univie.ac.at/mirrors/lib.stat.cmu.edu/DASL/.index.html}.
#' (\url{http://lib.stat.cmu.edu/DASL/}).
#' @keywords datasets
#' @examples
#'
#' data(stereogram)
#' require(Hmisc)
#' favstats(Time~Group, data=stereogram)
#'
NULL
#' Standardized test scores and GPAs
#'
#' Standardized test scores and GPAs for 1000 students.
#'
#'
#' @name students
#' @rdname students
#' @docType data
#' @format A data frame with 1000 observations on the following 6 variables.
#' \itemize{ \item{ACT}{ ACT score} \item{SAT}{ SAT score}
#' \item{Grad}{ has the student graduated from college?}
#' \item{GradGPA}{ college GPA at graduation} \item{HSGPA}{ high
#' school GPA} \item{Cohort}{ year of graduation or expected graduation}
#' }
#' @keywords datasets
#' @examples
#'
#' data(students)
#' xyplot(ACT ~ SAT, data=students)
#' xyplot(GradGPA ~ HSGPA, data=students)
#'
NULL
#' Taste test data
#'
#' Tthe results from a study comparing different preparation methods for taste
#' test samples.
#'
#' The samples were prepared for tasting using either a coarse screen or a fine
#' screen, and with either a high or low liquid content. A total taste score is
#' recorded for each of 16 groups of 50 testers each. Each group had 25 men and
#' 25 women, each of whom scored the samples on a scale from -3 (terrible) to 3
#' (excellent). The sum of these individual scores is the overall taste score
#' for the group.
#'
#' @name tastetest
#' @rdname tastetest
#' @aliases tastetest taste1
#' @docType data
#' @format A data frame with 16 observations on 2 (\code{taste1}) or 4
#' (\code{tastetest}) variables. \itemize{ \item{score}{ taste score
#' from a group of 50 testers} \item{scr}{ a factor with levels
#' \code{coarse} \code{fine}} \item{liq}{ a factor with levels \code{hi}
#' \code{lo}} \item{type}{ a factor with levels \code{A} \code{B}
#' \code{C} \code{D}} }
#' @source E. Street and M. G. Carroll, \emph{Preliminary evaluation of a food
#' product}, Statistics: A Guide to the Unknown (Judith M. Tanur et al., eds.),
#' Holden-Day, 1972, pp. 220-238.
#' @keywords datasets
#' @examples
#'
#' data(tastetest)
#' data(taste1)
#' require(Hmisc)
#' xyplot(score~scr, data=tastetest)
#' xyplot(score~scr, groups=liq, tastetest, type='a')
#' favstats(score~scr, data=tastetest)
#'
NULL
#' Estimating tirewear
#'
#' Treadwear is estimated by two methods: weight loss and groove wear.
#'
#'
#' @name tirewear
#' @rdname tirewear
#' @docType data
#' @format A data frame with 16 observations on the following 2 variables.
#' \itemize{ \item{weight}{ estimated wear (1000's of miles) base on
#' weight loss} \item{groove}{ estimated wear (1000's of miles) based on
#' groove wear} }
#' @references R. D. Stichler, G. G. Richey, and J. Mandel, "Measurement of
#' Treadware of Commercial Tires", \emph{Rubber Age}, 73:2 (May 1953).
#' @source These data are available at DASL, the Data and Story Library
# (\url{ftp://sunsite.univie.ac.at/mirrors/lib.stat.cmu.edu/DASL/.index.html}).
#' (\url{http://lib.stat.cmu.edu/DASL/}).
#' @keywords datasets
#' @examples
#'
#' data(tirewear)
#' xyplot(weight ~ groove, data=tirewear)
#'
NULL
#' New England traffic fatalities (1951-1959)
#'
#' Used by Tufte as an example of the importance of context, these data show
#' the traffic fatality rates in New England in the 1950s. Connecticut
#' increased enforcement of speed limits in 1956. In their full context, it is
#' difficult to say if the decline in Connecticut traffic fatalities from 1955
#' to 1956 can be attributed to the stricter enforcement.
#'
#'
#' @name traffic
#' @rdname traffic
#' @docType data
#' @format A data frame with 9 observations on the following 6 variables.
#' \itemize{ \item{year}{ a year from 1951 to 1959}
#' \item{cn.deaths}{ number of traffic deaths in Connecticut}
#' \item{ny}{ deaths per 100,000 in New York} \item{cn}{ deaths
#' per 100,000 in Connecticut} \item{ma}{ deaths per 100,000 in
#' Massachusetts} \item{ri}{ deaths per 100,000 in in Rhode Island} }
#' @references Donald T. Campbell and H. Laurence Ross. "The Connecticut
#' Crackdown on Speeding: Time-Series Data in Quasi-Experimental Analysis",
#' \emph{Law \& Society Review} Vol. 3, No. 1 (Aug., 1968), pp. 33-54.
#'
#' Gene V. Glass. "Analysis of Data on the Connecticut Speeding Crackdown as a
#' Time-Series Quasi-Experiment" \emph{Law & Society Review}, Vol. 3, No. 1
#' (Aug., 1968), pp. 55-76.
#' @source Tufte, E. R. \emph{The Visual Display of Quantitative Information},
#' 2nd ed. Graphics Press, 2001.
#' @keywords datasets
#' @examples
#'
#' data(traffic)
#' xyplot(cn.deaths ~ year, data=traffic, type=c('l','g'))
#' trafficLong <- reshape(traffic,direction='long', idvar="year",
#' varying=list(3:6), v.names='deathRate',
#' times=names(traffic)[3:6], timevar='state')
#' xyplot(deathRate~year, groups=state, data=trafficLong, type='b',
#' auto.key=list(lines=TRUE, points=FALSE, columns=2))
#'
NULL
#' Trebuchet data
#'
#' Measurements from an experiment that involved firing projectiles with a
#' small trebuchet under different conditions.
#'
#'
#' @name trebuchet
#' @rdname trebuchet
#' @aliases trebuchet trebuchet1 trebuchet2
#' @docType data
#' @format Data frames with the following variables. \itemize{
#' \item{object}{ the object serving as projectile\code{bean} \code{big
#' washerb} \code{bigWash} \code{BWB} \code{foose} \code{golf} \code{MWB}
#' \code{SWB} \code{tennis ball} \code{wood}}
#' \item{projectileWt}{ weight of projectile (in grams)}
#' \item{counterWt}{ weight of counter weight (in kg)}
#' \item{distance}{ distance projectile traveled (in cm)}
#' \item{form}{ a factor with levels \code{a} \code{b} \code{B} \code{c}
#' describing the configuration of the trebuchet.} }
#' @source Data collected by Andrew Pruim as part of a Science Olympiad
#' competition.
#' @keywords datasets
#' @examples
#'
#' data(trebuchet); data(trebuchet1); data(trebuchet2)
#' xyplot(distance~projectileWt, data=trebuchet1)
#' xyplot(distance~projectileWt, data=trebuchet2)
#' xyplot(distance~projectileWt, groups=projectileWt, data=trebuchet)
#'
NULL
#' Utilities bills
#'
#' Data from utility bills at a residence.
#'
#'
#' @name utilities
#' @rdname utilities
#' @aliases utilities utilities2
#' @docType data
#' @format A data frame the following variables. \itemize{
#' \item{month}{ month (coded as a number)} \item{day}{ day of
#' month on which bill was calculated} \item{year}{ year of bill}
#' \item{temp}{ average temperature (F) for billing period}
#' \item{kwh}{ electricity usage (kwh)} \item{ccf}{ gas usage
#' (ccf)} \item{thermsPerDay}{ a numeric vector}
#' \item{billingDays}{ number of billing days in billing period}
#' \item{totalbill}{ total bill (in dollars)} \item{gasbill}{ gas
#' bill (in dollars)} \item{elecbill}{ exectric bill (in dollars)}
#' \item{notes}{ notes about the billing period}
#' \item{ccfpday}{ average gas usage per day [\code{utilities2} only]}
#' \item{kwhpday}{ average electric usage per day [\code{utilities2}
#' only]} \item{gasbillpday}{ gas bill divided by billing days
#' [\code{utilities2} only]} \item{elecbillpday}{ electric bill divided
#' by billing days a numeric vector [\code{utilities2} only]}
#' \item{totalbillpday}{ total bill divided by billing days a numeric
#' vector [\code{utilities2} only]} \item{therms}{ \code{thermsPerDay *
#' billingDays} [\code{utilities2} only]} \item{monthsSinceY2K}{ months
#' since 2000 [\code{utilities2} only]} }
#' @source Daniel T. Kaplan, \emph{Statistical modeling: A fresh approach},
#' 2009.
#' @keywords datasets
#' @examples
#'
#' data(utilities); data(utilities2)
#' xyplot(gasbill ~ temp, data=utilities)
#' xyplot(gasbillpday ~ temp, data=utilities2)
#'
NULL
#' Women in the workforce
#'
#' The labor force participation rate of women in each of 19 U.S. cities in
#' each of two years. # Reference: United States Department of Labor Statistics
#' # # Authorization: free use # # Description: # # Variable Names: # # 1.
#' City: City in the United States # 2. labor72: Labor Force Participation rate
#' of women in 1972 # 3. labor68: Labor Force Participation rate of women in
#' 1968 # # The Data: #
#'
#' @name workingWomen
#' @rdname workingWomen
#' @docType data
#' @format A data frame with 19 observations on the following 3 variables.
#' \itemize{ \item{city}{ name of a U.S. city (coded as a factor with
#' 19 levels)} \item{labor72}{ percent of women in labor force in 1972}
#' \item{labor68}{ percent of women in labor force in 1968} }
#' @source These data are from the United States Department of Labor Statistics
#' and are also available at DASL, the Data and Story Library
# (\url{ftp://sunsite.univie.ac.at/mirrors/lib.stat.cmu.edu/DASL/.index.html}).
#' (\url{http://lib.stat.cmu.edu/DASL/}).
#' @keywords datasets
#' @examples
#'
#' data(workingWomen)
#' xyplot(labor72 ~ labor68, workingWomen)
#'
NULL
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# substitution to correct auto-generated file markup s:list("\([^)]*\)"):\1:g
#' Michael Jordan personal scoring
#'
#' The number of points scored by Michael Jordan in each game of the 1986-87
#' regular season.
#'
#'
#' @name Jordan8687
#' @rdname Jordan8687
#' @docType data
#' @format A data frame with 82 observations on the following 2 variables.
#' \itemize{ \item{Game}{a numeric vector} \item{Points}{a
#' numeric vector} }
#' @keywords datasets
#' @examples
#'
#' if (require(mosaicData)) {
#' data(Jordan8687)
#' xqqmath(~Points, data=Jordan8687)
#' }
#'
NULL
#' ACT scores and GPA
#'
#' ACT scores and college GPA for a small sample of college students.
#'
#'
#' @name actgpa
#' @rdname actgpa
#' @docType data
#' @format A data frame with 26 observations on the following 2 variables.
#' \itemize{ \item{ACT}{ a numeric vector} \item{GPA}{ a numeric
#' vector} }
#' @keywords datasets
#' @examples
#'
#' if (require(mosaicData)) {
#' xyplot(GPA ~ ACT, data=actgpa)
#' }
#'
NULL
#' Airline On-Time Arrival Data
#'
#' Flights categorized by destination city, airline, and whether or not the
#' flight was on time.
#'
#'
#' @name airlineArrival
#' @rdname airlineArrival
#' @docType data
#' @format A data frame with 11000 observations on the following 3 variables.
#' \itemize{ \item{Airport}{ a factor with levels \code{LosAngeles}
#' \code{Phoenix} \code{SanDiego} \code{SanFrancisco} \code{Seattle}}
#' \item{Result}{ a factor with levels \code{Delayed} \code{OnTime}}
#' \item{Airline}{ a factor with levels \code{Alaska}
#' \code{AmericaWest}} }
#' @references These and similar data appear in many text books under the topic
#' of Simpson's paradox.
#' @source Barnett, Arnold. 1994. ``How numbers can trick you.''
#' \emph{Technology Review}, vol. 97, no. 7, pp. 38--45.
#' @keywords datasets
#' @examples
#'
#' row.perc(xtabs(~Airline+Result, data=airlineArrival))
#' for (city in levels(airlineArrival$Airport)) {
#' cat(paste('\nArriving in ', city,':\n',sep=''))
#' print(row.perc(xtabs(~Airline+Result, airlineArrival,
#' subset=Airport==city)))
#' }
#'
NULL
#' Air pollution measurements
#'
#' Air pollution measurements at three locations.
#'
#'
#' @name airpollution
#' @rdname airpollution
#' @docType data
#' @format A data frame with 6 observations on the following 2 variables.
#' \itemize{ \item{pollution}{ a numeric vector}
#' \item{location}{ a factor with levels \code{Hill Suburb} \code{Plains
#' Suburb} \code{Urban City}} }
#' @source David J. Saville and Graham R. Wood, \emph{Statistical methods: A
#' geometric primer}, Springer, 1996.
#' @keywords datasets
#' @examples
#'
#' data(airpollution)
#' summary(lm(pollution ~ location, data=airpollution))
#'
NULL
#' Ball dropping data
#'
#' Undergraduate students in a physics lab recorded the height from which a
#' ball was dropped and the time it took to reach the floor.
#'
#'
#' @name balldrop
#' @rdname balldrop
#' @docType data
#' @format A data frame with 30 observations on the following 2 variables.
#' \itemize{ \item{height}{ height in meters} \item{time}{ time
#' in seconds} }
#' @source Steve Plath, Calvin College Physics Department
#' @keywords datasets
#' @examples
#'
#' xyplot(time ~ height, data=balldrop)
#'
NULL
#' Major League Batting 2000-2005
#'
#' Major League batting data for the seasons from 2000-2005.
#'
#'
#' @name batting
#' @rdname batting
#' @docType data
#' @format A data frame with 8062 observations on the following 22 variables.
#' \itemize{ \item{player}{ unique identifier for each player}
#' \item{year}{ year} \item{stint}{ for players who were traded
#' mid-season, indicates which portion of the season the data cover}
#' \item{team}{ three-letter code for team} \item{league}{ a
#' factor with levels \code{AA} \code{AL} \code{NL}} \item{G}{ games}
#' \item{AB}{ at bats} \item{R}{ runs} \item{H}{ hits}
#' \item{H2B}{ doubles} \item{H3B}{ triples}
#' \item{HR}{ home runs} \item{RBI}{ runs batted in}
#' \item{SB}{ stolen bases} \item{CS}{ caught stealing}
#' \item{BB}{ bases on balls (walks)} \item{SO}{ strike outs}
#' \item{IBB}{ intentional base on balls} \item{HBP}{ hit by
#' pitch} \item{SH}{ a numeric vector} \item{SF}{ sacrifice fly}
#' \item{GIDP}{ grounded into double play} }
#' @keywords datasets
#' @examples
#'
#' data(batting)
#' histogram(~HR,batting)
#'
NULL
#' Births by Day
#'
#' The number of live births in the United States for each day of the year
#' 1978.
#'
#'
#' @name births78
#' @rdname births78
#' @docType data
#' @format A data frame with 365 observations on the following 3 variables.
#' \itemize{ \item{date}{ date (as a factor)}
#' \item{births}{ number of live births} \item{dayofyear}{ number
#' of days since start of year} }
#' @keywords datasets
#' @examples
#'
#' data(births78)
#' xyplot(births ~ dayofyear, births78)
#'
NULL
#' Buckthorn
#'
#' Data from an experiment to determine the efficacy of various methods of
#' eradicating buckthorn, an invasive woody shrub. Buckthorn plants were
#' chopped down and the stumps treated with various concentrations of
#' glyphosate. The next season, researchers returned to see whether the plant
#' had regrown.
#'
#'
#' @name buckthorn
#' @rdname buckthorn
#' @docType data
#' @format A data frame with 165 observations on the following 3 variables.
#' \itemize{ \item{shoots}{ number of new shoots coming from stump}
#' \item{conc}{ concentration of glyphosate applied}
#' \item{dead}{ weather the stump was considered dead} }
#' @source David Dornbos, Calvin College
#' @keywords datasets
#' @examples
#'
#' data(buckthorn)
#'
NULL
#' Bugs
#'
#' This data frame contains data from an experiment to see if insects are more
#' attracted to some colors than to others. The researchers prepared colored
#' cards with a sticky substance so that insects that landed on them could not
#' escape. The cards were placed in a field of oats in July. Later the
#' researchers returned, collected the cards, and counted the number of cereal
#' leaf beetles trapped on each card.
#'
#'
#' @name bugs
#' @rdname bugs
#' @docType data
#' @format A data frame with 24 observations on the following 2 variables.
#' \itemize{ \item{Color}{ color of card; one of \code{B}(lue)
#' \code{G}(reen) \code{W}(hite) \code{Y}(ellow)} \item{NumTrap}{ number
#' of insects trapped on the card} }
#' @source M. C. Wilson and R. E. Shade, Relative attractiveness of various
#' luminescent colors to the cereal leaf beetle and the meadow spittlebug,
#' \emph{Journal of Economic Entomology} 60 (1967), 578--580.
#'
#' @keywords datasets
#'
#' @examples
#'
#' data(bugs)
#' summary(NumTrap ~ Color, bugs, fun=favstats)
#'
NULL
#' Concrete Compressive Strength Data
#'
#' These data were collected by I-Cheng Yeh to determine how the compressive
#' strength of concrete is related to its ingredients (cement, blast furnace
#' slag, fly ash, water, superplasticizer, coarse aggregate, and fine
#' aggregate) and age.
#'
#'
#' @name concrete
#' @rdname concrete
#' @aliases concreteAll concrete28
#' @docType data
#' @format \code{concreteAll} is a data frame with the following 9 variables.
#' \itemize{ \item{cement}{ amount of cement (kg/m^3)}
#' \item{slag}{ amount of blast furnace slag (kg/m^3)}
#' \item{ash}{ amount of fly ash(kg/m^3)} \item{water}{ amount of
#' water (kg/m^3)} \item{superP}{ amount of superplasticizer (kg/m^3)}
#' \item{coarseAg}{ amount of coarse aggregate (kg/m^3)}
#' \item{fineAg}{ amount of fine aggregate (kg/m^3)}
#' \item{age}{ age of concrete in days }
#' \item{strength}{ compressive strength measured in MPa} }
#' \code{concrete28} is a subset of \code{concreteAll}.
#' @references I-Cheng Yeh (1998), "Modeling of strength of high performance
#' concrete using artificial neural networks," \cite{Cement and Concrete
#' Research}, Vol. 28, No. 12, pp. 1797-1808.
#' @source Data were obtained from the Machine Learning Repository
#' (\url{http://archive.ics.uci.edu/ml/}) where they were deposited by I-Cheng
#' Yeh (\email{icyeh at chu.edu.tw}) who retains the copyright for these data.
#' @keywords datasets
#' @examples
#'
#' data(concreteAll)
#' data(concrete28)
#'
NULL
#' Corn Yield
#'
#' William Gosset analyzed data from an experiment comparing the yield of
#' regular and kiln-dried corn.
#'
#' Gosset (Student) reported on the results of seeding plots with two different
#' kinds of seed. Each type of seed (regular and kiln-dried) was planted in
#' adjacent plots, accounting for 11 pairs of "split" plots.
#'
#' @name corn
#' @rdname corn
#' @docType data
#' @format A data frame with 11 observations on the following 2 variables.
#' \itemize{ \item{reg}{ yield of regular corn (lbs/acre)}
#' \item{kiln}{ yield of kiln-dried corn (lbs/acre)} }
#' @references W.S. Gosset, "The Probable Error of a Mean," Biometrika, 6
#' (1908), pp 1-25.
#' @source These data are also available at DASL, the data and story library
#' (\url{http://lib.stat.cmu.edu/DASL/}).
#' @keywords datasets
#' @examples
#'
#' corn2 <- stack(corn)
#' names(corn2) <- c('yield','treatment')
#' lm(yield ~ treatment, data = corn2)
#' t.test(yield ~ treatment, data=corn2)
#' t.test(corn$reg, corn$kiln)
#'
NULL
#' Cuckoo eggs in other birds' nests
#'
#' Cuckoos are knows to lay their eggs in the nests of other (host) birds. The
#' eggs are then adopted and hatched by the host birds. These data were
#' originally collected by O. M. Latter in 1902 to see how the size of a cuckoo
#' egg is related to the species of the host bird.
#'
#'
#' @name cuckoo
#' @rdname cuckoo
#' @docType data
#' @format A data frame with 120 observations on the following 2 variables.
#' \itemize{ \item{length}{ length of egg (mm)}
#' \item{species}{ a factor with levels \code{hedge sparrow}
#' \code{meadow pipet} \code{pied wagtail} \code{robin} \code{tree pipet}
#' \code{wren}} }
#' @references These data are also available from DASL, the data and story
#' library
#' (\url{http://lib.stat.cmu.edu/DASL/}).
#' @source L.H.C. Tippett, \emph{The Methods of Statistics}, 4th Edition, John
#' Wiley and Sons, Inc., 1952, p. 176.
#' @keywords datasets
#' @examples
#'
#' data(cuckoo)
#' bwplot(length~species,cuckoo)
#'
NULL
#' Death Penalty and Race
#'
#' A famous example of Simpson's paradox.
#'
#'
#' @name deathPenalty
#' @rdname deathPenalty
#' @aliases deathPenalty deathPen
#' @docType data
#' @format A data frame with 326 observations. The factors are coded more
#' succinctly in \code{deathPen}, but otherwise the data are the same.
#' \itemize{ \item{DeathPenalty}{ a factor with levels \code{Yes}
#' \code{No}} \item{Penalty}{ a factor with levels \code{Death}
#' \code{Not}} \item{Victim}{ a factor with levels \code{Black}
#' \code{White} (or \code{Bl} \code{Wh})} \item{Defendant}{ a factor
#' with levels \code{Black} \code{White} (or \code{Bl} \code{Wh})} }
#' @source Radelet, M. (1981). Racial characteristics and imposition of the
#' death penalty. \emph{American Sociological Review}, 46:918--927.
#' @keywords datasets
#' @examples
#'
#' xtabs(~Defendant+Penalty,deathPenalty)
#' xtabs(~Defendant+Victim+Penalty,deathPenalty)
#'
NULL
#' Drag force experiment
#'
#' The data come from an experiment to determine how terminal velocity depends
#' on the mass of the falling object. A helium balloon was rigged with a small
#' basket and just the ballast to make it neutrally buoyant. Mass was then
#' added and the terminal velocity is calculated by measuring the time it took
#' to fall between two sensors once terminal velocity has been reached. Larger
#' masses were drop from higher heights and used sensors more widely spaced.
#'
#'
#' @name drag
#' @rdname drag
#' @docType data
#' @format A data frame with 42 observations on the following 5 variables.
#' \itemize{ \item{time}{ time (in seconds) to travel between two
#' sensors} \item{mass}{ net mass (in kg) of falling object}
#' \item{height}{ distance (in meters) between two sensors}
#' \item{velocity}{ average velocity (in m/s) computed from \code{time}
#' and \code{height}} \item{force.drag}{ calculated drag force (in N,
#' \code{force.drag = mass * 9.8}) using the fact that at terminal velocity,
#' the drag force is equal to the force of gravity} }
#' @source Calvin College physics students under the supervision of Professor
#' Steve Plath.
#' @keywords datasets
#' @examples
#'
#' data(drag)
#' with(drag,force.drag / mass)
#' xyplot(velocity ~ mass, drag)
#'
NULL
#' Endurance and vitamin C
#'
#' The effect of a single 600 mg dose of ascorbic acid versus a sugar placebo
#' on the muscular endurance (as measured by repetitive grip strength trials)
#' of fifteen male volunteers (19-23 years old).
#'
#' Three initial maximal contractions were performed for each subject, with the
#' greatest value indicating maximal grip strength. Muscular endurance was
#' measured by having the subjects squeeze the dynamometer, hold the
#' contraction for three seconds, and repeat continuously until a value of 50%
#' maximum grip strength was achieved for three consecutive contractions.
#' Endurance was defined as the number of repetitions required to go from
#' maximum grip strength to the initial 50% value. Subjects were given frequent
#' positive verbal encouragement in an effort to have them complete as many
#' repetitions as possible.
#'
#' The study was conducted in a double-blind manner with crossover.
#'
#' @name endurance
#' @rdname endurance
#' @docType data
#' @format A data frame with 15 observations on the following 5 variables.
#' \itemize{ \item{Vitamin}{ number of repetitions until reaching 50%
#' maximal grip after taking viatimin} \item{First}{ which treatment was
#' done first, a factor with levels \code{Placebo} \code{Vitamin}}
#' \item{Placebo}{ number of repetitions until reaching 50% maximal grip
#' strength after taking placebo} }
#' @references Keith, R. E., and Merrill, E. (1983). The effects of vitamin C
#' on maximum grip strength and muscular endurance. \emph{Journal of Sports
#' Medicine and Physical Fitness}, 23, 253-256.
#' @source These data are available from OzDASL, the Australasian data and
#' story library (\url{http://www.statsci.org/data/}).
#' @keywords datasets
#' @examples
#'
#' data(endurance)
#' t.test(endurance$Vitamin, endurance$Placebo, paired=TRUE)
#' t.test(log(endurance$Vitamin), log(endurance$Placebo), paired=TRUE)
#' t.test(1/endurance$Vitamin, 1/endurance$Placebo, paired=TRUE)
#' xqqmath( ~ Vitamin - Placebo, data = endurance)
#' xqqmath( ~ log(Vitamin) - log(Placebo), data = endurance)
#' xqqmath( ~ 1/Vitamin - 1/Placebo, data = endurance)
#'
NULL
#' Family smoking
#'
#' A cross-tabulation of whether a student smokes and how many of his or her
#' parents smoke from a study conducted in the 1960's.
#'
#'
#' @name familySmoking
#' @rdname familySmoking
#' @docType data
#' @format A data frame with 5375 observations on the following 2 variables.
#' \itemize{ \item{Student}{ a factor with levels \code{DoesNotSmoke}
#' \code{Smokes}} \item{Parents}{ a factor with levels
#' \code{NeitherSmokes} \code{OneSmokes}} \code{BothSmoke} }
#' @references The data also appear in
#'
#' Brigitte Baldi and David S. Moore, \emph{The Practice of Statistics in the
#' Life Sciences}, Freeman, 2009.
#' @source S. V. Zagona (ed.), \emph{Studies and issues in smoking behavior},
#' University of Arizona Press, 1967.
#' @keywords datasets
#' @examples
#'
#' data(familySmoking)
#' xchisq.test( xtabs(~Parents + Student, familySmoking) )
#'
NULL
#' NCAA football fumbles
#'
#' This data frame gives the number of fumbles by each NCAA FBS team for the
#' first three weeks in November, 2010.
#'
#' The fumble counts listed here are total fumbles, not fumbles lost. Some of
#' these fumbles were recovered by the team that fumbled.
#'
#' @name fumbles
#' @rdname fumbles
#' @docType data
#' @format A data frame with 120 observations on the following 7 variables.
#' \itemize{ \item{team}{ NCAA football team} \item{rank}{ rank
#' based on fumbles per game through games on November 26, 2010}
#' \item{W}{ number of wins through games on November 26, 2010}
#' \item{L}{ number of losses through games on November 26, 2010}
#' \item{week1}{ number of fumbles on November 6, 2010}
#' \item{week2}{ number of fumbles on November 13, 2010}
#' \item{week3}{ number of fumbles on November 20, 2010} }
#' @source
#' \url{http://www.teamrankings.com/college-football/stat/fumbles-per-game}
#' @keywords datasets
#' @examples
#'
#' data(fumbles)
#' m <- max(fumbles$week1)
#' table(factor(fumbles$week1,levels=0:m))
#' favstats( ~ week1, data=fumbles)
#' # compare with Poisson distribution
#' signif( cbind(
#' fumbles=0:m,
#' observedCount=table(factor(fumbles$week1,levels=0:m)),
#' modelCount= 120* dpois(0:m,mean(fumbles$week1)),
#' observedPct=table(factor(fumbles$week1,levels=0:m))/120,
#' modelPct= dpois(0:m,mean(fumbles$week1))
#' ) ,3)
#' showFumbles <- function(x,lambda=mean(x),...) {
#' mx <- max(x)
#' result <- histogram(~x, type="density", xlim=c(-.5,(mx+2.5)),
#' xlab='number of fumbles',
#' panel=function(x,y,...){
#' panel.histogram(x,alpha=0.8,breaks=seq(-0.5,(mx+2.5),by=1,...))
#' panel.points(0:(mx+2),dpois(0:(mx+2),lambda),pch=19,alpha=0.8)
#' }
#' )
#' print(result)
#' return(result)
#' }
#' showFumbles(fumbles$week1)
#' showFumbles(fumbles$week2)
#' showFumbles(fumbles$week3)
#'
NULL
#' FUSION type 2 diabetes study
#'
#' Phenotype and genotype data from the Finland United States Investigation of
#' NIDDM (type 2) Diabetes (FUSION) study.
#'
#'
#' @name pheno
#' @rdname fusion
#' @aliases pheno fusion1 fusion2
#' @docType data
#' @format Data frames with the following variables. \itemize{
#' \item{id}{ subject ID number for matching between data sets}
#' \item{t2d}{ a factor with levels \code{case} \code{control}}
#' \item{bmi}{ body mass index} \item{sex}{ a factor with levels
#' \code{F} \code{M}} \item{age}{ age of subject at time phenotypes were
#' colelcted} \item{smoker}{ a factor with levels \code{former}
#' \code{never} \code{occasional} \code{regular}} \item{chol}{ total
#' cholesterol} \item{waist}{ waist circumference (cm)}
#' \item{weight}{ weight (kg) } \item{height}{ height (cm) }
#' \item{whr}{ waist hip ratio } \item{sbp}{ systolic blood
#' pressure} \item{dbp}{ diastolic blood pressure}
#' \item{marker}{ RS name of SNP} \item{markerID}{ numeric ID for
#' SNP} \item{allele1}{ first allele coded as 1=A, 2=C, 3=G, 4=T}
#' \item{allele2}{ second allele coded as 1=A, 2=C, 3=G, 4=T}
#' \item{genotype}{ both alleles coded as a factor}
#' \item{Adose}{ number of A alleles} \item{Cdose}{ number of C
#' alleles} \item{Gdose}{ number of G alleles}
#' \item{Tdose}{ number of T alleles} }
#' @source Similar to the data presented in
#'
#' Laura J. Scott, Karen L. Mohlke, Lori L. Bonnycastle, Cristen J. Willer, Yun
#' Li, William L. Duren, Michael R. Erdos, Heather M. Stringham, Pe- ter S.
#' Chines, Anne U. Jackson, Ludmila Prokunina-Olsson, Chia-Jen J. Ding, Amy J.
#' Swift, Narisu Narisu, Tianle Hu, Randall Pruim, Rui Xiao, Xiao- Yi Y. Li,
#' Karen N. Conneely, Nancy L. Riebow, Andrew G. Sprau, Maurine Tong, Peggy P.
#' White, Kurt N. Hetrick, Michael W. Barnhart, Craig W. Bark, Janet L.
#' Goldstein, Lee Watkins, Fang Xiang, Jouko Saramies, Thomas A. Buchanan,
#' Richard M. Watanabe, Timo T. Valle, Leena Kinnunen, Goncalo R. Abecasis,
#' Elizabeth W. Pugh, Kimberly F. Doheny, Richard N. Bergman, Jaakko
#' Tuomilehto, Francis S. Collins, and Michael Boehnke, A genome-wide
#' association study of type 2 diabetes in Finns detects multiple
#' susceptibility vari- ants, \emph{Science} (2007).
#' @keywords datasets
#' @examples
#'
#' data(pheno); data(fusion1); data(fusion2)
#' fusion1m <- merge(fusion1, pheno, by="id", all.x=FALSE, all.y=FALSE)
#' xtabs(~t2d + genotype, data=fusion1m)
#' xtabs(~t2d + Gdose, data=fusion1m)
#' chisq.test( xtabs( ~t2d + genotype, data=fusion1m ) )
#' f1.glm <- glm( factor(t2d) ~ Gdose, data=fusion1m, family=binomial)
#' summary(f1.glm)
#'
NULL
#' Golf ball numbers
#'
#' Allan Rossman used to live on a golf course in a spot where dozens of balls
#' would come into his yard every week. He collected the balls and eventually
#' tallied up the numbers on the first 5000 golf balls he collected. Of these
#' 486 bore the number 1, 2, 3, or 4. The remaining 14 golf balls were omitted
#' from the data.
#'
#'
#' @name golfballs
#' @rdname golfballs
#' @docType data
#' @format The format is: num [1:4] 137 138 107 104
#' @source Data collected by Allan Rossman in Carlisle, PA.
#' @keywords datasets
#' @examples
#'
#' data(golfballs)
#' golfballs/sum(golfballs)
#' chisq.test(golfballs, p=rep(.25,4))
#'
NULL
#' GPA, ACT, and SAT scores
#'
#' GPA, ACT, and SAT scores for a sample of students.
#'
#'
#' @name gpa
#' @rdname gpa
#' @docType data
#' @format A data frame with 271 observations on the following 4 variables.
#' \itemize{ \item{satm}{ SAT mathematics score}
#' \item{satv}{ SAT verbal score} \item{act}{ ACT score}
#' \item{gpa}{ college grade point average} }
#' @keywords datasets
#' @examples
#'
#' data(gpa)
#' splom(gpa)
#'
NULL
#' Punting helium- and air-filled footballs
#'
#' Two identical footballs, one air-filled and one helium-filled, were used
#' outdoors on a windless day at The Ohio State University's athletic complex.
#' Each football was kicked 39 times and the two footballs were alternated with
#' each kick. The experimenter recorded the distance traveled by each ball.
#'
#'
#' @name heliumFootballs
#' @rdname heliumFootballs
#' @docType data
#' @format A data frame with 39 observations on the following 3 variables.
#' \itemize{ \item{Trial}{ trial number} \item{Air}{ distance
#' traveled by air-filled football (yards)} \item{Helium}{ distance
#' traveled by helium-filled football (yards)} }
#' @references Lafferty, M. B. (1993), "OSU scientists get a kick out of sports
#' controversy", \emph{The Columbus Dispatch} (November, 21, 1993), B7.
#' @source These data are available from DASL, the data and story library
# (\url{ftp://sunsite.univie.ac.at/mirrors/lib.stat.cmu.edu/DASL/.index.html}).
#' (\url{http://lib.stat.cmu.edu/DASL/}).
#' @keywords datasets
#' @examples
#'
#' data(heliumFootballs)
#' xyplot(Helium~Air, data=heliumFootballs)
#' bwplot(~(Helium-Air), data=heliumFootballs)
#'
NULL
#' Cooling muscles with ice
#'
#' This data set contains the results of an experiment comparing the efficacy
#' of different forms of dry ice application in reducing the temperature of the
#' calf muscle.
#'
#' The 12 subjects in this study came three times, at least four days apart,
#' and received one of three ice treatments (cubed ice, crushed ice, or ice
#' mixed with water). In each case, the ice was prepared in a plastic bag and
#' applied dry to the subjects calf muscle. The temperature measurements were
#' taken on the skin surface and inside the calf muscle (via a 4 cm long probe)
#' every 30 seconds for 20 minutes prior to icing, for 20 minutes during icing,
#' and for 2 hours after the ice had been removed. The temperature
#' measurements are stored in variables that begin with \code{B} (baseline),
#' \code{T} (treatment), or \code{R} (recovery) followed by a numerical code
#' for the elapsed time formed by concatenating the number of minutes and
#' seconds. For example, \code{R1230} contains the temperatures 12 minutes and
#' 30 seconds after the ice had been removed.
#'
#' Variables include \itemize{ \item{Subject}{ identification number}
#' \item{Sex}{ a factor with levels \code{female} \code{male}}
#' \item{Weight}{ weight of subject (kg)} \item{Height}{ height
#' of subject (cm)} \item{Skinfold}{ skinfold thickness}
#' \item{Calf}{ calf diameter (cm)} \item{Age}{ age of subject}
#' \item{Location}{ a factor with levels \code{intramuscular}
#' \code{surface}} \item{Treatment}{ a factor with levels \code{crushed}
#' \code{cubed} \code{wet}} \item{B0}{ baseline temperature at time 0}
#' \item{B30}{ baseline temperature 30 seconds after start}
#' \item{B100}{ baseline temperature 1 minute after start}
#' \item{B1930}{ baseline temperature 19 minutes 30 seconds start}
#' \item{T0}{ treatment temperature at beginning of treatment}
#' \item{T30}{ treatment temperature 30 seconds after start of
#' treatment} \item{T100}{ treatment temperature 1 minute after start of
#' treatment} \item{T1930}{ treatment temperature 19 minutes 30 seconds
#' after start of treatment} \item{R0}{ recovery temperature at start of
#' recovery} \item{R30}{ recovery temperature 30 seconds after start of
#' recovery} \item{R100}{ recovery temperature 1 minute after start of
#' recovery} \item{R12000}{ recovery temperature 120 minutes after start
#' of recovery} }
#'
#' @name ice
#' @rdname ice
#' @docType data
#' @source Dykstra, J. H., Hill, H. M., Miller, M. G., Michael T. J., Cheatham,
#' C. C., and Baker, R.J., Comparisons of cubed ice, crushed ice, and wetted
#' ice on intramuscular and surface temperature changes, \emph{Journal of
#' Athletic Training} 44 (2009), no. 2, 136--141.
#' @keywords datasets
#' @examples
#'
#' data(ice)
#' xyplot(Weight ~ Skinfold, groups=Sex, data=ice, auto.key=TRUE)
#'
NULL
#' Inflation data
#'
#' The article developed four measures of central bank independence and
#' explored their relation to inflation outcomes in developed and developing
#' countries. This datafile deals with two of these measures in 23 nations.
#'
#'
#' @name inflation
#' @rdname inflation
#' @docType data
#' @format A data frame with 23 observations on the following 5 variables.
#' \itemize{ \item{country}{ country where data were collected}
#' \item{ques}{ questionnaire index of independence}
#' \item{inf}{ annual inflation rate, 1980-1989 (percent)}
#' \item{legal}{ legal index of independence}
#' \item{dev}{ developed (1) or developing (2) nation} }
#' @references A. Cukierman, S.B. Webb, and B. Negapi, "Measuring the
#' Independence of Central Banks and Its Effect on Policy Outcomes," World Bank
#' Economic Review, Vol. 6 No. 3 (Sept 1992), 353-398.
#' @source These data are available from OzDASL, the Australasian Data and
#' Story Library (\url{http://www.statsci.org/data/}).
#' @keywords datasets
#' @examples
#'
#' data(inflation)
#'
NULL
#' Goals and popularity factors for school kids
#'
#' Subjects were students in grades 4-6 from three school districts in
#' Michigan. Students were selected from urban, suburban, and rural school
#' districts with approximately 1/3 of their sample coming from each district.
#' Students indicated whether good grades, athletic ability, or popularity was
#' most important to them. They also ranked four factors: grades, sports,
#' looks, and money, in order of their importance for popularity. The
#' questionnaire also asked for gender, grade level, and other demographic
#' information.
#'
#'
#' @name kids
#' @rdname kids
#' @docType data
#' @format A data frame with 478 observations on the following 11 variables.
#' \itemize{ \item{Gender}{ a factor with levels \code{boy}
#' \code{girl}} \item{Grade}{ grade in school}
#' \item{Age}{ student age} \item{Race}{ a factor with levels
#' \code{Other} \code{White}} \item{Urban.Rural}{ a factor with levels
#' \code{Rural} \code{Suburban} \code{Urban}} \item{School}{ a factor
#' with levels \code{Brentwood Elementary} \code{Brentwood Middle} \code{Brown
#' Middle} \code{Elm} \code{Main} \code{Portage} \code{Ridge} \code{Sand}
#' \code{Westdale Middle}} \item{Goals}{ a factor with levels
#' \code{Grades} \code{Popular} \code{Sports}} \item{Grades}{ rank of
#' `make good grades' (1=most important for popularity; 4=least important)}
#' \item{Sports}{ rank of `beging good at sports' (1=most important for
#' popularity; 4=least important)} \item{Looks}{ rank of `beging
#' handsome or pretty' (1=most important for popularity; 4=least important)}
#' \item{Money}{ rank of `having lots of money' (1=most important for
#' popularity; 4=least important)} }
#' @references Chase, M. A., and Dummer, G. M. (1992), "The Role of Sports as a
#' Social Determinant for Children," Research Quarterly for Exercise and Sport,
#' 63, 418-424.
#' @source These data are available at DASL, the data and story library
# (\url{ftp://sunsite.univie.ac.at/mirrors/lib.stat.cmu.edu/DASL/.index.html}).
#' (\url{http://lib.stat.cmu.edu/DASL/}).
#' @keywords datasets
#' @examples
#'
#' data(kids)
#' xtabs(~Goals + Urban.Rural, data=kids)
#' chisq.test(xtabs(~Goals + Urban.Rural, data=kids))
#'
NULL
#' Results from a little survey
#'
#' These data are from a little survey given to a number of students in
#' introductory statistics courses. Several of the items were prepared in
#' multiple versions and distributed randomly to the students.
#'
#'
#' @name littleSurvey
#' @rdname littleSurvey
#' @docType data
#' @format A data frame with 279 observations on the following 20 variables.
#' \itemize{ \item{number}{ a number between 1 and 30}
#' \item{colorVer}{ which version of the 'favorite color' question was
#' on the survey. A factor with levels \code{v1} \code{v2}}
#' \item{color}{ favorite color if among predefined choices. A factor
#' with levels \code{} \code{black} \code{green} \code{other} \code{purple}
#' \code{red}} \item{otherColor}{ favorite color if not among choices
#' above.} \item{animalVer}{ which version of the 'favorite color'
#' question was on the survey. A factor with levels \code{v1} \code{v2}}
#' \item{animal}{ favorite animal if among predefined choices. A factor
#' with levels \code{} \code{elephant} \code{giraffe} \code{lion}
#' \code{other}.} \item{otherAnimal}{ favorite animal if not among the
#' predefined choices.} \item{pulseVer}{ which version of the 'pulse'
#' question was on the survey} \item{pulse}{ self-reported pulse}
#' \item{TVver}{ which of three versions of the TV question was on the
#' survey} \item{tvBox}{ a factor with levels \code{<1} \code{>4}
#' \code{>8} \code{1-2} \code{2-4} \code{4-8} \code{none} \code{other}}
#' \item{tvHours}{ a numeric vector} \item{surpriseVer}{ which of
#' two versions of the 'surprise' question was on the survey}
#' \item{surprise}{ a factor with levels \code{no} \code{yes}}
#' \item{playVer}{ which of two versions of the 'play' question was on
#' the survey} \item{play}{ a factor with levels \code{no} \code{yes}}
#' \item{diseaseVer}{ which of two versions of the 'play' question was
#' on the survey} \item{disease}{ a factor with levels \code{A}
#' \code{B}} \item{homeworkVer}{ which of two versions of the 'homework'
#' question was on the survey} \item{homework}{ a factor with levels
#' \code{A} \code{B}} }
#' @keywords datasets
#' @examples
#'
#' data(littleSurvey)
#' xtabs(~surprise+surpriseVer, data=littleSurvey)
#' xtabs(~disease+diseaseVer, data=littleSurvey)
#'
NULL
#' Test performance and noise
#'
#' In this experiment, hyperactive and control students were given a
#' mathematics test in either a quiet or loud testing environment.
#'
#'
#' @name mathnoise
#' @rdname mathnoise
#' @docType data
#' @format A data frame with 40 observations on the following 3 variables.
#' \itemize{ \item{score}{ score on a mathematics test}
#' \item{noise}{ a factor with levels \code{hi} \code{lo}}
#' \item{group}{ a factor with levels \code{control} \code{hyper}} }
#' @source Sydney S. Zentall and Jandira H. Shaw, Effects of classroom noise on
#' perfor- mance and activity of second-grade hyperactive and control children,
#' \emph{Journal of Educational Psychology} 72 (1980), no. 6, 830.
#' @keywords datasets
#' @examples
#'
#' data(mathnoise)
#' xyplot(score~noise, data=mathnoise, group=group, type='a',
#' auto.key=list(columns=2, lines=TRUE, points=FALSE))
#'
NULL
#' MIAA basketball 2004-2005 season
#'
#' Individual player statistics for the 2004-2005 Michigan Intercollegiate
#' Athletic Association basketball season.
#'
#'
#' @name miaa05
#' @rdname miaa05
#' @docType data
#' @format A data frame with 134 observations on the following 27 variables.
#' \itemize{ \item{Number}{ jersey number}
#' \item{Player}{ player's name} \item{GP}{ games played}
#' \item{GS}{ games started} \item{Min}{ minutes played}
#' \item{AvgMin}{ average minutes played per game}
#' \item{FG}{ field goals made} \item{FGA}{ field goals
#' attempted} \item{FGPct}{ field goal percentage}
#' \item{FG3}{ 3-point field goals made} \item{FG3A}{ 3-point
#' field goals attempted} \item{FG3Pct}{ 3-point field goal percentage}
#' \item{FT}{ free throws made} \item{FTA}{ free throws
#' attempted} \item{FTPct}{ free throw percentage}
#' \item{Off}{ offensive rebounds} \item{Def}{ defensive
#' rebounds} \item{Tot}{ total rebounds } \item{RBG}{ rebounds
#' per game} \item{PF}{ personal fouls} \item{FO}{ games fouled
#' out} \item{A}{ assists} \item{TO}{ turn overs}
#' \item{Blk}{ blocked shots} \item{Stl}{ steals}
#' \item{Pts}{ points scored} \item{PTSG}{ points per game} }
#' @source MIAA sports archives (\url{http://www.miaa.org/})
#' @keywords datasets
#' @examples
#'
#' data(miaa05)
#' histogram(~FTPct, data=miaa05)
#'
NULL
#' Major League Baseball 2004 team data
#'
#' Team batting statistics, runs allowed, and runs scored for the 2004 Major
#' League Baseball season.
#'
#'
#' @name mlb2004
#' @rdname mlb2004
#' @docType data
#' @format A data frame with 30 observations on the following 20 variables.
#' \itemize{ \item{Team}{ team city, a factor}
#' \item{League}{ League, a factor with levels \code{AL} \code{NL}}
#' \item{W}{ number of wins} \item{L}{ number of losses}
#' \item{G}{ number of games} \item{R}{ number of runs scored}
#' \item{OR}{ oppnents' runs -- number of runs allowed}
#' \item{Rdiff}{ run difference -- \code{R - OR}}
#' \item{AB}{ number of at bats} \item{H}{ number of hits}
#' \item{DBL}{ number of doubles} \item{TPL}{ number of triples}
#' \item{HR}{ number of home runs} \item{BB}{ number of walks
#' (bases on balls)} \item{SO}{ number of strike outs}
#' \item{SB}{ number of stolen bases} \item{CS}{ number of times
#' caught stealing} \item{BA}{ batting average}
#' \item{SLG}{ slugging percentage} \item{OBA}{ on base average}
#' }
#' @keywords datasets
#' @examples
#'
#' data(mlb2004)
#' xyplot(W ~ Rdiff, data=mlb2004)
#'
NULL
#' NCAA Division I Basketball Results
#'
#' Results of NCAA basketball games
#'
#'
#' @name ncaa2010
#' @rdname ncaa2010
#' @aliases ncaa2010 ncaa2009 ncaa2008
#' @docType data
#' @format Seven variables describing NCAA Division I basketball games.
#' \itemize{ \item{date}{ date on which game was played}
#' \item{away}{ visiting team} \item{ascore}{ visiting team's
#' score} \item{home}{ home team} \item{hscore}{ home team's
#' score} \item{notes}{ code indicting games played at neutral sites (n
#' or N) or in tournaments (T)} \item{location}{ where game was played}
#' }
#' @source \url{kenpom.com}
#' @keywords datasets
#' @examples
#'
#' data(ncaa2010)
#' # add some additional variables to the data frame
#' ncaa2010$dscore <- ncaa2010$hscore- ncaa2010$ascore
#' ncaa2010$homeTeamWon <- ncaa2010$dscore > 0
#' ncaa2010$numHomeTeamWon <- -1 + 2 * as.numeric(ncaa2010$homeTeamWon)
#' w <- which(ncaa2010$homeTeamWon)
#' ncaa2010$winner <- as.character(ncaa2010$away)
#' ncaa2010$winner[w] <- as.character(ncaa2010$home)[w]
#' ncaa2010$loser <- as.character(ncaa2010$home)
#' ncaa2010$loser[w] <- as.character(ncaa2010$away)[w]
#' ncaa2010$homeTeamWon <- ncaa2010$winner == ncaa2010$home
#' ncaa2010$numHomeTeamWon <- -1 + 2 * as.numeric(ncaa2010$homeTeamWon)
#'
NULL
#' NFL 2007 season
#'
#' Results of National Football League games (2007 season, including playoffs)
#'
#'
#' @name nfl2007
#' @rdname nfl2007
#' @docType data
#' @format A data frame with 267 observations on the following 7 variables.
#' \itemize{ \item{Date}{ date on which game was played}
#' \item{Visitor}{ visiting team} \item{VisitorScore}{ score for
#' visiting team} \item{Home}{ home team} \item{HomeScore}{ score
#' for home team} \item{Line}{ `betting line'}
#' \item{TotalLine}{ 'over/under' line (for combined score of both
#' teams)} }
#' @keywords datasets
#' @examples
#'
#' data(nfl2007); nfl <- nfl2007
#' nfl$dscore <- nfl$HomeScore - nfl$VisitorScore
#' w <- which(nfl$dscore > 0)
#' nfl$winner <- nfl$Visitor; nfl$winner[w] <- nfl$Home[w]
#' nfl$loser <- nfl$Home; nfl$loser[w] <- nfl$Visitor[w]
#' # did the home team win?
#' nfl$homeTeamWon <- nfl$dscore > 0
#' table(nfl$homeTeamWon)
#' table(nfl$dscore > nfl$line)
#'
NULL
#' Noise -- unfinished documentation
#'
#' In order to test the effect of room noise, subjects were given a test under
#' 5 diff sets of conditions: 1) no noise, 2) intermittent low volume, 3)
#' intermittent high volume, 4) continuous low volume, and 5) continuous high
#' volume.
#'
#'
#' @name noise
#' @rdname noise
#' @docType data
#' @format A data frame with 50 observations on the following 5 variables.
#' \itemize{ \item{id}{ subject identifier} \item{score}{ score
#' on the test} \item{condition}{ numeric code for condition}
#' \item{volume}{ a factor with levels \code{high} \code{low}
#' \code{none}} \item{frequency}{ a factor with levels \code{continuous}
#' \code{intermittent} \code{none}} }
#' @keywords datasets
#' @examples
#'
#' data(noise)
#' noise2 <- noise[noise$volume != 'none',]
#' model <- lm(score~volume*frequency, data=noise2)
#' anova(model)
#' xyplot(score~volume,noise2, groups=frequency, type='a',
#' auto.key=list(columns=2, points=FALSE, lines=TRUE))
#'
NULL
#' Palette repair data
#'
#' The palettes data set contains data from a firm that recycles palettes.
#' Palettes from warehouses are bought, repaired, and resold. (Repairing a
#' palette typically involves replacing one or two boards.) The company has
#' four employees who do the repairs. The employer sampled five days for each
#' employee and recorded the number of palettes repaired.
#'
#'
#' @name palettes
#' @rdname palettes
#' @docType data
#' @format A data frame with 20 observations on the following 3 variables.
#' \itemize{ \item{palettes}{ number of palettes repaired}
#' \item{employee}{ a factor with levels \code{A} \code{B} \code{C}
#' \code{D}} \item{day}{ a factor with levels \code{day1} \code{day2}
#' \code{day3} \code{day4} \code{day5}} }
#' @source Michael Stob, Calvin College
#' @keywords datasets
#' @examples
#'
#' data(palettes)
#' # Do the employees differ in the rate at which they repair palettes?
#' pal.lm1 <- lm(palettes~employee,palettes)
#' anova(pal.lm1)
#' # Now using day as a blocking variable
#' pal.lm2 <- lm(palettes~employee+day,palettes)
#' anova(pal.lm2)
#' xyplot(palettes~day, data=palettes,
#' groups=employee,
#' main="Productivity by day and employee",
#' type='b',auto.key=list(columns=4,points=FALSE,lines=TRUE))
#'
NULL
#' Paper airplanes
#'
#' Student-collected data from an experiment investigating the design of paper
#' airplanes.
#'
#' These data were collected by Stewart Fischer and David Tippetts, statistics
#' students at the Queensland University of Technology in a subject taught by
#' Dr. Margaret Mackisack. Here is their description of the data and its
#' collection:
#'
#' The experiment decided upon was to see if by using two different designs of
#' paper aeroplane, how far the plane would travel. In considering this, the
#' question arose, whether different types of paper and different angles of
#' release would have any effect on the distance travelled. Knowing that paper
#' aeroplanes are greatly influenced by wind, we had to find a way to eliminate
#' this factor. We decided to perform the experiment in a hallway of the
#' University, where the effects of wind can be controlled to some extent by
#' closing doors.
#'
#' In order to make the experimental units as homogeneous as possible we
#' allocated one person to a task, so person 1 folded and threw all planes,
#' person 2 calculated the random order assignment, measured all the distances,
#' checked that the angles of flight were right, and checked that the plane
#' release was the same each time.
#'
#' The factors that we considered each had two levels as follows:
#'
#' Paper: A4 size, 80g and 50g
#'
#' Design: High Performance Dual Glider, and Incredibly Simple Glider (patterns
#' attached to original report)
#'
#' Angle of release: Horizontal, or 45 degrees upward.
#'
#' The random order assignment was calculated using the random number function
#' of a calculator. Each combination of factors was assigned a number from one
#' to eight, the random numbers were generated and accordingly the order of the
#' experiment was found.
#'
#' @name paperplanes
#' @rdname paperplanes
#' @docType data
#' @format A data frame with 16 observations on the following 5 variables.
#' \itemize{ \item{distance}{ distance plane traveled (cm)}
#' \item{paper}{ type of paper used} \item{angle}{ a numeric
#' vector} \item{design}{ design of plane (\code{hi performance} or
#' \code{simple})} \item{order}{ order in which planes were thrown} }
#' @references Mackisack, M. S. (1994). What is the use of experiments
#' conducted by statistics students? \emph{Journal of Statistics Education}, 2,
#' no 1.
#' @source These data are also available at OzDASL, the Australasian Data and
#' Story Library (\url{http://www.statsci.org/data/}).
#' @keywords datasets
#' @examples
#'
#' data(paperplanes)
#'
NULL
#' Pendulum data
#'
#' Period and pendulum length for a number of string and mass pendulums
#' constructed by physics students. The same mass was used throughout, but the
#' length of the string was varied from 10cm to 16 m.
#'
#'
#' @name pendulum
#' @rdname pendulum
#' @docType data
#' @format A data frame with 27 observations on the following 3 variables.
#' \itemize{ \item{length}{ length of the pendulum (in meters)}
#' \item{period}{ average time of period (in seconds) over several
#' swings of the pendulum} \item{delta.length}{ an estimate of the
#' accuracy of the length measurement} }
#' @source Calvin College physics students under the direction of Professor
#' Steve Plath.
#' @keywords datasets
#' @examples
#'
#' data(pendulum)
#' xyplot(period ~ length, data=pendulum)
#'
NULL
#' Pets and stress
#'
#' Does having a pet or a friend cause more stress?
#'
#' Fourty-five women, all self-proclaimed dog-lovers, were randomly divided
#' into three groups of subjects. Each performed a stressful task either alone,
#' with a friend present, or with their dog present. The average heart rate
#' during the task was used as a measure of stress.
#'
#' @name petstress
#' @rdname petstress
#' @docType data
#' @format A data frame with 45 observations on the following 2 variables.
#' \itemize{ \item{Group}{ a factor with levels \code{C}ontrol,
#' \code{F}riend, or \code{P}et} \item{Rate}{ average heart rate while
#' performing a stressful task} }
#' @references These data also appear in
#'
#' Brigitte Baldi and David S. Moore, \emph{The Practice of Statistics in the
#' Life Sciences}, Freeman, 2009.
#' @source K. M. Allen, J. Blascovich, J. Tomaka, and R. M. Kelsey, Presence of
#' human friends and pet dogs as moderators of autonomic responses to stress in
#' women, \emph{Journal of Personality and Social Psychology} 61 (1991), no. 4,
#' 582--589.
#' @keywords datasets
#' @examples
#'
#' data(petstress)
#'
NULL
#' Pass the Pigs
#'
#' This data set contains information collected from rolling the pair of pigs
#' (found in the game "Pass the Pigs") 6000 times.
#'
#'
#' In "Pass the Pigs", players roll two pig-shaped rubber dice and earn or lose
#' points depending on the configuration of the rolled pigs. Players compete
#' individually to earn 100 points. On each turn, a player rolls he or she
#' decides to stop or until "pigging out" or
#'
#' The pig configurations and their associated scores are
#'
#' 1 = Dot Up (0)
#'
#' 2 = Dot Down (0)
#'
#' 3 = Trotter (5)
#'
#' 4 = Razorback (5)
#'
#' 5 = Snouter (10)
#'
#' 6 = Leaning Jowler (15)
#'
#' 7 = Pigs are touching one another (-1; lose all points)
#'
#' One pig Dot Up and one Dot Down ends the turn (a "pig out") and results in 0
#' points for the turn. If the pigs touch, the turn is ended and all points
#' for the game must be forfeited. Two pigs in the Dot Up or Dot Down
#' configuration score 1 point. Otherwise, The scores of the two pigs in
#' different configurations are added together. The score is doubled if both
#' both pigs have the same configuration, so, for example, two Snouters are
#' worth 40 rather than 20.
#'
#' The vector \code{pigConfig} is provided to assist in converting from the
#' numeric codes above to pig configurations.
#'
#' @name pigs
#' @rdname pigs
#' @aliases pigs pigConfig
#' @docType data
#' @format A data frame with 6000 observations on the following 6 variables.
#' \itemize{ \item{roll}{ roll number (1-6000)}
#' \item{black}{ numerical code for position of black pig}
#' \item{blackF}{ position of black pig coded as a factor}
#' \item{pink}{ numerical code for position of pink pig}
#' \item{pinkF}{ position of pink pig coded as a factor}
#' \item{score}{ score of the roll} \item{height}{ height from
#' which pigs were rolled (5 or 8 inches)} \item{start}{ starting
#' position of the pigs (0 = both pigs backwards, 1 = one bacwards one
#' forwards, 2 = both forwards)} }
#' @source John C. Kern II, Duquesne University (\email{kern at mathcs.duq.edu})
#' @keywords datasets
#' @examples
#'
#' data(pigs)
#' table(pigConfig[pigs$black])
#'
NULL
#' Major League Baseball 2005 pitching
#'
#' Major League Baseball pitching statistics for the 2005 season.
#'
#'
#' @name pitching2005
#' @rdname pitching2005
#' @docType data
#' @format A data frame with 653 observations on the following 27 variables.
#' \itemize{ \item{playerID}{ unique identifier for each player}
#' \item{yearID}{ year} \item{stint}{ for players who played with
#' multiple teams in the same season, \code{stint} is increased by one each
#' time the player joins a new team} \item{teamID}{ three-letter
#' identifier for team} \item{lgID}{ league team plays in, coded as
#' \code{AL} or \code{NL}} \item{W}{ wins} \item{L}{ losses}
#' \item{G}{ games played in} \item{GS}{ games started}
#' \item{CG}{ complete games} \item{SHO}{ shut outs}
#' \item{SV}{ saves recorded} \item{IPouts}{ outs recorded
#' (innings pitched, measured in outs rather than innings)}
#' \item{H}{ hits allowed} \item{ER}{ earned runs allowed}
#' \item{HR}{ home runs allowed} \item{BB}{ walks (bases on
#' balls) allowed} \item{SO}{ strike outs} \item{BAOpp}{ opposing
#' hitters' batting average} \item{ERA}{ earned run average}
#' \item{IBB}{ intentional walks} \item{WP}{ wild pitches}
#' \item{HBP}{ number of batters hit by pitch} \item{BK}{ balks}
#' \item{BFP}{ batters faced pitching} \item{GF}{ ratio of ground
#' balls to fly balls} \item{R}{ runs allowed} }
#' @keywords datasets
#' @examples
#'
#' data(pitching2005)
#' xyplot(IPouts/3 ~ W, data=pitching2005, ylab="innings pitched", xlab="wins")
#'
NULL
#' Poison data
#'
#' The data give the survival times (in hours) in a 3 x 4 factorial experiment,
#' the factors being (a) three poisons and (b) four treatments. Each
#' combination of the two factors is used for four animals. The allocation to
#' animals is completely randomized.
#'
#'
#' @name poison
#' @rdname poison
#' @docType data
#' @format A data frame with 48 observations on the following 3 variables.
#' \itemize{ \item{Poison}{ type of poison (1, 2, or 3)}
#' \item{Treatment}{ manner of treatment (1, 2, 3, or 4)}
#' \item{Time}{ time until death (hours)} }
#' @references Box, G. E. P., and Cox, D. R. (1964). An analysis of
#' transformations (with Discussion). J. R. Statist. Soc. B, 26, 211-252.
#'
#' Aitkin, M. (1987). Modelling variance heterogeneity in normal regression
#' using GLIM. Appl. Statist., 36, 332-339.
#'
#' Smyth, G. K., and Verbyla, A. P. (1999). Adjusted likelihood methods for
#' modelling dispersion in generalized linear models. Environmetrics 10,
#' 696-709. \url{http://www.statsci.org/smyth/pubs/ties98tr.html}.
#' @source These data are also available from OzDASL, the Australian Data and
#' Story Library (\url{http://www.statsci.org/data/}). (Note: The time measurements
#' of the data at OzDASL are in units of tens of hours.)
#' @keywords datasets
#' @examples
#'
#' data(poison)
#' poison.lm <- lm(Time~factor(Poison) * factor(Treatment), data=poison)
#' xplot(poison.lm,w=c(4,2))
#' anova(poison.lm)
#' # improved fit using a transformation
#' poison.lm2 <- lm(1/Time~factor(Poison) * factor(Treatment), data=poison)
#' xplot(poison.lm2,w=c(4,2))
#' anova(poison.lm)
#'
NULL
#' American football punting
#'
#' Investigators studied physical characteristics and ability in 13 football
#' punters. Each volunteer punted a football ten times. The investigators
#' recorded the average distance for the ten punts, in feet. They also recorded
#' the average hang time (time the ball is in the air before the receiver
#' catches it), and a number of measures of leg strength and flexibility.
#'
#'
#' @name punting
#' @rdname punting
#' @docType data
#' @format A data frame with 13 observations on the following 7 variables.
#' \itemize{ \item{distance}{ mean distance for 10 punts (feet) }
#' \item{hang}{ mean hang time (seconds) }
#' \item{rStrength}{ right leg strength (pounds)}
#' \item{lStrength}{ left leg strength (pounds)}
#' \item{rFlexibility}{ right leg flexibility (degrees)}
#' \item{lFlexibility}{ left leg flexibility (degrees)}
#' \item{oStrength}{ overall leg strength (foot-pounds)} }
#' @references "The relationship between selected physical performance
#' variables and football punting ability" by the Department of Health,
#' Physical Education and Recreation at the Virginia Polytechnic Institute and
#' State University, 1983.
#' @source These data are also available at OzDASL
#' (\url{http://www.statsci.org/data/}).
#' @keywords datasets
#' @examples
#'
#' data(punting)
#' xyplot(hang ~ distance, data=punting)
#'
NULL
#' Rat poison -- unfinished documentation
#'
#' Data from an experiment to see whether flavor and location of rat poison
#' influence the consumption by rats.
#'
#'
#' @name ratpoison
#' @rdname ratpoison
#' @docType data
#' @format A data frame with 20 observations on the following 3 variables.
#' \itemize{ \item{consumption}{ a numeric vector}
#' \item{flavor}{ a factor with levels \code{bread}
#' \code{butter-vanilla} \code{plain} \code{roast beef}}
#' \item{location}{ a factor with levels \code{A} \code{B} \code{C}
#' \code{D} \code{E}} }
#' @keywords datasets
#' @examples
#'
#' data(ratpoison)
#'
NULL
#' Simulated golf ball data
#'
#' A matrix of random golf ball numbers simulated using
#' \code{rmultinom(n=10000,size=486,prob=rep(0.25,4))}.
#'
#'
#' @name rgolfballs
#' @rdname rgolfballs
#' @docType data
#' @keywords datasets
#' @examples
#'
#' data(rgolfballs)
#'
NULL
#' Rubber band launching -- unfinished documentation
#'
#' Results of an experiment comparing a rubber band travels to the amount it
#' was stretched prior to launch.
#'
#'
#' @name rubberband
#' @rdname rubberband
#' @docType data
#' @format A data frame with 16 observations on the following 2 variables.
#' \itemize{ \item{Stretch}{ amount rubber band was stretched before
#' launch} \item{Distance}{ distance rubber band traveled } }
#' @keywords datasets
#' @examples
#'
#' data(rubberband)
#' xyplot(Distance ~ Stretch, data=rubberband, type=c("p","r"))
#'
NULL
#' Maze tracing and scents
#'
#' Subjects were asked to to complete a pencil and paper maze when they were
#' smelling a floral scent and when they were not.
#'
#'
#' @name scent
#' @rdname scent
#' @docType data
#' @format A data frame with 21 observations on the following 12 variables.
#' \itemize{ \item{id}{ ID number} \item{sex}{ a factor with
#' levels \code{F} and\code{M}} \item{smoker}{ a factor with levels
#' \code{N}, \code{Y}} \item{opinion}{ opinion of the odor
#' (\code{indiff}, \code{neg}, or \code{pos}}) \item{age}{ age of
#' subject (in years)} \item{first}{ which treatment was first,
#' \code{scented} or \code{unscented}} \item{u1}{ time (in seconds) in
#' first unscented trial} \item{u2}{ time (in seconds) in second
#' unscented trial} \item{u3}{ time (in seconds) in third unscented
#' trial} \item{s1}{ time (in seconds) in first scented trial}
#' \item{s2}{ time (in seconds) in second scented trial}
#' \item{s3}{ time (in seconds) in third scented trial} }
#' @references Hirsch, A. R., and Johnston, L. H. "Odors and Learning," Smell
#' \& Taste Treatment and Research Foundation, Chicago.
#' @source These data are also available at DASL, the data and story library
# (\url{ftp://sunsite.univie.ac.at/mirrors/lib.stat.cmu.edu/DASL/.index.html}).
#' (\url{http://lib.stat.cmu.edu/DASL/}).
#' @keywords datasets
#' @examples
#'
#' data(scent)
#' summary(scent)
#'
NULL
#' Dwindling soap
#'
#' A bar of soap was weighed after showering to see how much soap was used each
#' shower.
#'
#' According to Rex Boggs:
#'
#' I had a hypothesis that the daily weight of my bar of soap [in grams] in my
#' shower wasn't a linear function, the reason being that the tiny little bar
#' of soap at the end of its life seemed to hang around for just about ever. I
#' wanted to throw it out, but I felt I shouldn't do so until it became
#' unusable. And that seemed to take weeks.
#'
#' Also I had recently bought some digital kitchen scales and felt I needed to
#' use them to justify the cost. I hypothesized that the daily weight of a bar
#' of soap might be dependent upon surface area, and hence would be a quadratic
#' function \dots{} .
#'
#' The data ends at day 22. On day 23 the soap broke into two pieces and one
#' piece went down the plughole.
#'
#' @name soap
#' @rdname soap
#' @docType data
#' @format A data frame with 15 observations on the following 3 variables.
#' \itemize{ \item{Date}{ } \item{Day}{ days since start of soap
#' usage and data collection} \item{Weight}{ weight of bar of soap (in
#' grams) } }
#' @source Data collected by Rex Boggs and available from OzDASL
#' (\url{http://www.statsci.org/data/}).
#' @keywords datasets
#' @examples
#'
#' data(soap)
#' xyplot(Weight~Day, data=soap)
#'
NULL
#' Measuring spheres
#'
#' Measurements of the diameter (in meters) and mass (in kilograms) of a set of
#' steel ball bearings.
#'
#'
#' @name spheres
#' @rdname spheres
#' @docType data
#' @format A data frame with 12 observations on the following 2 variables.
#' \itemize{ \item{diameter}{ diameter of bearing (m)}
#' \item{mass}{ mass of the bearing (kg) } }
#' @references data(spheres)
#' @source These data were collected by Calvin College physics students under
#' the direction of Steve Plath.
#' @keywords datasets
NULL
#' Stepping experiment
#'
#' An experiment was conducted by students at The Ohio State University in the
#' fall of 1993 to explore the nature of the relationship between a person's
#' heart rate and the frequency at which that person stepped up and down on
#' steps of various heights.
#'
#' An experiment was conducted by students at The Ohio State University in the
#' fall of 1993 to explore the nature of the relationship between a person's
#' heart rate and the frequency at which that person stepped up and down on
#' steps of various heights. The response variable, heart rate, was measured in
#' beats per minute. There were two different step heights: 5.75 inches (coded
#' as \code{lo}), and 11.5 inches (coded as \code{hi}). There were three rates
#' of stepping: 14 steps/min. (coded as \code{slow}), 21 steps/min. (coded as
#' \code{medium}), and 28 steps/min. (coded as \code{fast}). This resulted in
#' six possible height/frequency combinations. Each subject performed the
#' activity for three minutes. Subjects were kept on pace by the beat of an
#' electric metronome. One experimenter counted the subject's pulse for 20
#' seconds before and after each trial. The subject always rested between
#' trials until her or his heart rate returned to close to the beginning rate.
#' Another experimenter kept track of the time spent stepping. Each subject was
#' always measured and timed by the same pair of experimenters to reduce
#' variability in the experiment. Each pair of experimenters was treated as a
#' block.
#'
#' @name step
#' @rdname step
#' @docType data
#' @format A data frame with 30 observations on the following 7 variables.
#' \itemize{ \item{order}{ performance order}
#' \item{block}{ number of experimenter block}
#' \item{restHR}{ resting heart rate (beats per minute)}
#' \item{HR}{ final heart rate} \item{height}{ height of step
#' (\code{hi} or \code{lo})} \item{freq}{ whether subject stepped
#' \code{fast}, \code{medium}, or \code{slow}} }
#' @source These data are available at DASL, the data and story library
# (\url{ftp://sunsite.univie.ac.at/mirrors/lib.stat.cmu.edu/DASL/.index.html}).
#' (\url{http://lib.stat.cmu.edu/DASL/}).
#' @keywords datasets
#' @examples
#'
#' data(step)
#' xyplot(HR-restHR ~ freq, data=step, groups=height, type='a')
#' xyplot(HR-restHR ~ height, data=step, groups=freq, type='a')
#'
NULL
#' Stereogram fusion
#'
#' Results of an experiment on the effect of prior information on the time to
#' fuse random dot steregrams. One group (NV) was given either no information
#' or just verbal information about the shape of the embedded object. A second
#' group (group VV) received both verbal information and visual information
#' (e.g., a drawing of the object).
#'
#'
#' @name stereogram
#' @rdname stereogram
#' @docType data
#' @format A data frame with 78 observations on the following 2 variables.
#' \itemize{ \item{Time}{ time until subject was able to fuse a random
#' dot stereogram} \item{Group}{ treatment group: \code{NV}(no visual
#' instructions) \code{VV} (visual instructions)} }
#' @references Frisby, J. P. and Clatworthy, J. L., "Learning to see complex
#' random-dot stereograms," \emph{Perception}, 4, (1975), pp. 173-178.
#'
#' Cleveland, W. S. \emph{Visualizing Data}. 1993.
#' @source These data are available at DASL, the data and story library
# (\url{ftp://sunsite.univie.ac.at/mirrors/lib.stat.cmu.edu/DASL/.index.html}.
#' (\url{http://lib.stat.cmu.edu/DASL/}).
#' @keywords datasets
#' @examples
#'
#' data(stereogram)
#' require(Hmisc)
#' favstats(Time~Group, data=stereogram)
#'
NULL
#' Standardized test scores and GPAs
#'
#' Standardized test scores and GPAs for 1000 students.
#'
#'
#' @name students
#' @rdname students
#' @docType data
#' @format A data frame with 1000 observations on the following 6 variables.
#' \itemize{ \item{ACT}{ ACT score} \item{SAT}{ SAT score}
#' \item{Grad}{ has the student graduated from college?}
#' \item{GradGPA}{ college GPA at graduation} \item{HSGPA}{ high
#' school GPA} \item{Cohort}{ year of graduation or expected graduation}
#' }
#' @keywords datasets
#' @examples
#'
#' data(students)
#' xyplot(ACT ~ SAT, data=students)
#' xyplot(GradGPA ~ HSGPA, data=students)
#'
NULL
#' Taste test data
#'
#' Tthe results from a study comparing different preparation methods for taste
#' test samples.
#'
#' The samples were prepared for tasting using either a coarse screen or a fine
#' screen, and with either a high or low liquid content. A total taste score is
#' recorded for each of 16 groups of 50 testers each. Each group had 25 men and
#' 25 women, each of whom scored the samples on a scale from -3 (terrible) to 3
#' (excellent). The sum of these individual scores is the overall taste score
#' for the group.
#'
#' @name tastetest
#' @rdname tastetest
#' @aliases tastetest taste1
#' @docType data
#' @format A data frame with 16 observations on 2 (\code{taste1}) or 4
#' (\code{tastetest}) variables. \itemize{ \item{score}{ taste score
#' from a group of 50 testers} \item{scr}{ a factor with levels
#' \code{coarse} \code{fine}} \item{liq}{ a factor with levels \code{hi}
#' \code{lo}} \item{type}{ a factor with levels \code{A} \code{B}
#' \code{C} \code{D}} }
#' @source E. Street and M. G. Carroll, \emph{Preliminary evaluation of a food
#' product}, Statistics: A Guide to the Unknown (Judith M. Tanur et al., eds.),
#' Holden-Day, 1972, pp. 220-238.
#' @keywords datasets
#' @examples
#'
#' data(tastetest)
#' data(taste1)
#' require(Hmisc)
#' xyplot(score~scr, data=tastetest)
#' xyplot(score~scr, groups=liq, tastetest, type='a')
#' favstats(score~scr, data=tastetest)
#'
NULL
#' Estimating tirewear
#'
#' Treadwear is estimated by two methods: weight loss and groove wear.
#'
#'
#' @name tirewear
#' @rdname tirewear
#' @docType data
#' @format A data frame with 16 observations on the following 2 variables.
#' \itemize{ \item{weight}{ estimated wear (1000's of miles) base on
#' weight loss} \item{groove}{ estimated wear (1000's of miles) based on
#' groove wear} }
#' @references R. D. Stichler, G. G. Richey, and J. Mandel, "Measurement of
#' Treadware of Commercial Tires", \emph{Rubber Age}, 73:2 (May 1953).
#' @source These data are available at DASL, the Data and Story Library
# (\url{ftp://sunsite.univie.ac.at/mirrors/lib.stat.cmu.edu/DASL/.index.html}).
#' (\url{http://lib.stat.cmu.edu/DASL/}).
#' @keywords datasets
#' @examples
#'
#' data(tirewear)
#' xyplot(weight ~ groove, data=tirewear)
#'
NULL
#' New England traffic fatalities (1951-1959)
#'
#' Used by Tufte as an example of the importance of context, these data show
#' the traffic fatality rates in New England in the 1950s. Connecticut
#' increased enforcement of speed limits in 1956. In their full context, it is
#' difficult to say if the decline in Connecticut traffic fatalities from 1955
#' to 1956 can be attributed to the stricter enforcement.
#'
#'
#' @name traffic
#' @rdname traffic
#' @docType data
#' @format A data frame with 9 observations on the following 6 variables.
#' \itemize{ \item{year}{ a year from 1951 to 1959}
#' \item{cn.deaths}{ number of traffic deaths in Connecticut}
#' \item{ny}{ deaths per 100,000 in New York} \item{cn}{ deaths
#' per 100,000 in Connecticut} \item{ma}{ deaths per 100,000 in
#' Massachusetts} \item{ri}{ deaths per 100,000 in in Rhode Island} }
#' @references Donald T. Campbell and H. Laurence Ross. "The Connecticut
#' Crackdown on Speeding: Time-Series Data in Quasi-Experimental Analysis",
#' \emph{Law \& Society Review} Vol. 3, No. 1 (Aug., 1968), pp. 33-54.
#'
#' Gene V. Glass. "Analysis of Data on the Connecticut Speeding Crackdown as a
#' Time-Series Quasi-Experiment" \emph{Law & Society Review}, Vol. 3, No. 1
#' (Aug., 1968), pp. 55-76.
#' @source Tufte, E. R. \emph{The Visual Display of Quantitative Information},
#' 2nd ed. Graphics Press, 2001.
#' @keywords datasets
#' @examples
#'
#' data(traffic)
#' xyplot(cn.deaths ~ year, data=traffic, type=c('l','g'))
#' trafficLong <- reshape(traffic,direction='long', idvar="year",
#' varying=list(3:6), v.names='deathRate',
#' times=names(traffic)[3:6], timevar='state')
#' xyplot(deathRate~year, groups=state, data=trafficLong, type='b',
#' auto.key=list(lines=TRUE, points=FALSE, columns=2))
#'
NULL
#' Trebuchet data
#'
#' Measurements from an experiment that involved firing projectiles with a
#' small trebuchet under different conditions.
#'
#'
#' @name trebuchet
#' @rdname trebuchet
#' @aliases trebuchet trebuchet1 trebuchet2
#' @docType data
#' @format Data frames with the following variables. \itemize{
#' \item{object}{ the object serving as projectile\code{bean} \code{big
#' washerb} \code{bigWash} \code{BWB} \code{foose} \code{golf} \code{MWB}
#' \code{SWB} \code{tennis ball} \code{wood}}
#' \item{projectileWt}{ weight of projectile (in grams)}
#' \item{counterWt}{ weight of counter weight (in kg)}
#' \item{distance}{ distance projectile traveled (in cm)}
#' \item{form}{ a factor with levels \code{a} \code{b} \code{B} \code{c}
#' describing the configuration of the trebuchet.} }
#' @source Data collected by Andrew Pruim as part of a Science Olympiad
#' competition.
#' @keywords datasets
#' @examples
#'
#' data(trebuchet); data(trebuchet1); data(trebuchet2)
#' xyplot(distance~projectileWt, data=trebuchet1)
#' xyplot(distance~projectileWt, data=trebuchet2)
#' xyplot(distance~projectileWt, groups=projectileWt, data=trebuchet)
#'
NULL
#' Utilities bills
#'
#' Data from utility bills at a residence.
#'
#'
#' @name utilities
#' @rdname utilities
#' @aliases utilities utilities2
#' @docType data
#' @format A data frame the following variables. \itemize{
#' \item{month}{ month (coded as a number)} \item{day}{ day of
#' month on which bill was calculated} \item{year}{ year of bill}
#' \item{temp}{ average temperature (F) for billing period}
#' \item{kwh}{ electricity usage (kwh)} \item{ccf}{ gas usage
#' (ccf)} \item{thermsPerDay}{ a numeric vector}
#' \item{billingDays}{ number of billing days in billing period}
#' \item{totalbill}{ total bill (in dollars)} \item{gasbill}{ gas
#' bill (in dollars)} \item{elecbill}{ exectric bill (in dollars)}
#' \item{notes}{ notes about the billing period}
#' \item{ccfpday}{ average gas usage per day [\code{utilities2} only]}
#' \item{kwhpday}{ average electric usage per day [\code{utilities2}
#' only]} \item{gasbillpday}{ gas bill divided by billing days
#' [\code{utilities2} only]} \item{elecbillpday}{ electric bill divided
#' by billing days a numeric vector [\code{utilities2} only]}
#' \item{totalbillpday}{ total bill divided by billing days a numeric
#' vector [\code{utilities2} only]} \item{therms}{ \code{thermsPerDay *
#' billingDays} [\code{utilities2} only]} \item{monthsSinceY2K}{ months
#' since 2000 [\code{utilities2} only]} }
#' @source Daniel T. Kaplan, \emph{Statistical modeling: A fresh approach},
#' 2009.
#' @keywords datasets
#' @examples
#'
#' data(utilities); data(utilities2)
#' xyplot(gasbill ~ temp, data=utilities)
#' xyplot(gasbillpday ~ temp, data=utilities2)
#'
NULL
#' Women in the workforce
#'
#' The labor force participation rate of women in each of 19 U.S. cities in
#' each of two years. # Reference: United States Department of Labor Statistics
#' # # Authorization: free use # # Description: # # Variable Names: # # 1.
#' City: City in the United States # 2. labor72: Labor Force Participation rate
#' of women in 1972 # 3. labor68: Labor Force Participation rate of women in
#' 1968 # # The Data: #
#'
#' @name workingWomen
#' @rdname workingWomen
#' @docType data
#' @format A data frame with 19 observations on the following 3 variables.
#' \itemize{ \item{city}{ name of a U.S. city (coded as a factor with
#' 19 levels)} \item{labor72}{ percent of women in labor force in 1972}
#' \item{labor68}{ percent of women in labor force in 1968} }
#' @source These data are from the United States Department of Labor Statistics
#' and are also available at DASL, the Data and Story Library
# (\url{ftp://sunsite.univie.ac.at/mirrors/lib.stat.cmu.edu/DASL/.index.html}).
#' (\url{http://lib.stat.cmu.edu/DASL/}).
#' @keywords datasets
#' @examples
#'
#' data(workingWomen)
#' xyplot(labor72 ~ labor68, workingWomen)
#'
NULL
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