Package 'tigerstats'

Description

tigerstats

Details

Datasets and functions useful for teaching elementary statistics.

Author(s)

Rebekah Robinson ([email protected]), Homer White ([email protected])

References

https://homerhanumat.github.io/tigerstats

Description

Alcohol policy violations on the Georgetown College campus over several years.

Format

A data frame with 10 observations on the following 4 variables.

year

Academic year ending with Spring of the given year.

enrollment

Full-time equivalent enrollment.

writeups

Number of write-ups for alcohol violations.

writeups.per.100

Number of write-ups per 100 students.

Source

Collected by MAT 111 students as a project.

Description

Study conducted in November 2001 by students in MAT 111. Subjects were 267 Georgetown College students. Not all subjects got the same survey form.

Format

A data frame with 268 observations on the following 8 variables.

def.race

Suggested race of the defendant in the survey form.

vic.race

Suggested race of the victim in the survey form.

conc.situation

Scenario described in the in the "rock concert" question on the survey form.

sentence

Sentence, in years, recommended for the defendant.

conc.decision

Whether or not the subject chose to buy a ticket (or buy another ticket).

year

Class rank of the subject.

sex

a factor with level Sex of the survey participant.

major

possible values: humanities math.sci pre.prof social.sci Type of major the subject intends.

Details

Here is a sample survey form, with variants noted.

Attitudes Survey

Crime: You are on a jury for a manslaughter case in Lewistown, PA. The defendant has been found guilty, and in Pennsylvania it is part of the job of the jury to recommend a sentence to the judge. The facts of the case are as follows. The defendant, Tyrone Marcus Watson, a 35-year old native of Lewistown, was driving under the influence of alcohol on the evening of Tuesday July 17, 2001. At approximately 11:00 PM Watson drove through a red light, striking a pedestrian, Betsy Brockenheimer, a 20-year old resident of Lewistown. Brockenheimer was taken unconscious to the hospital and died of her injuries about one hour later. Watson did not flee the scene, nor did he resist arrest.

The prior police record for Mr. Watson is as follows: two minor traffic violations, and one previous arrest, five years ago, for DUI. No one was hurt in that incident.

Watson has now been convicted of DUI and manslaughter. The minimum jail term for this combination of offenses is two years; the maximum term is fifty years. In the blank below, write a number from 2 to 50 as your recommended length of sentence for Tyrone Marcus Watson. _______________

[In the question above, name of defendant could vary: either William Shane Winchester or Tyrone Marcus Watson. The name of the victim could also vary: either Betsy Brockenheimer or Latisha Dawes.]

Spending Habits

You have purchased a $30 ticket to see a rock concert in Rupp Arena. When you arrive at the Arena on the night of the performance, you find that you have lost the ticket. You have no receipt, so it will not be possible to see the concert unless you purchase another ticket. Would you purchase another ticket? Circle below.

YES NO

[In other forms, the question above could have been: You plan to see a rock concert in Rupp Arena. Tickets for the performance are $30. When you arrive at the Arena on the night of the performance, you find that you have lost two bills from your purse or wallet: a ten and a twenty. Would you buy the ticket anyway?]

Respondent Data

I am (circle one): freshman sophomore junior senior

I am (circle one) male female

(Optional) My intended major is: _____________________

Source

Georgetown College

Description

Wrapper for barchart in package lattice. Creates a bar chart from raw data using formula-data syntax similar to that of xtabs, or from a table. Defaults to a "standard" bar chart in which the bars are vertical and un-stacked. Supports percentage bar charts.

Usage

barchartGC(x,data=parent.frame(),type="frequency",flat=FALSE,auto.key=TRUE,
                       horizontal=FALSE,stack=FALSE,...)

Arguments

Value

A trellis object describing the bar chart.

Author(s)

Homer White [email protected]

Examples

#bar chart of counts for one factor variable:
barchartGC(~sex,data=m111survey)

#bar chart with percentages and title:
barchartGC(~sex,data=m111survey,
   type="percent",
   main="Distribution of Sex")

#bar chart of counts, to study the relationship between
#two factor variables:
barchartGC(~sex+seat,data=m111survey)

#percentage bar chart, two factor variables:
barchartGC(~sex+seat,data=m111survey,type="percent")

#From tabulated data:
sexseat <- xtabs(~sex+seat,data=m111survey)
barchartGC(sexseat,type="percent",main="Sex and Seating Preference")

#from tabulated data:
dieTosses <- c(one=8,two=18,three=11,four=7,five=9,six=7)
barchartGC(dieTosses,main="60 Rolls of a Die")

# a "flat" bar chart, pictorial version of xtabs() 
barchartGC(~sex+seat,data=m111survey,flat=TRUE,ylab="Sex")

# a "flat" bar chart, pictorial version of xtabs() 
barchartGC(~sex+seat,data=m111survey,type="percent",flat=TRUE,ylab="Sex")

Description

Experiment performed at UC-Davis; fifteen students participated. Each student was asked to place as many beans into a cup as he/she could, in 15 seconds. Each student performed this task once with the dominant hand, and once with the non-dominant hand, but the order of performance was randomized. The purpose of the study was to see whether manual dexterity was better for the dominant hand. Terminology: your dominant hand is the hand you use the most.

Format

A data frame with 15 observations on the following 3 variables.

Dom

Number of beans placed into cup with the dominant hand.

NonDom

Number of beans placed with the non-dominant hand.

Diff

Difference in number of beans placed (dominant hand minus non-dominant hand).

Source

Uts and Heckard, Mind on Statistics, Fourth Edition.

Description

An app to investigate the binomial family.

Usage

BinomNorm()

Value

no value. Graphical side-effects only.

Author(s)

Homer White ([email protected])

Examples

## Not run: 
if (require(manipulate)) BinomNorm()

## End(Not run)

Description

An app to investigate how skew-ness in a binomial distribution vanishes when np is large enough. Sample size is set at n = 50, but the user can vary p with a slider.

Usage

BinomSkew()

Value

no value. Graphical side-effects only.

Author(s)

Homer White ([email protected])

Examples

## Not run: 
if (require(manipulate)) BinomSkew()

## End(Not run)

Description

Wrapper for binom.test in package stats. Employs the binomial distribution in inferential procedures for a single proportion.

Usage

binomtestGC(x,data=parent.frame(),n=numeric(),p=NULL,
                         alternative=c("two.sided","less","greater"),
                         success="yes",conf.level=0.95,graph=FALSE,verbose=TRUE)

Arguments

Value

an object of class GCbinomtest.

Author(s)

Homer White [email protected]

Examples

#Confidence interval only:
binomtestGC(~sex,data=m111survey,success="female")

#Confidence interval and two-sided test, Null Hypothesis p = 0.5:
binomtestGC(~sex,data=m111survey,success="female",p=0.5)

#For confidence level other than 95%, use conf.level argument.
#For 90% interval:
binomtestGC(~sex,data=m111survey,success="female",conf.level=0.90)

#For one-sided test, set alternative argument as desired:
binomtestGC(~sex,data=m111survey,p=0.50,
    success="female",alternative="greater")

#Summary data:
#In one sample, 40 successes in 100 trials.  Testing whether p = 0.45.
binomtestGC(x=40,n=100,p=0.45)

Description

PITCHf/x data on nine-time All-Star Miguel Cabrera, who is thought to be one of the best pure hitters in baseball. Covers seasons 2009 through 2012.

Format

A data frame with 6265 observations on the following 12 variables. Each observation is a pitch to Cabrera.

season

The year of play

gamedate

Date of the game in which the pitch was thrown

pitch_type

Type of pitch thrown, as determined by a computer algorithm.

balls

Current ball count

strikes

Current strike count

speed

speed of pitch (in mph). (When crossing plate?)

px

x-coordinate of pitch (in feet, measured from center of plate)

pz

vertical coordinate of pitch (in feet above plate)

swung

Whether or not Cabrera swung at the ball. Factor with levels "no", "yes".

hitx

x-coordinate of landing point of ball (if it was hit). Relative to park.

hity

y-coordinate of landing point of ball (if it was hit). Relative to park.

hit_outcome

Outcome when ball was hit. Factor with levels E (error), H (hit), O (batter out).

Source

Marchi and Albert: analyzing Baseball Data with R, CRC Press 2014. For more on the PITCHf/x system, see http://en.wikipedia.org/wiki/PITCHf/x.

Description

An app to illustrate use of the chi-square statistic to test for a relationship between two categorical variables. The P-value is computed by resampling, and the resamples are done one at a time. A histogram of resampled chi-square statistics is displayed after each resample, and summary information is output to the console.

Usage

ChisqSimSlow(form,data,effects=c("random","fixed"))

Arguments

Value

Graphical and numerical output

Author(s)

Homer White [email protected]

Examples

## Not run: 
ChisqSimSlow(~weather+crowd.behavior,data=ledgejump,effects="fixed")

## End(Not run)

Description

Perform chi-square test, either goodness of fit or test for association. Enter either formula-data input or a summary table. Simulation is optional.

Usage

chisqtestGC(x, data = parent.frame(), p = NULL, correct = TRUE, 
             graph = FALSE, simulate.p.value = FALSE, 
             B = 2000, verbose = TRUE)

Arguments

Value

an object of class GCchisqtest

Author(s)

Homer White [email protected]

Examples

#Goodness of fit test for one factor variable:
chisqtestGC(~seat,data=m111survey,p=c(1/3,1/3,1/3))

#Test for relationship between two factor variables:
chisqtestGC(~sex+seat,data=m111survey)

#You can input a two-way table directly into chisqtestGC():
SexSeat <- xtabs(~sex+seat,data=m111survey)
chisqtestGC(SexSeat)

#Several types of simulation are possible, e.g.:
chisqtestGC(~weather+crowd.behavior,data=ledgejump,simulate.p.value="fixed",B=2500)

#For less ouptut, set argument verbose to FALSE:
chisqtestGC(~sex+seat,data=m111survey,verbose=FALSE)

Description

College-aged males chugging a 12-ounce can of a certain beverage.

Format

A data frame with 13 observations on the following 2 variables.

Weight

Weight of the subject (in pounds).

ChugTime

How long (in seconds) the subject requires to drink the beverage.

Source

Utts and Heckard, Mind on Statistics, Fourth Edition.

Description

An app to investigate how sample size and confidence level affect the width of a confidence interval. A sample is drawn from the input population and a confidence interval for the population mean is calculated. The kernel density plot for the population and the histogram for each new sample are plotted, along with the confidence interval. Summary information is output to the console to tally the number of times the computed confidence interval covers the true population mean and how many times it misses. There is an option to draw 100 or 1000 samples at a time.

Usage

CIMean(form,data)

Arguments

Value

Graphical and numerical output

Author(s)

Rebekah Robinson [email protected]

Examples

## Not run: 
if (require(manipulate)) CIMean(~height,data=imagpop)

## End(Not run)

Description

An app to investigate how many times a confidence interval for one population proportion captures the true population parameter. The true population proportion is plotted as a vertical red line and the user can visually see how changes to the sample, population proportion, sample size, and confidence level affect the width of the confidence interval. Summary information is output to the console to tally the number of times the computed confidence interval covers the true population mean and how many times it misses.

Usage

CIProp()

Value

Graphical and numerical output

Note

Uses manipulate from R Studio

Author(s)

Rebekah Robinson [email protected]

Examples

## Not run: 
if (require(manipulate)) CIProp()

## End(Not run)

Description

Computes column percentages for a given two-way table.

Usage

colPerc(tab)

Arguments

Value

An object of class table, giving column percentages for the input table.

Author(s)

Homer White [email protected]

Examples

MyTable <- xtabs(~weather+crowd.behavior,data=ledgejump)
colPerc(MyTable)

Description

A dataset recreated from summary data that describes relationships between race of defendant, race of victim, and outcome of trial in a number of capital cases in Florida in 1976-1977.

Format

A data frame with 326 rows and 3 variables

defrace

Race of the defendant in the capital case

vicrace

Race of the victim

death

Whether or not the defendant in the case received the death penalty

Source

Michael J. Radelet: "Racial Characteristics and the Imposition of the Death Penalty", American Sociological Review, 46 (1981).

Description

A manipulative app that facilitates exploration of the distribution of a single numerical variable, conditioned upon the values of either a numerical variable or a factor.

Usage

DtrellHist(form,data)

Arguments

Value

Graphical output.

Author(s)

Homer White [email protected]

Examples

## Not run: 
if (require(manipulate)) DtrellHist(~dist|speed,data=cars) 

## End(Not run)

Description

An app to facilitate exploration of the relationship between two numerical variables, conditional upon the values of a third variable.

Usage

DtrellScat(form,data)

Arguments

Value

Graphical and numerical output.

Author(s)

Homer White [email protected]

Examples

## Not run: 
if (require(manipulate)) DtrellScat(sat~salary|frac,data=sat)

## End(Not run)

Description

An app to investigate how the Empirical Rule applies to symmetric data and skewed data. The user can select is they want to view a histogram of symmetric data or skewed data. Vertical bars are also plotted to signify one, two, and three standard deviations from the mean. Summary data is output to the console giving the proportion of the histogram that falls within one, two, and three standard deviations of the mean.

Usage

EmpRule()

Value

Graphical and numerical output

Author(s)

Rebekah Robinson [email protected]

Examples

## Not run: 
if (require(manipulate)) EmpRule()

## End(Not run)

Description

An app to facilitate visual understanding of Empirical Rule approximations of probabilities, percentages.

Usage

EmpRuleGC(mean=0,sd=1,xlab="x")

Arguments

Value

Returns no value. Used for the plotting side-effects.

Note

Uses manipulate in R Studio

Author(s)

Homer White [email protected]

Examples

## Not run: 
if(require(manipulate)) EmpRuleGC(mean=70,sd=3,xlab="Height (inches)")

## End(Not run)

Description

Simple instructional function to compute expected cell counts from a table of observed counts.

Usage

expCounts(tab)

Arguments

Author(s)

Homer White [email protected]

Description

Hypothetical school, used for illustrative purposes

Format

A data frame with 28 observations on the following 5 variables.

Students

Name of each student

Sex

sex of the student

class

class rank of the student

GPA

grade poitn average

Honors

whether or not he student is in the Honors Program

Description

The regression minimizes the residual sum of squares (RSS). In this game, the player chooses slope and y-intercept of a line so as to approximate the regression line. The move-able line is set initially as a horizontal line with height equal to the mean of the y-coordinates of the scatter plot, so initially the residual sum of squares equals the total sum of squares (TSS). The player's score is the sum of the number of turns taken and the difference between the current RSS and the regression line's RSS (as a percentage of TSS-RSS for regression line). The aim is to lower one's score.

Usage

FindRegLine

Value

Graphical and numerical output.

Note

Requires package manipulate, available only in R Studio.

Author(s)

Homer White [email protected]

Examples

## Not run: 
if (require(manipulate)) FindRegLine()

## End(Not run)

Description

A British Ford Escort was driven along a prescribed course. Each drive was done at a different speed, and the fuel efficiency was recorded for each drive.

Format

A data frame with 15 observations on the following 2 variables.

speed

in kilometers per hour.

efficiency

fuel efficiency, measured in liters of fuel required to travel 100 kilometers.

Source

The Basic Practice of Statistics, by Moore and McCabe.

Description

Data on father-son pairs. Collected in 1885 by Francis Galton.

Format

A data frame with 1078 observations on the following 2 variables.

fheight

Height of the father, in inches.

sheight

Height of the son, in inches.

Description

Results of a survey conducted by Georgetown College students on 47 Georgetown College upperclass students.

Format

A data frame with 47 observations on the following 6 variables.

rating.fresh

how happy the subjects remembers being as a first-year student, on a scale of 1 to 10.

rating.js

how happy the subjects feels now, on a scale of 1 to 10.

greek

whether or not the subject belongs to a greek organization.

athlete

whether or not the subject is a varsity athlete

rating.diff

upper-level happiness rating minus remembered first-year rating

happier

whether or not subject feels happier now than as a first-year student

Source

MAT 111 at Georgetown College

Description

Data collected by GC students.

Format

A data frame with 62 observations on the following 4 variables.

height

height of the survey participant, in inches

GPA

grade-point average

enough_Sleep

Does the participant feel that he/she gets enough sleep?

sex

sex of the survey participant

Source

MAT 111 at Georgetown College

Description

The General Social Survey (GSS) is a nationwide poll that has been conducted since 1972 (semiannually since 1994). Most interviews are done face-to-face. For further information, see below.

Format

A data frame with 2765 observations on the following 13 variables.

sex

a factor with levels Female Male

race

a factor with levels AfrAm Hispanic Other White

degree

a factor with levels Bachelor Graduate HighSchool JunColl NotHs

relig

a factor with levels Catholic Jewish Other Protestant

polparty

a factor with levels Democrat Independent Other Republican

cappun

a factor with levels Favor Oppose Whether or not the subject favors capital punishment.

tvhours

the subject estimates number of hours per day he or she watches TV.

marijuan

a factor with levels Legal NotLegal Whether or not subject believes that marijuana should be legalized.

owngun

a factor with levels No Yes. Does the subject own a gun?

gunlaw

a factor with levels Favor Oppose Whether or not the subject favors stricter gunlaws.

age

age of the subject

chldidel

the ideal number of children the subject would like to have.

emailtime

estimated number of hours per week subject spends using email.

Source

National Opinion Research Center: http://www3.norc.org/gss+website/. Found in Uts and Heckard: Mind on Statistics, Fourth Edition.

Description

General Social Survey, 2008

Format

A data frame with 2023 observations on the following 12 variables.

sex

a factor with levels Female Male

race

a factor with levels AfrAm Other White

degree

a factor with levels Bachelor Graduate HighSchool JunColl NotHs

relig

a factor with levels Catholic Jewish None Other Protestant

polparty

a factor with levels Democrat Independent Other Republican

cappun

a factor with levels Favor Oppose

tvhours

a numeric vector

marijuan

a factor with levels Legal NotLegal

owngun

a factor with levels No Yes

gunlaw

a factor with levels Favor Oppose

age

a numeric vector

chldidel

a numeric vector

Source

National Opinion Research Center: http://www3.norc.org/gss+website/. Found in Uts and Heckard: Mind on Statistics, Fourth Edition.

References

For more information see gss02

Description

A selection of variables from the 2012 General Social Survey. The variables are as follows:

• age. Age of the subject.

• sex. Sex of the subject.

• race. Race of the subject.

• polviews. Subject's political views.

• relig. Religion of the subject.

• cappun. Opinion on capital punishment.

• owngun. Whether or not one owns a gun.

• emailhr. Number of hours per week spent on email.

• bigbang. Whether or not subject believes the Big Bang theory is true.

• premarsx. Opinion on premarital sex.

• pornlaw. Should pornography be legal?

• zodiac. Sign of the Zodiac under which the subject was born.

Format

A data frame with 1976 rows and 12 variables

Source

http://www3.norc.org/gss+website/.

Description

A study performed by MAT 111 students at Georgetown College.

Format

A data frame with 100 observations on the following 3 variables.

sex

a factor with levels female male

hair.color

a factor with levels dark light

act

composite ACT score of subject.

Source

MAT 111 at Georgetown College

Description

Height and handspan of a few subjects.

Format

A data frame with 167 observations on the following 3 variables.

Sex

a factor with levels Female Male

Height

height of subject, in inches.

HandSpan

handspan of subject, in centimeters.

Source

Uts and Heckard, Mind on Statistics, Fourth Edition.

Description

The station is located in Hanford, WA.

Format

A data frame with 27 observations on the following 2 variables.

year

calendar year

temp

average high temperature for that year.

Source

For more on the Hanford station, see http://www.hanford.gov/page.cfm/HMS

Description

The weather station is located in Hanford, WA. Note that this dataset is more complete than hanford1

Format

A data frame with 66 observations on the following 2 variables.

year

calendar year

temp

average high temperature for that year.

Source

For more on the Hanford station, see http://www.hanford.gov/page.cfm/HMS

Description

A convenience function to show vignettes associated with package tigerstats. Vignette will open in the user's default browser.

Usage

helpGC(topic)

Arguments

Value

side effects

Author(s)

Homer White ([email protected])

Examples

## Not run: 
helpGC(lmGC)

## End(Not run)

Description

Various year-by-year statistics for the Major League player Ricky Henderson

Format

A data frame with 23 observations on the following 18 variables.

Season

season

TM

team played for

G

number of games played in

AB

number of at-bats

R

runs scored

H

number of base hits

X2B

doubles

X3B

triples

HR

home runs

RBI

runs batted in

BB

bases on balls

SO

number of times struck out

SB

Number of stolen bases

CS

number of times caught stealing

AVG

batting average

OBP

on-base percentage

SLG

slugging rate

OPS

on-base plus slugging

Source

unknown (possibly Albert: Teaching Statistics With Baseball)

Description

Data on the 147 batters inducted into the Major LeagueBaseball Hall of Fame as of the year 2013.

Format

A data frame with 147 observations on the following 29 variables.

Name

Player name

Rk

Unknown

Inducted

Year inducted into Hall of Fame

Yrs

Number of years planed in the Majors

From

First year in the Majors

To

Last year in the Majors

MidCareer

Middle year of player's career

Era

Era of Baseball history in which the player was (for the most part) active. Values are: 19th century (before 1900), Dead Ball (1900-1919), Lively Ball (1920-1940), Integration, (1941-1959), Expansion (1960-1975), Free Agency (1976-1992), Long Ball (1993 +).

ASG

Number of All-Star games played

WAR.pos

Wins above replacement (WAR), as defined for a position player

G

Games played

PA

Number of plate appearances

AB

Number of times at bat

R

Runs scored

H

Base hits

X2B

Doubles

X3B

Triples

Triple.Rate

Number of triples divided by number of times at bat

HR

Home runs

HR.Rate

Number of home runs divided by number of times at bat

RBI

Runs batted in

SB

Number of successful stolen base attempts

CS

Number of times thrown out while attempting to steal a base

BB

Base on Balls (number of times "walked")

SO

Number of times struck out

BA

Batting average

OBP

On base percentage

SLG

Slugging average

OPS

OBP plus SLG

Source

Modified from Marchi and Albert: analyzing Baseball Data with R, CRC Press 2014.

Description

Data on the 70 pitchers inducted into the Major League Baseball Hall of Fame as of the year 2013.

Format

A data frame with 147 observations on the following 32 variables.

Name

Player name

Rk

Unknown

Inducted

Year inducted into Hall of Fame

Yrs

Number of years planed in the Majors

From

First year in the Majors

To

Last year in the Majors

MidCareer

Middle year of player's career

Era

Era of Baseball history in which the player was (for the most part) active. Values are: 19th century (before 1900), Dead Ball (1900-1919), Lively Ball (1920-1940), Integration, (1941-1959), Expansion (1960-1975), Free Agency (1976-1992), Long Ball (1993 +).

ASG

Number of All-Star games played

WAR

Wins above replacement

W

Games won

L

Games lost

W.L.

proportion of games won

ERA

Earned run average

G

Games played

GS

Games started

GF

Games finished

CG

Complete games

SHO

Shut-outs

SV

Saves

IP

Innings pitched

H

Hits allowed

R

Runs allowed

ER

Earned Runs allowed

HR

Home Runs allowed

BB

Bases on Balls (number of "walks")

IBB

Intentional walks

SO

Strikeouts

HBP

Hit batter with pitch (?)

BK

Balks

WP

Wild Pitches

BF

Total batters faced

Source

Modified from Marchi and Albert: analyzing Baseball Data with R, CRC Press 2014.

Description

An imaginary population, used for instructional purposes. The variables are as follows:

• sex. (male, female).

• math. Whether or not you were a mathematics major.

• income. Your annual income, rounded to the nearest $100.

• cappun. Opinion about the death penalty (favor, oppose).

• height. Height in inches.

• idealheight. The height you would like to be, in inches.

• diff. ideal height - actual height.

• kkardashtemp. Your feelings about Kim Kardashian on a 0-100 scale (0=very cold, 100=very warm).

Format

A data frame with 10000 rows and 8 variables

Description

IQs of pairs of siblings.

Format

A data frame with 80 observations on the following 2 variables.

First

IQ of the older sibling.

Second

IQ of the younger sibling.

Source

William Harris, Georgetown College

Description

What will make you yell louder: being killed with a knife or being killed with a gun? Results of an entirely imaginary experiment performed on very strange volunteers. Members of the Knife group are killed by a knife, and members of the Gun group are killed by a gun. The volume of the screams of each subject during slaying is recorded. In order to ensure that the two groups are similar with respect to how loud they can yell in the first place, subjects are blocked by whether or not they have participated in hog-hollering contests. After blocking, subjects are randomly assigned to groups.

Format

A data frame with 20 observations on the following 3 variables.

hoghollerer

a factor with levels no yes whether or not the subject competes in hog-hollering contests

means

a factor with levels gun knife means by which subject is slain

volume

volume of expiring subject's cries.

Source

A morbid imagination.

Description

Students in MAT 111 performed an experiment to see whether the perception of the quality of peanut butter was affected by the labeling on the peanut butter jar. Each subject tasted from two jars, one of which was labeled Jiff, and the other of which was labeled Great Value (a cheaper brand). Unknown to the subjects, both jars contained Great Value peanut butter. Each subject rated the quality of the peanut butter on a scale of 1 to 10.

Format

A data frame with 30 observations on the following 3 variables.

jiffrating

rating subject gave to the PB in the jar with the Jiff label

greatvaluerating

rating subject gave to the PB in the jar with the Great Value label

sex

a factor with levels female male

Source

MAT 111 at Georgetown College

Description

A dataset recreated from summary data describing the relationship between weather and crowd behavior during 21 recorded incidents in England, in which a (suicidal) person was contemplating jumping from a ledge or other high structure and a crowd gathered to watch. The variables are as follows:

• weather. Warm or cool, based on the time of year when the incident occurred.

• crowd.behavior. The crowd either baited the would-be jumper, or was polite.

Format

A data frame with 21 rows and 2 variables

Source

"The baiting crowd in episodes of threatened suicide", Journal of Personality and Social Psychology, 41, 703-709. See also dataset 59 in A Handbook of Small Datasets by Hand et al. See also http://www.ncbi.nlm.nih.gov/pubmed/7288565.

Description

Regression analysis (one numerical predictor variable) with simplified output. Wrapper function for lm in package stats.

Usage

lmGC(form,data=parent.frame(),graph=FALSE,check=FALSE)

Arguments

Value

A list of class "GClm". Elements that may be queried include "slope", "intercept", "s" (residual standard error), "R^2" (unadjusted).

Author(s)

Homer White [email protected]

Examples

#To study the relationship between two numerical variables:
lmGC(fastest~GPA,data=m111survey,graph=TRUE)

Description

Results of a survey of MAT 111 students at Georgetown College.

• height How tall are you, in inches?

• ideal_ht A numeric vector How tall would you LIKE to be, in inches?

• sleep How much sleep did you get last night?

• fastest What is the highest speed at which you have ever driven a car?

• weight_feel How do you feel about your weight?

• love_first Do you believe in love at first sight?

• extra_life Do you believe in extraterrestrial life?

• seat When you have a choice, where do you prefer to sit in a classroom?

• GPA What is your college GPA?

• enough_Sleep Do you think you get enough sleep?

• sex What sex are you?

• diff.ideal.act. Your ideal height minus your actual height.

Format

A data frame with 71 rows and 12 variables

Source

Georgetown College, MAT 111.

Description

Results of a survey given at beginning of semester, to all students in MAT 111.

Format

A data frame with 89 observations on the following 14 variables.

height

Your height in inches.

ideal_ht

How tall you would LIKE to be, in inches.

sleep

How much sleep you got last night, in hours.

fastest

What is the highest speed at which you have ever driven a car (in mph)?

wt_feel

a factor with levels 1_underweight 2_about_right 3_overweight How do you feel about your weight?

love_first

a factor with levels no yes Do you believe in love at first sight?

et_life

a factor with levels no yes Do you believe in life on other planets?

seat

a factor with levels 1_front 2_middle 3_back When you have a choice, where do you prefer to sit in a classroom?

GPA

What is your current GPA?

engh_slp

a factor with levels no yes Do you think you get enough sleep?

sex

a factor with levels female male What sex are you?

anchor

a factor with levels australia united_states (Anchor for the next question.) For the next question, either Australia or the US, along with its population, was given in the leadup information to the question. The "anchor" variable records which version of the question you were given.

canada

"The population of country XXX is YYY million. About what is the population of Canada, in millions?" XXX was either the U.S. or Australia.

diff.ih.ah.

Your ideal height minus your actual height.

Source

MAT 111 at Georgetown College

Description

Results of a survey given at beginning of semester, to all students in MAT 111.

Format

A data frame with 85 observations on the following 14 variables.

height

Your height in inches.

ideal_ht

How tall you would LIKE to be, in inches.

sleep

How much sleep you got last night, in hours.

fastest

What is the highest speed at which you have ever driven a car (in mph)?

weight_feel

a factor with levels 1_underweight 2_about_right 3_overweight How do you feel about your weight?

love_first

a factor with levels no yes Do you believe in love at first sight?

extra_life

a factor with levels no yes Do you believe in life on other planets?

seat

a factor with levels 1_front 2_middle 3_back When you have a choice, where do you prefer to sit in a classroom?

GPA

What is your current GPA?

enough_Sleep

a factor with levels no yes Do you think you get enough sleep?

sex

a factor with levels female male What sex are you?

diff

ideal height minus actual height

symbol

a factor with levels a b (Anchor for the next question.) For the next question, either Australia or the US, along with its population, was given in the leadup information to the question. The "anchor" variable records which version of the question you were given. If "a", the population of Australia was given. If "b", the U.S. population was given.

pop_Canada

"The population of country XXX is YYY million. About what is the population of Canada, in millions?" XXX was either the U.S. or Australia.

Source

MAT 111 at Georgetown College

Description

Makes a function that simulates a game based where your winnings are the sum of a specified number of plays of a discrete random variable with a specified distribution.

Usage

make_game(outcomes, probs, plays)

Arguments

Value

a function of a single parameter n, with default value 1. n is the number of times you simulate the net winnings.

Author(s)

Homer White [email protected]

Examples

## Not run: 
play_game <- make_gmae(
  outcomes = c(-1, 0, 5)
  probs = c(0.4, 0.5, 0.1)
  plays = 2000
)
## Play "plays" times, get net winnings:
sampler()
## Play "plays" times again:
sampler()
## Play "plays" times, a third time:
sampler()
## 1000 more simulations of the net winnings:
sampler(n = 1000)

## End(Not run)

Description

An app to explore the sampling distribution of the sample mean. The user takes one sample at a time from a given population. Output to the console describes relevant features of the sample, and graphical output updates the empirical distribution of the sample mean.

Usage

MeanSampler(form,data,max.sample.size=30,show.sample=FALSE)

Arguments

Value

Graphical and numerical output.

Note

Uses manipulate.

Author(s)

Homer White [email protected]

Examples

## Not run: 
data(imagpop)
if (require(manipulate)) MeanSampler(~income,data=imagpop)

## End(Not run)

Description

An experiment performed by a student at Georgetown College. Forty-four subjects were randomized into four groups. All subjects read an article; one group read in a silent environment, while the other three groups heard each three different genres of music. Each subject took a reading comprehension test afterward.

• sex a factor with levels Female Male

• year class rank of subject

• type type of music subject listened to while reading

• score number of questions correct on reading comprehension test

Format

A data frame with 44 observations on 4 variables.

Source

Matt Doolin, MAT 111 at Georgetown College

Description

Students at GC observed their fellow students in the Cafe at lunch.

Format

A data frame with 86 observations on the following 2 variables.

napkins

number of napkins used by the subject during the meal.

sex

a factor with levels female male Sex of the person being observed

Source

MAT 111 at Georgetown College

Description

Results of a study on non-response to a mail survey. Subjects were residents of Denmark.

Format

A data frame with 4229 observations on the following 3 variables.

residence

where the subject resides: either in Copenhagen, a city outside of Copenhagen, or in the countryside

gender

sex of the subject

response

Whether or not the subject responded to the mail survey

Source

Rebuilt from a contingency table in E. B. Andersen (1991), The Statistical Analysis of Categorical Data, Second Edition. Springer-Verlag, Berlin. Table found in package vcd.

Description

Results of study conducted in Great Britain to see if nicotine withdrawal increases the risk of an accident.

Format

A data frame with 10 observations on the following 3 variables.

year

calendar year

Injuries.before.NSD

number of injury accidents on the day one week prior to National No Smoke Day in the United Kingdom

Injuries.on.NSD

number of injury accidents on National No Smoke Day in the United Kingdom

Source

J. Knowles, "Nicotine withdrawal and road accidents", Science, 400, 128, (8 July 1999). Found in Whitlock and Schluter, The Analysis of Biological Data.

Description

Old faithful geyser at Yellowstone Park.

Format

A data frame with 299 observations on the following 2 variables.

Duration

duration of eruption, in minutes

TimeNext

time until the next eruption, in minutes

Source

Unknown

Description

Results of a retrospective study, conducted in 1973, on 299 women who been surgically treated for ovarian cancer 10 years before.

Format

A data frame with 299 observations on the following 4 variables.

stage

factor indicating the stage of the cancer at the time of operation (early, advanced)

operation

factor indicating the amount of tissue removed during surgery (radical,limited)

survival

whether or not the subject was still alive after ten years (yes,no)

xray

factor indicating whether or not the subject also received x-ray treatments (yes,no)

Source

Rebuilt from a contingency table in E. B. Andersen (1991), The Statistical Analysis of Categorical Data. 2nd edition. Springer-Verlag, Berlin. Table found in package vcd.

Description

Shades desired areas under rectangles of probability histogram for binomial, returns numerical value of the area.

Usage

pbinomGC(bound,region="below",size=100,prob=0.5,graph=FALSE)

Arguments

Value

Numerical value of probability.

Author(s)

Homer White [email protected]

Examples

#This gives P(X <= 6) for binom X with 10 trials, chance of success 0.70 on each trial:
pbinomGC(6,region="below",size=10,prob=0.70)

#This gives P(45 <= X <= 55), where X is binom with 100 trials,
#chance of success on each trial p = 0.50:
pbinomGC(c(45,55),region="between",size=100,prob=0.50)

#This gives P(X >= 7) = P(X > 6), for binom X with 10 trials,
#70% chance of success on each trial
pbinomGC(6,region="above",size=10,prob=0.7)

Description

Shades desired areas under a specified chi-square curve, returns numerical value of the area.

Usage

pchisqGC(bound,region="above",df=NA,xlab="chi_square_statistic",graph=FALSE)

Arguments

Value

Numerical value of area under curve over region. Also plots the chi-square curve with the shaded area.

Author(s)

Homer White [email protected]

Examples

#This gives P(X < 6.8) where X is chisq with 3 degrees of freedom:
pchisqGC(6.8,df=3,region="below")

#This gives P(X >= 6.8), where X is chisq with 3 degrees of freedom
pchisqGC(6.8,df=3,region="above")

Description

A study of students at Penn State University.

Format

A data frame with 190 observations on the following 9 variables.

Sex

a factor with levels F M

HrsSleep

how many hours of sleep the subject gets per night

SQpick

a factor with levels Q S. Each subject was presented with two letters (S and Q), and asked to pick one. This variable indicates which letter the subject picked.

Height

height in inches

RandNumb

a numeric vector: Each subject was asked to choose randomly an integer from 1 to 10.

Fastest

highest speed, in mph, at which subject has ever driven a car

RtSpan

span of the right hand, in centimeters.

LftSpan

span of the left hand, in centimeters.

Form

a factor with levels QorS SorQ. The order of presentation of the S and Q options to the subject varied from one survey form to another. This variable indicates which letter was presented first on the form.

Source

Uts and Heckard, Mind on Statistics, Fourth Edition.

Description

Used by generic plot function

Usage

## S3 method for class 'GClm'
plot(x,...)

Arguments

Value

two diagnostic plots

Author(s)

Homer White [email protected]

Examples

SpeedModel <- lmGC(fastest~GPA,data=m111survey)
plot(SpeedModel)

Description

Used by generic plot function

Usage

## S3 method for class 'polyGC'
plot(x,...)

Arguments

Value

two diagnostic plots

Author(s)

Homer White [email protected]

Examples

mpgModel <- polyfitGC(mpg~wt,data=mtcars)
plot(mpgModel)

Description

Shades desired areas under a specified normal curve, returns numerical value of the area.

Usage

pnormGC(bound,region="below",mean=0,sd=1,graph=FALSE)

Arguments

Value

Numerical value of area under curve over region.

Author(s)

Homer White [email protected]

Examples

#This gives P(X < 75) for X normal with mean=70 and sd=4:
pnormGC(75,region="below",mean=70,sd=4)

#This gives P(X > 71) for X normal with mean=70 and sd=4:
pnormGC(71,region="above",mean=70,sd=4)

#This gives P(-1 < X < 1), for standard normal X:
pnormGC(c(-1,1),region="between")

#This gives P(X < 68 or X > 71), for X normal with mean =70 and sd=4:
pnormGC(c(68,71),region="outside",mean=70,sd=4)

Description

An app to explore the idea of influence. Note how the influence of the blue point wanes as the number of points in the central cloud increases, and also wanes as the correlation of the central cloud increases.

Usage

Points2Watch()

Value

Graphical output.

Note

Requires package manipulate, available only in R Studio. Uses mvrnorm from package MASS.

Author(s)

Homer White [email protected]

Examples

## Not run: 
if (require(manipulate)) Points2Watch()

## End(Not run)

Description

Regression analysis (one numerical predictor variable) with simplified output. Wrapper function for lm in package stats.

Usage

polyfitGC(form,data=parent.frame(),degree=2,graph=TRUE,check=FALSE)

Arguments

Value

A list of class "polyGC". Elements that may be queried include "s" (residual standard error) and "R^2" (unadjusted).

Author(s)

Homer White [email protected]

Examples

#To study the relationship between two numerical variables:
polyfitGC(mpg~wt,data=mtcars,degree=2,graph=TRUE)
#check the second-fdegree fit:
polyfitGC(mpg~wt,data=mtcars,degree=2,check=TRUE)

Description

Instructional function, and possibly a utility function for some apps.

Usage

popsamp(n,pop,...)

Arguments

Value

The sample, as a data frame.

Author(s)

Homer White [email protected]

Examples

data(imagpop)
popsamp(10,imagpop)  #Simple random sampling (no replacement)
popsamp(10,imagpop,replace=TRUE)  #Random sampling with replacement

Description

Used by generic predict function

Usage

## S3 method for class 'GClm'
predict(object,x,level=NULL,...)

Arguments

Value

numeric prediction

Author(s)

Homer White [email protected]

Examples

#predict fastest speed driven, for person with GPA=3.0:
SpeedModel <- lmGC(fastest~GPA,data=m111survey)
predict(SpeedModel,x=3.0)
#include prediction interval:
predict(SpeedModel,x=3.0,level=0.95)

Description

Used by generic predict function

Usage

## S3 method for class 'polyGC'
predict(object,x,level=NULL,...)

Arguments

Value

numeric prediction

Author(s)

Homer White [email protected]

Examples

#predict mpg for a car weighing 3 tons:
mpgModel <- polyfitGC(mpg~wt,data=mtcars,degree=2)
predict(mpgModel,x=3.0)
#include prediction interval:
predict(mpgModel,x=3.0,level=0.95)

Description

An app to explore the sampling distribution of the sample proportion. The user takes one sample at a time from a given population. Output to the console describes relevant features of the sample, and graphical output updates the empirical distribution of the sample proportion.

Usage

PropSampler(form,data,max.sample.size=110,show.sample=FALSE)

Arguments

Value

Graphical and numerical output.

Note

Uses manipulate.

Author(s)

Homer White [email protected]

Examples

## Not run: 
data(imagpop)
if (require(manipulate)) PropSampler(~cappun,data=imagpop)

## End(Not run)

Description

Employs the normal approximation to perform test for one or two proportions.

Usage

proptestGC(x,data=parent.frame(),n=numeric(),p=NULL,
                         alternative=c("two.sided","less","greater"),
                         success="yes",first=NULL,conf.level=0.95,
                         correct=TRUE,graph=FALSE,verbose=TRUE)

Arguments

Value

A list, either of class "gcp1test" (one-proportion) or "gcp2test" (two proportions). Components of this list that may be usefully queried include: "statistic", "p.value", and "interval".

Author(s)

Homer White [email protected]

Examples

data(m111survey)
#2-proportions, formula-data input, 95%-confidence interval only:
proptestGC(~sex+seat,data=m111survey,success="2_middle")

#For other confidence levels, use argument conf.level.  For 90%-interval for one proportion p:
proptestGC(~sex,data=m111survey,success="male",conf.level=0.90)

#one proportion, formula-data input, confidence interval and two-sided test with H_0:  p = 0.33:
proptestGC(~seat,data=m111survey,success="1_front",p=0.33)

#Summary data:
#In first sample, 23 successes out of 100 trials.  In second sample, 33 out of 110.
proptestGC(x=c(23,33),n=c(100,110))

#Summary data:
#In one sample, 40 successes in 100 trials.  Testing whether p = 0.45.
proptestGC(x=40,n=100,p=0.45,correct=TRUE)

#Want less output?  Set argument verbose to FALSE:
proptestGC(~sex+seat,data=m111survey,success="2_middle",p=0.33,verbose=FALSE)

Description

Shades desired areas under a specified t-curve, returns numerical value of the area.

Usage

ptGC(bound,region="between",df=1,graph=FALSE)

Arguments

Value

Numerical value of area under curve over region. Also plots the t-curve with the shaded area.

Author(s)

Homer White [email protected]

Examples

#This gives P(-2 < t < 2) for a t-random variable with 1 degree of freedom:
ptGC(c(-2,2),region="between",df=1)

#This gives P(t < -1) for a t-random variable with 5 degrees of freedom:
ptGC(-1,region="below",df=5)

#This gives P( t < -2 OR t >2), for a t-random variable with 5 degrees of freedom:
ptGC(c(-2,2),region="outside",df=5)

Description

Two football players at GC asked their team-mates to do as many push-ups as they could in two minutes.

Format

A data frame with 30 observations on the following 3 variables.

weight

weight of subject in pounds.

pushups

number of push-ups completed.

position

a factor with levels LINE SKILL: type of position played by the subject. Line positions require high body mass, skill positions require a lot of running around.

Source

MAT 111, Georgetown College

Description

When you know a certain area under a normal density curve, this function returns the x-axis values of the boundary of that area.

Usage

qnormGC(area,region="below",mean=0,sd=1,graph=FALSE)

Arguments

Value

Numerical value of the percentile, and a vector when there are two bounds.

Author(s)

Homer White [email protected]

Examples

#80th percentile of a normal distribution with mean=70 and sd=4:
qnormGC(0.80,region="below",mean=70,sd=4)

#Return value x so that P(X > x) = 0.10 (same as the 90th percentile)
qnormGC(0.10,region="above",mean=70,sd=4)

#This gives the multiplier for 95%-confidence intervals based on the z-statistic
qnormGC(0.95,region="between")

#This gives critical values for a two-sided z-test with alpha = 0.01:
qnormGC(0.01,region="outside")

Description

Makes a function that samples from a normal distribution.

Usage

random_normal_factory(mean, sd)

Arguments

Value

a function of a single parameter n, with default value 1.

Author(s)

Homer White [email protected]

Examples

## Not run: 
sampler <- random_normal_factory(mean = 70, sd = 5)
## sample one
sampler()
## sample another
sampler()
## sample a third time
sampler()
## sample another 1000
sampler(n = 1000)

## End(Not run)

Description

Randomizes subjects into treatment groups according to specified criteria.

Usage

RandomExp(data,sizes=NULL,groups=NULL,block=NULL,seed=NULL)

Arguments

Value

A data frame: the input frame data augmented with a variable treat.grp indicating the assignment of subjects to groups.

Author(s)

Homer White [email protected]

Examples

data(SmallExp) #small hypothetical list of subjects

#completely randomized design
RandomExp(SmallExp)  

#Block with reppect to sex:
RandomExp(SmallExp,sizes=c(8,8),groups=letters[1:2],block="sex")

#Block for both sex and athletic status:
RandomExp(SmallExp,sizes=c(8,8),groups=letters[1:2],block=c("sex","athlete"))

Description

An app to explore estimation of coefficients in simple regression.

Usage

RegEstimate(x=1:10)

Arguments

Value

Graphical and numerical output.

Author(s)

Homer White [email protected]

Examples

## Not run: 
if (require(manipulate)) RegEstimate()

## End(Not run)

Description

Computes row percentages for a given two-way table.

Usage

rowPerc(tab)

Arguments

Value

An object of class table, giving row percentages for the input table.

Author(s)

Homer White [email protected]

Examples

data(ledgejump)
MyTable <- xtabs(~weather+crowd.behavior,data=ledgejump)
rowPerc(MyTable)

Description

Result of an experiment conducted to investigate the effect of salinity level in soil on the growth of plants.

Format

A data frame with 24 observations on the following 3 variables.

salt

amount of salt applied to the plot (in parts per million)

biomass

total biomass of plot at the end of the study period (units unknown)

block

field in which the plot was located

Details

From the source (see below): "Experimental fields of land were located at an agricultural field station, and each field was divided into six smaller plots. Each of the smaller plots was treated with a different amount of salt (measured in ppm) and the biomass at the end of the experiment was recorded."

Source

The Course Notes of Carl Schwarz, Simon Fraser University: http://people.stat.sfu.ca/~cschwarz/CourseNotes/

Description

An app to explore the Central Limit Theorem in the context of the difference of sample means.

Usage

SampDist2Means(pop,max.samp.sizes=50,sim.reps=1000)

Arguments

Value

Graphical and numerical output.

Note

Uses manipulate in R Studio. Also requires package lattice.

Author(s)

Homer White [email protected]

Examples

## Not run: 
data(imagpop)
if (require(manipulate)) SampDist2Means(imagpop)

## End(Not run)

Description

An app to explore the sampling distribution of the difference of two sample proportions.

Usage

SampDist2Props(form,data,max.sample.sizes=100,sim.reps=1000)

Arguments

Value

Graphical and numerical output.

Author(s)

Homer White [email protected]

Examples

## Not run: 
data(imagpop)
SampDist2Props(~sex+cappun,data=imagpop)

## End(Not run)

Description

An app to explore the Central Limit Theorem.

Usage

SampDistMean(pop,max.samp.size=50,sim.reps=1000)

Arguments

Value

Graphical and numerical output.

Note

Uses manipulate in R Studio.

Author(s)

Homer White [email protected]

Examples

## Not run: 
data(imagpop)
if (require(manipulate)) SampDistMean(imagpop)

## End(Not run)

Description

SAT scores by state. The variables are as follows:

• state. A state in the U.S.

• expend. Mean annual expenditure per student (in 1000$).

• ratio. Mean student-teacher ratio.

• salary. Mean annual teacher salary.

• frac. Percentage of students in the state who take the SAT.

• verbal. Mean SAT Verbal score for the state.

• math. Mean SAT Math score for the state.

• sat. Sum of mean Verbal and mean Math.

Format

A data frame with 50 rows and 8 variables

Source

Deborah Lynn Guber, "Getting what you pay for: the debate over equity in public school expenditures" (1999), Journal of Statistics Education 7(2).

Description

Results of an experiment conducted on ten Weddell seals.

Format

A data frame with 10 observations on the following 2 variables.

o2.nonfeeding

Oxygen consumption during recovery time after a dive during which no plankton was consumed by the seal, in ml of O2 per kilogram of weight

o2.feeding

Oxygen consumption during recovery time after a dive during which plankton was consumed, in ml of O2 per kilogram of weight

Source

Williams, T. M., L. A. Fuiman, M. Horning, and R. W. Davis. 2004. The Journal of Experimental Biology 207: 973 to 982. http://jeb.biologists.org/content/207/6/973.full

Description

The regression line is not as steep as the SD Line (line through point of averages, with slope = sd(y)/sd(x)). The difference is especially noticeable when the scatter plot is the result of a sample from a bivariate normal distribution. This app explains why we use the regression line to predict y from x, even though the SD line appears to be a better linear summary of the scatter plot. Can be used as a starting-point for a discussion of "regression to the mean."

Usage

ShallowReg(n=900,rho=0.5)

Arguments

Value

Graphical output.

Note

Uses manipulate, available only in R Studio, and mvrnorm from package MASS.

Author(s)

Homer White [email protected]

Examples

## Not run: 
if (require(manipulate)) ShallowReg()

## End(Not run)

Description

An app to investigate the visual and numerical differences between a sample and a population. A sample is drawn from the input population and then a variable of choice is selected by the user. If a categorical variable is chosen, the user sees a bar chart with red bars designating the population and blue bars designating the sample. Simultaneously, a summary table (of percents) is output to the console for both the population and the sample. If a numerical variable is chose, the kernel density plot for the population is plotted in red and the histogram for each new sample is plotted in blue. Simultaneously, the summary information for minimum, maximum, quartiles, median, mean, and standard deviation are output to the console for both the population and the sample. The size of the sample can be changed to explore how this affects statistics and the plots.

Usage

SimpleRandom()

Value

Graphical and numerical output

Author(s)

Rebekah Robinson [email protected]

Examples

## Not run: 
if (require(manipulate)) SimpleRandom()

## End(Not run)

Description

Similar to SimpleRandom, but with a fixed sample size set by the user.

Usage

SimpleRandom2(n=100)

Arguments

Value

Graphical and numerical output

Author(s)

Homer White [email protected]

Examples

## Not run: 
if (require(manipulate)) SimpleRandom()

## End(Not run)

Description

An app to illustrate the effect of skewness on the shape of a boxplot.

Usage

Skewer()

Value

Graphical output.

Note

Requires manipulate; uses functions from package lattice

Author(s)

Homer White [email protected]

Examples

## Not run: 
if (require(mainpulate)) Skewer()

## End(Not run)

Description

An app to illustrate use of the chi-square statistic to test for goodness of fit. The P-value is computed by resampling, and the resamples are done one at a time. A histogram of resampled chi-square statistics is displayed after each resample, and summary information is output to the console.

Usage

SlowGoodness(x,p)

Arguments

Value

Graphical and numerical output

Author(s)

Homer White [email protected]

Examples

## Not run: 
throws <- c(one=8,two=18,three=11,four=7,five=9,six=7)
SlowGoodness(throws,p=rep(1/6,6))

## End(Not run)

Description

Subjects in a hypothetical experiment

Format

A data frame with 16 observations on the following 3 variables.

name

name of the subject

sex

sex of the subject

athlete

whether or not the subject is an athlete

Description

Biologists were interested in whether beetles prefer areas where beavers have cut down cottonwood trees. (The tree-stumps produce tender green shoots that beetles are thought to like.) 23 circular plots, all of equal area, were studied. For each plot the researchers counted the number of cottonwood stumps, and also the number of clusters of beetle larvae found in the plot.

Format

A data frame with 23 observations on the following 2 variables.

stumps

number of stumps in the plot

larvae

number of larvae clusters in the plot

Source

Basic Practice of Statistics, by Moore and McCabe.

Description

Average temperatures for cities in the United States.

• city Name of the city

• latitude latitude of the city, in degrees north of the Equator

• JanTemp mean temperature of the city in January.

• AprTemp mean temperature of the city in April.

• AugTemp mean temperature of the city in August.

Format

A data frame with 20 observations on 5 variables.

Source

Mind on Statistics, Fourth Edition, Uts and Heckard.

Description

Plot the density curve of a t random variable at various degrees of freedom. Compare with the standard normal curve.

Usage

tExplore()

Value

Used only for graphical side effects.

Author(s)

Homer White [email protected]

Examples

## Not run: 
if (require(manipulate)) tExplore()

## End(Not run)

Description

Modifies the current theme for use with lattice graphics in R Presentation dicuments. Increases size of title, axis lables and axis numbers, thickens some lines, etc.

Usage

theme.rpres()

Value

Returns a list to be supplied as the theme to the lattice function trellis.par.set().

Note

Deprecated in favor of themerpres(). May not appear in future versions.

See Also

trellis.par.set, show.settings

Examples

trellis.par.set(theme=theme.rpres())

Description

Modifies the current theme for use with lattice graphics in R Presentation dicuments. Increases size of title, axis lables and axis numbers, thickens some lines, etc.

Usage

themerpres()

Value

Returns a list to be supplied as the theme to the lattice function trellis.par.set().

See Also

trellis.par.set, show.settings

Examples

trellis.par.set(theme=themerpres())

Description

A waiter recorded all tips earned during a 2.5 month period in early 1990, along with other information about the customers who gave the tips. The variables are as follows:

• obs Observation number of event

• totbill The total bill

• tip Amount of the tip

• sex Sex of tipper (F or M)

• smoker Whether the tipper smokes: Yes or No

• day Day of the week

• time Whether the meal was during the Day or the Night

• size Number of people in the dining party

Format

A data frame with 244 rows and 8 variables

Source

Bryant, P. G. and Smith, M. A.(1995), Practical Data Analysis: Case Found in Interactive and Dynamic Graphics for Data Analysis: With Examples Using R and GGobi, Dianne Cook and Deborah F. Swayne. See also http://www.ggobi.org/book/.

Description

Tornado damage in the U.S., by state. Also includes Puerto Rico.

• state the state

• damage mean annual damage from tornadoes, over a five-year period, in millions of dollars

Format

A data frame with 51 observations on 2 variables.

Source

Moore and McCabe, The Basic Practice of Statistics.

Description

An app to explore the distribution of the t-statistic. The user takes one sample at a time from a given population. Graphical output updates the empirical distribution of the sample mean.

Usage

tSampler(form,data,max.sample.size=30,show.sample=FALSE)

Arguments

Value

Graphical and numerical output.

Note

Uses manipulate.

Author(s)

Homer White [email protected]

Examples

## Not run: 
data(imagpop)
if (require(manipulate)) tSampler(~income,data=imagpop)

## End(Not run)

Description

t-tests and confidence intervals for one and two samples.

Usage

ttestGC(x=NULL,data=parent.frame(),mean=numeric(),sd=numeric(),n=numeric(),
 mu=NULL,alternative=c("two.sided","less","greater"),var.equal=FALSE,
 conf.level=0.95,graph=FALSE,first=NULL,verbose=TRUE)

Arguments

Value

A list of class "GCttest" Components of the list that may be usefully queried include: "statistic", "p.value", and "interval".

Author(s)

Homer White [email protected] for matched pairs.

Examples

#One-sample t, 95%-confidence interval only:
ttestGC(~fastest,data=m111survey)

#For other confidence levels, set argument conf.level as desired.  For 90%-interval:
ttestGC(~fastest,data=m111survey,conf.level=0.90)

# One-sample t, 95%-confidence interval and two-sided test with H_0:  mu = 100:
ttestGC(~fastest,data=m111survey,mu=100)

#Two-sample t, 95%-confidence interval only:
ttestGC(fastest~sex,data=m111survey)

#control order of groups with argument first:
ttestGC(fastest~sex,data=m111survey,first="male")

# Matched pairs, confidence interval with one-sided test, H_0: mu-d = 0:
ttestGC(~ideal_ht-height,data=m111survey,mu=0,alternative="greater")

#Summary data, one sample, one-sided test with H_0:  mu = 52.5:
ttestGC(mean=55,sd=4,n=16,mu=52.5,alternative="greater")

#Summary data, two samples:
ttestGC(mean=c(50,55),sd=c(3,4),n=c(25,40),mu=0)

Description

An app to explore the concepts of Type I and Type II errors, and the concept of power. We take samples from a population that is imagined to be normal, and perform the t-procedures for one mean. The Null Hypothesis is H0: mu=170. A slider allows us to vary the true mean mu.

Usage

Type12Errors()

Value

Graphical and numerical output.

Note

Uses manipulate.

Author(s)

Homer White [email protected]

Examples

## Not run: 
if (require(manipulate)) Type12Errors()

## End(Not run)

Description

Results of a survey of students at UC-Davis.

• Sex a factor with levels Female Male

• TV Number of hours spent watching TV per week

• computer number of hours spent on computer per week

• Sleep hours of sleep per night

• Seat a factor with levels Back Front Middle Where do you prefer to sit in class, when you have a choice?

• alcohol number of alcoholic drinks consumed per week

• Height height in inches

• momheight height of mother, in inches

• dadheight height of father, in inches

• exercise number of hours of exercise per week

• GPA grade point average

• class a factor with levels LibArts NonLib Student Category: liberal arts or not

Format

A data frame with 173 observations on 12 variables..

Source

Mind on Statistics, Fourth Edition, Uts and Heckard.

Description

An app to investigate how the variance and sample size affects the shape of a histogram and violin plot generated from normal data. Summary data (minimum, median, mean, maximum, and quartiles) are displayed in the output for each random sample drawn.

Usage

Variability()

Value

Graphical and numerical output

Note

Uses histogram and bwplot from the lattice package.

Author(s)

Rebekah Robinson [email protected]

Examples

## Not run: 
if (require(manipulate)) Variability()

## End(Not run)

Description

An app to illustrate the effectiveness of the correlation coefficient as a measure of the strength of a linear relationship.

Usage

VaryCorrelation(n=300)

Arguments

Value

Graphical output.

Note

Uses manipulate in R Studio, and mvrnorm from package MASS.

Author(s)

Homer White [email protected]

Examples

## Not run: 
if(require(manipulate)) VaryCorrelation(n=500)

## End(Not run)

Description

PITCHf/x data on Justin Verlander, winner of the 2011 Cy Young Award. Covers his 2009 through 2012 seasons.

Format

A data frame with 15307 observations on the following 12 variables. Each observation is a single pitch.

season

The year of play

gamedate

Date of the game in which the pitch was thrown

pitch_type

Type of pitch thrown: CH (Change-up), CU (Curveball), FF (Four-Seam Fastball), FT (Two-Seam Fastball), SL (Slider). Pitch type is determined by computer algorithm.

balls

Current ball count

strikes

Current strike count

pitches

number of pitches previously thrown in the game

speed

speed of pitch (in mph). (When crossing plate?)

px

x-coordinate of pitch (in feet, measured from center of plate)

pz

vertical coordinate of pitch (in feet above plate)

pfx_x

the horizontal movement, in inches, of the pitch between the release point and home plate, as compared to a theoretical pitch thrown at the same speed with no spin-induced movement. Measured at 40 feet from home plate.

pfx_z

the vertical movement, in inches, of the pitch between the release point and home plate, as compared to a theoretical pitch thrown at the same speed with no spin-induced movement. Measured at 40 feet from home plate.

batter_hand

A factor with two values: L (left) and R (right).

Source

Marchi and Albert: analyzing Baseball Data with R, CRC Press 2014. For more on the PITCHf/x system, see http://en.wikipedia.org/wiki/PITCHf/x.

Description

An app to explore confidence interval for a proportion.

Usage

watch_statisticians(n, p, interval_number = 50, level = 0.95)

Arguments

Value

Graph side-effects

Author(s)

Homer White [email protected]

Examples

## Not run: 
watch_statisticians(n = 100, p = 0.5, interval_number = 50, level = 0.95)

## End(Not run)

Description

An app to explore confidence interval for a proportion.

Usage

watch_statisticians_slow(n, p, interval_number = 50, level = 0.95)

Arguments

Value

Graph side-effects

Author(s)

Homer White [email protected]

Examples

## Not run: 
watch_statisticians_slow(n = 100, p = 0.5, interval_number = 50, level = 0.95)

## End(Not run)

Description

A Study of Risky Behaviors in High School Seniors, from year 2003.

• Sex

• Grades Typical grades you earn in school

• WtAction What do you plan to do about your weight?

• Seatbelt How often do you wear a seat-belt?

• Sunscreen How much do you wear sunscreen?

• Grades_1 Same as grades, but with some groups combined

• Sun_1 Same as sunscreen, but with some groups combined

Format

A data frame with 3042 observations on 7 variables.

Source

Mind on Statistics, Fourth Edition, by Uts and Heckard.