R Dataset / Package pscl / absentee

How To Create a Barplot

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How To Create a Pie Chart

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How To Compute the Mean

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How To Create a Plot

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How to Compute the Median

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Attachment Size
dataset-99508.csv 849 bytes
Dataset License
GNU General Public License v2.0
Documentation License
GNU General Public License v2.0
Documentation

On this Picostat.com statistics page, you will find information about the absentee data set which pertains to Absentee and Machine Ballots in Pennsylvania State Senate Races. The absentee data set is found in the pscl R package. You can load the absentee data set in R by issuing the following command at the console data("absentee"). This will load the data into a variable called absentee. If R says the absentee data set is not found, you can try installing the package by issuing this command install.packages("pscl") and then attempt to reload the data. If you need to download R, you can go to the R project website. You can download a CSV (comma separated values) version of the absentee R data set. The size of this file is about 849 bytes.


Absentee and Machine Ballots in Pennsylvania State Senate Races

Description

Absentee ballot outcomes contrasted with machine ballots, cast in Pennsylvania State Senate elections, selected districts, 1982-1993.

Usage

data(absentee)

Format

A data frame with 22 observations on the following 8 variables.

year

a numeric vector, year of election, 19xx

district

a numeric vector, Pennsylvania State Senate district

absdem

a numeric vector, absentee ballots cast for the Democratic candidate

absrep

a numeric vector, absentee ballots cast for the Republican candidate

machdem

a numeric vector, votes cast on voting machines for the Democratic candidate

machrep

a numeric vector, votes cast on voting machines for the Republican candidate

dabs

a numeric vector, Democratic margin among absentee ballots

dmach

a numeric vector, Democratic margin among ballots case on voting machines

Details

In November 1993, the state of Pennsylvania conducted elections for its state legislature. The result in the Senate election in the 2nd district (based in Philadelphia) was challenged in court, and ultimately overturned. The Democratic candidate won 19,127 of the votes cast by voting machine, while the Republican won 19,691 votes cast by voting machine, giving the Republican a lead of 564 votes. However, the Democrat won 1,396 absentee ballots, while the Republican won just 371 absentee ballots, more than offsetting the Republican lead based on the votes recorded by machines on election day. The Republican candidate sued, claiming that many of the absentee ballots were fraudulent. The judge in the case solicited expert analysis from Orley Ashenfelter, an economist at Princeton University. Ashenfelter examined the relationship between absentee vote margins and machine vote margins in 21 previous Pennsylvania Senate elections in seven districts in the Philadelphia area over the preceding decade.

Source

Ashenfelter, Orley. 1994. Report on Expected Asbentee Ballots. Typescript. Department of Economics, Princeton University.

References

Ashenfelter, Orley, Phillip Levine and David Zimmerman. 2003. Statistics and Econometrics: Methods and Applications. New York: John Wiley and Sons.

Jackman, Simon. 2009. Bayesian Analysis for the Social Sciences. Wiley: Hoboken, New Jersey. Examples 2.13, 2.14, 2.15.

Examples

data(absentee)
summary(absentee)denom <- absentee$absdem + absentee$absrep
y <- (absentee$absdem - absentee$absrep)/denom * 100
denom <- absentee$machdem + absentee$machrep
x <- (absentee$machdem - absentee$machrep)/denom *100ols <- lm(y ~ x,
          subset=c(rep(TRUE,21),FALSE)  ## drop data point 22
          )## predictions for disputed absentee point
yhat22 <- predict(ols,
                  newdata=list(x=x[22]),
                  se.fit=TRUE,
                  interval="prediction")
tstat <- (y[22]-yhat22$fit[,"fit"])/yhat22$se.fit
cat("tstat on actual outcome for obs 22:",tstat,"\n")
cat(paste("Pr(t>",round(tstat,2),") i.e., one-sided:\n",sep=""))
cat(1-pt(tstat,df=yhat22$df),"\n")## make a picture
xseq <- seq(min(x)-.1*diff(range(x)),
            max(x)+.1*diff(range(x)),
            length=100)
yhat <- predict(ols,interval="prediction",
                newdata=list(x=xseq))
plot(y~x,
     type="n",
     axes=FALSE,
     ylim=range(yhat,y),
     xlim=range(xseq),xaxs="i",
     xlab="Democratic Margin, Machine Ballots (Percentage Points)",
     ylab="Democratic Margin, Absentee Ballots (Percentage Points)")
polygon(x=c(xseq,rev(xseq)),  ## overlay 95% prediction CI
        y=c(yhat[,"lwr"],rev(yhat[,"upr"])),
        border=FALSE,
        col=gray(.85))
abline(ols,lwd=2)           ## overlay ols
points(x[-22],y[-22],pch=1) ## data
points(x[22],y[22],pch=16)  ## disputed data pointtext(x[22],y[22],
     "Disputed\nElection",
     cex=.75,
     adj=1.25)
axis(1)
axis(2)
--

Dataset imported from https://www.r-project.org.

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