R Dataset / Package sandwich / PublicSchools

On this R-data statistics page, you will find information about the PublicSchools data set which pertains to US Expenditures for Public Schools. The PublicSchools data set is found in the sandwich R package. You can load the PublicSchools data set in R by issuing the following command at the console data("PublicSchools"). This will load the data into a variable called PublicSchools. If R says the PublicSchools data set is not found, you can try installing the package by issuing this command install.packages("sandwich") and then attempt to reload the data with the library() command. 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 PublicSchools R data set. The size of this file is about 969 bytes.

US Expenditures for Public Schools


Per capita expenditure on public schools and per capita income by state in 1979.




A data frame containing 51 observations of 2 variables.


per capita expenditure on public schools,


per capita income.


Table 14.1 in Greene (1993)


Cribari-Neto F. (2004), Asymptotic Inference Under Heteroskedasticity of Unknown Form, Computational Statistics \& Data Analysis, 45, 215-233.

Greene W.H. (1993), Econometric Analysis, 2nd edition. Macmillan Publishing Company, New York.

US Department of Commerce (1979), Statistical Abstract of the United States. US Government Printing Office, Washington, DC.


## Willam H. Greene, Econometric Analysis, 2nd Ed.
## Chapter 14
## load data set, p. 385, Table 14.1
data(PublicSchools)## omit NA in Wisconsin and scale income
ps <- na.omit(PublicSchools)
ps$Income <- ps$Income * 0.0001## fit quadratic regression, p. 385, Table 14.2
fmq <- lm(Expenditure ~ Income + I(Income^2), data = ps)
summary(fmq)## compare standard and HC0 standard errors
## p. 391, Table 14.3
sqrt(diag(vcovHC(fmq, type = "const")))
sqrt(diag(vcovHC(fmq, type = "HC0")))
if(require(lmtest)) {
## compare t ratio
coeftest(fmq, vcov = vcovHC(fmq, type = "HC0"))## White test, p. 393, Example 14.5
wt <- lm(residuals(fmq)^2 ~ poly(Income, 4), data = ps)
wt.stat <- summary(wt)$r.squared * nrow(ps)
c(wt.stat, pchisq(wt.stat, df = 3, lower = FALSE))## Bresch-Pagan test, p. 395, Example 14.7
bptest(fmq, studentize = FALSE)
bptest(fmq)## Francisco Cribari-Neto, Asymptotic Inference, CSDA 45
## quasi z-tests, p. 229, Table 8
## with Alaska
coeftest(fmq, df = Inf)[3,4]
coeftest(fmq, df = Inf, vcov = vcovHC(fmq, type = "HC0"))[3,4]
coeftest(fmq, df = Inf, vcov = vcovHC(fmq, type = "HC3"))[3,4]
coeftest(fmq, df = Inf, vcov = vcovHC(fmq, type = "HC4"))[3,4]
## without Alaska (observation 2)
fmq1 <- lm(Expenditure ~ Income + I(Income^2), data = ps[-2,])
coeftest(fmq1, df = Inf)[3,4]
coeftest(fmq1, df = Inf, vcov = vcovHC(fmq1, type = "HC0"))[3,4]
coeftest(fmq1, df = Inf, vcov = vcovHC(fmq1, type = "HC3"))[3,4]
coeftest(fmq1, df = Inf, vcov = vcovHC(fmq1, type = "HC4"))[3,4]
}## visualization, p. 230, Figure 1
plot(Expenditure ~ Income, data = ps,
xlab = "per capita income",
ylab = "per capita spending on public schools")
inc <- seq(0.5, 1.2, by = 0.001)
lines(inc, predict(fmq, data.frame(Income = inc)), col = 4)
fml <- lm(Expenditure ~ Income, data = ps)
text(ps[2,2], ps[2,1], rownames(ps)[2], pos = 2)

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

Attachments: csv, json

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