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\name{education}
\alias{education}
\docType{data}
\title{Education Expenditure Data}
\description{
Education Expenditure Data, from Chatterjee and Price (1977,
p.108). This data set, representing the education expenditure
variables in the 50 US states, providing an interesting example of
heteroscedacity.
}
\usage{data(education, package="robustbase")}
\format{
A data frame with 50 observations on the following 6 variables.
\describe{
\item{\code{State}}{State}
\item{\code{Region}}{Region (1=Northeastern, 2=North central, 3=Southern, 4=Western)}
\item{\code{X1}}{Number of residents per thousand residing in urban areas in 1970}
\item{\code{X2}}{Per capita personal income in 1973}
\item{\code{X3}}{Number of residents per thousand under 18 years of age in 1974}
\item{\code{Y}}{Per capita expenditure on public education in a
state, projected for 1975}
}
}
\source{
P. J. Rousseeuw and A. M. Leroy (1987)
\emph{Robust Regression and Outlier Detection};
Wiley, p.110, table 16.
}
\examples{
data(education)
education.x <- data.matrix(education[, 3:5])
%%covMcd(education.x)
%%______ FIXME ___ X1:X3 is not what you mean !! ________
summary(lm.education <- lm(Y ~ Region + X1+X2+X3, data=education))
%%summary(lts.education <- ltsReg(Y .....)
## See example(lmrob.M.S) # for how robust regression is used
}
\keyword{datasets}
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