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\name{hbk}
\alias{hbk}
\docType{data}
\title{Hawkins, Bradu, Kass's Artificial Data}
\description{
Artificial Data Set generated by Hawkins, Bradu, and Kass (1984). The
data set consists of 75 observations in four dimensions (one response
and three explanatory variables). It provides a good example of the
masking effect. The first 14 observations are outliers, created in
two groups: 1--10 and 11--14.
Only observations 12, 13 and 14 appear as outliers when using
classical methods, but can be easily unmasked using robust
distances computed by, e.g., MCD - covMcd().
}
\usage{data(hbk, package="robustbase")}
\format{
A data frame with 75 observations on 4 variables, where the last
variable is the dependent one.
\describe{
\item{X1}{x[,1]}
\item{X2}{x[,2]}
\item{X3}{x[,3]}
\item{Y}{y}
}
}
\note{
This data set is also available in package \pkg{wle} as
\code{artificial}.
}
\source{
Hawkins, D.M., Bradu, D., and Kass, G.V. (1984)
Location of several outliers in multiple regression data using
elemental sets.
\emph{Technometrics} \bold{26}, 197--208.
P. J. Rousseeuw and A. M. Leroy (1987)
\emph{Robust Regression and Outlier Detection};
Wiley, p.94.
}
\examples{
data(hbk)
plot(hbk)
summary(lm.hbk <- lm(Y ~ ., data = hbk))
hbk.x <- data.matrix(hbk[, 1:3])
(cHBK <- covMcd(hbk.x))
}
\keyword{datasets}
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