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\name{pension}
\alias{pension}
\title{Pension Funds Data}
\description{
The total 1981 premium income of pension funds of Dutch firms,
for 18 Professional Branches, from de Wit (1982).
}
\usage{data(pension, package="robustbase")}
\format{
A data frame with 18 observations on the following 2 variables.
\describe{
\item{\code{Income}}{Premium Income (in millions of guilders)}
\item{\code{Reserves}}{Premium Reserves (in millions of guilders)}
}
}
\source{
P. J. Rousseeuw and A. M. Leroy (1987)
\emph{Robust Regression and Outlier Detection};
Wiley, p.76, table 13.
}
\examples{
data(pension)
plot(pension)
summary(lm.p <- lm(Reserves ~., data=pension))
summary(lmR.p <- lmrob(Reserves ~., data=pension))
summary(lts.p <- ltsReg(Reserves ~., data=pension))
abline( lm.p)
abline(lmR.p, col=2)
abline(lts.p, col=2, lty=2)
## MM: "the" solution is much simpler:
plot(pension, log = "xy")
lm.lp <- lm(log(Reserves) ~ log(Income), data=pension)
lmR.lp <- lmrob(log(Reserves) ~ log(Income), data=pension)
plot(log(Reserves) ~ log(Income), data=pension)
## no difference between LS and robust:
abline( lm.lp)
abline(lmR.lp, col=2)
}
\keyword{datasets}
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