\HeaderA{Gasoline}{Refinery yield of gasoline}{Gasoline}
\keyword{datasets}{Gasoline}
\begin{Description}\relax
The \code{Gasoline} data frame has 32 rows and 6 columns.
\end{Description}
\begin{Format}\relax
This data frame contains the following columns:
\describe{
\item[yield] a numeric vector giving the percentage of crude oil converted to
gasoline after distillation and fractionation

\item[endpoint] a numeric vector giving the temperature (degrees F) at which all
the gasoline is vaporized

\item[Sample] an ordered factor giving the inferred crude oil sample number

\item[API] a numeric vector giving the crude oil gravity (degrees API)

\item[vapor] a numeric vector giving the vapor pressure of the crude oil
\eqn{(\mathrm{lbf}/\mathrm{in}^2)}{(lbf/in^2)}

\item[ASTM] a numeric vector giving the crude oil 10\% point ASTM---the
temperature at which 10\% of the crude oil has become vapor. 

}
\end{Format}
\begin{Details}\relax
Prater (1955) provides data on crude oil properties and
gasoline yields.  Atkinson (1985)
uses these data to illustrate the use of diagnostics in multiple
regression analysis.  Three of the covariates---\textsf{API},
\textsf{vapor}, and \textsf{ASTM}---measure characteristics of the
crude oil used to produce the gasoline.  The other covariate ---
\textsf{endpoint}---is a characteristic of the refining process.
Daniel and Wood (1980) notice that the covariates characterizing
the crude oil occur in only ten distinct groups and conclude that the
data represent responses measured on ten different crude oil samples.
\end{Details}
\begin{Source}\relax
Prater, N. H. (1955), Estimate gasoline yields from crudes,
\emph{Petroleum Refiner}, \bold{35} (5).

Atkinson, A. C. (1985), \emph{Plots, Transformations, and
Regression}, Oxford Press, New York.

Daniel, C. and Wood, F. S. (1980), \emph{Fitting Equations to Data},
Wiley, New York

Venables, W. N. and Ripley, B. D. (1999) \emph{Modern Applied
Statistics with S-PLUS (3rd ed)}, Springer, New York.
\end{Source}

