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<html><head><title>R: N. L. Prater's Petrol Refinery Data</title>
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<table width="100%" summary="page for petrol {MASS}"><tr><td>petrol {MASS}</td><td align="right">R Documentation</td></tr></table>
<h2>N. L. Prater's Petrol Refinery Data</h2>
<h3>Description</h3>
<p>
The yield of a petroleum refining process with four covariates.
The crude oil appears to come from only 10 distinct samples.
</p>
<p>
These data were originally used by Prater (1956) to
build an estimation equation for the yield of the refining
process of crude oil to gasoline.
</p>
<h3>Usage</h3>
<pre>
petrol
</pre>
<h3>Format</h3>
<p>
The variables are as follows
<dl>
<dt><code>No</code></dt><dd>Crude oil sample identification label. (factor)
</dd>
<dt><code>SG</code></dt><dd>Specific gravity, degrees API. (Constant within sample.)
</dd>
<dt><code>VP</code></dt><dd>Vapour pressure in psi. (Constant within sample.)
</dd>
<dt><code>V10</code></dt><dd>Volatility of crude; ASTM 10% point. (Constant within sample.)
</dd>
<dt><code>EP</code></dt><dd>Desired volatility of gasoline. (The end point. Varies within sample.)
</dd>
<dt><code>Y</code></dt><dd>Yield as a percentage of crude.
</dd></dl>
<h3>Source</h3>
<p>
N. H. Prater (1956)
Estimate gasoline yields from crudes.
<EM>Petroleum Refiner</EM>
<B>35</B>, 236–238.
</p>
<p>
This dataset is also given in
D. J. Hand, F. Daly, K. McConway, D. Lunn and E. Ostrowski (eds) (1994)
<EM>A Handbook of Small Data Sets.</EM>
Chapman & Hall.
</p>
<h3>References</h3>
<p>
Venables, W. N. and Ripley, B. D. (2002)
<EM>Modern Applied Statistics with S.</EM> Fourth edition. Springer.
</p>
<h3>Examples</h3>
<pre>
library(nlme)
Petrol <- petrol
Petrol[, 2:5] <- scale(as.matrix(Petrol[, 2:5]), scale = FALSE)
pet3.lme <- lme(Y ~ SG + VP + V10 + EP,
random = ~ 1 | No, data = Petrol)
pet3.lme <- update(pet3.lme, method = "ML")
pet4.lme <- update(pet3.lme, fixed = Y ~ V10 + EP)
anova(pet4.lme, pet3.lme)
</pre>
<hr><div align="center">[Package <em>MASS</em> version 7.2-44 <a href="00Index.html">Index]</a></div>
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