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<html><head><title>R: Numbers of Car Insurance claims</title>
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<table width="100%" summary="page for Insurance {MASS}"><tr><td>Insurance {MASS}</td><td align="right">R Documentation</td></tr></table>
<h2>Numbers of Car Insurance claims</h2>
<h3>Description</h3>
<p>
The data given in data frame <code>Insurance</code> consist of the
numbers of policyholders of an insurance company who were
exposed to risk, and the numbers of car insurance claims made by
those policyholders in the third quarter of 1973.
</p>
<h3>Usage</h3>
<pre>
Insurance
</pre>
<h3>Format</h3>
<p>
This data frame contains the following columns:
<dl>
<dt><code>District</code></dt><dd>district of policyholder (1 to 4): 4 is major cities.
</dd>
<dt><code>Group</code></dt><dd>group of car (1 to 4), <1 litre, 1–1.5 litre, 1.5–2 litre, >2 litre.
</dd>
<dt><code>Age</code></dt><dd>of driver in 4 ordered groups, <25, 25–29, 30–35, >35.
</dd>
<dt><code>Holders</code></dt><dd>numbers of policyholders
</dd>
<dt><code>Claims</code></dt><dd>numbers of claims
</dd></dl>
<h3>Source</h3>
<p>
L. A. Baxter, S. M. Coutts and G. A. F. Ross (1980) Applications of
linear models in motor insurance.
<EM>Proceedings of the 21st International Congress of Actuaries, Zurich</EM>
pp. 11–29
</p>
<p>
M. Aitkin, D. Anderson, B. Francis and J. Hinde (1989)
<EM>Statistical Modelling in GLIM.</EM>
Oxford University Press.
</p>
<h3>References</h3>
<p>
Venables, W. N. and Ripley, B. D. (1999)
<EM>Modern Applied Statistics with S-PLUS.</EM> Third
Edition. Springer.
</p>
<h3>Examples</h3>
<pre>
## main-effects fit as Poisson GLM with offset
glm(Claims ~ District + Group + Age + offset(log(Holders)),
data = Insurance, family = poisson)
# same via loglm
loglm(Claims ~ District + Group + Age + offset(log(Holders)),
data = Insurance)
</pre>
<hr><div align="center">[Package <em>MASS</em> version 7.2-44 <a href="00Index.html">Index]</a></div>
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