chmiss

Data from a 1970's study on the relationship between insurance redlining in
Chicago and racial composition, fire and theft rates, age of housing and
income in 47 zip codes. Missing values have been randomly added.

datasets

Books are "Linear Models with R" published 1st Ed. August 2004, 2nd Ed. July 2014 by CRC press, ISBN 9781439887332, and "Extending the Linear Model with R" published by CRC press in 1st Ed. December 2005 and 2nd Ed. March 2016, ISBN 9781584884248 and "Practical Regression and ANOVA in R" contributed documentation on CRAN (now very dated).

Julian Faraway

faraway

Functions and Datasets for Books by Julian Faraway

chmiss function

This dataframe contains the following columns<dl><dt>race</dt>
<dd>racial composition in percent minority</dd><dt>fire</dt>
<dd>fires per 100 housing units</dd>
 <dt>theft</dt>
<dd>theft per 1000 population</dd>
 <dt>age</dt>
<dd>percent of housing units
built before 1939</dd>
 <dt>involact</dt>
<dd>new FAIR plan policies and
renewals per 100 housing units</dd>
 <dt>income</dt>
<dd>median family income
in thousands of dollars</dd>
 <dt>side</dt>
<dd>North or South side of
Chicago</dd></dl>

Format

Chicago insurance redlining — chmiss

This dataframe contains the following columns<dl>

<dt>race</dt>
<dd>racial composition in percent minority</dd>

<dt>fire</dt>
<dd>fires per 100 housing units</dd>
 <dt>theft</dt>
<dd>theft per 1000 population</dd>
 <dt>age</dt>
<dd>percent of housing units
built before 1939</dd>
 <dt>involact</dt>
<dd>new FAIR plan policies and
renewals per 100 housing units</dd>
 <dt>income</dt>
<dd>median family income
in thousands of dollars</dd>
 <dt>side</dt>
<dd>North or South side of
Chicago</dd>



</dl>

chmiss: Chicago insurance redlining

Description

Arguments

Format