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This is the data set called ‘DETROIT’ in the book 'Subset selection in regression' by Alan J. Miller published in the Chapman & Hall series of monographs on Statistics & Applied Probability, no. 40. The data are unusual in that a subset of three predictors can be found which gives a very much better fit to the data than the subsets found from the Efroymson stepwise algorithm, or from forward selection or backward elimination. The original data were given in appendix A of 'Regression analysis and its application: A data-oriented approach' by Gunst & Mason, Statistics textbooks and monographs no. 24, Marcel Dekker. It has caused problems because some copies of the Gunst & Mason book do not contain all of the data, and because Miller does not say which variables he used as predictors and which is the dependent variable. (HOM was the dependent variable, and the predictors were FTP ... WE)
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A data frame with 13 rows and 14 variables:
FTP: Full-time police per 100,000 population
UEMP: UEMP - % unemployed in the population
MAN: MAN - number of manufacturing workers in thousands
LIC: LIC - Number of handgun licences per 100,000 population
GR: GR - Number of handgun registrations per 100,000 population
CLEAR: CLEAR - % homicides cleared by arrests
WM: WM - Number of white males in the population
NMAN: NMAN - Number of non-manufacturing workers in thousands
GOV: GOV - Number of government workers in thousands
HE: HE - Average hourly earnings
WE: WE - Average weekly earnings:
HOM: HOM - Number of homicides per 100,000 of population
ACC: ACC - Death rate in accidents per 100,000 population
ASR: ASR - Number of assaults per 100,000 population
http://lib.stat.cmu.edu/datasets/detroit
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