Detroit: Detroit Homicide Data for 1961-73
In gencve: General Cross Validation Engine

Description Usage Format Details References Examples

For convenience we have labelled the input variables 1 through 11 to be consistent with the notation used in Miller (2002). Only the first 11 variables were used in Miller's analyses. The best fitting subset regression with these 11 variables, uses only 3 inputs and has a residual sum of squares of 6.77 while using forward selection produces a best fit with 3 inputs with residual sum of squares 21.19. Backward selection and stagewise methods produce similar results. It is remarkable that there is such a big difference. Note that the usual forward and backward selection algorithms may fail since the linear regression using 11 variables gives essentially a perfect fit.

1	data(Detroit)

A data frame with 13 observations on the following 14 variables.

FTP.1: Full-time police per 100,000 population
UEMP.2: Percent unemployed in the population
MAN.3: Number of manufacturing workers in thousands
LIC.4: Number of handgun licences per 100,000 population
GR.5: Number of handgun registrations per 100,000 population
CLEAR.6: Percent homicides cleared by arrests
WM.7: Number of white males in the population
NMAN.8: Number of non-manufacturing workers in thousands
GOV.9: Number of government workers in thousands
HE.10: Average hourly earnings
WE.11: Average weekly earnings
ACC: Death rate in accidents per 100,000 population
ASR: Number of assaults per 100,000 population
HOM: Number of homicides per 100,000 of population

The data were orginally collected and discussed by Fisher (1976) but the complete dataset first appeared in Gunst and Mason (1980, Appendix A). Miller (2002) discusses this dataset throughout his book. The data were obtained from the StatLib data archive.

Fisher, J.C. (1976). Homicide in Detroit: The Role of Firearms. Criminology, vol.14, 387-400.

Gunst, R.F. and Mason, R.L. (1980). Regression analysis and its application: A data-oriented approach. Marcel Dekker.

Miller, A. J. (2002). Subset Selection in Regression. 2nd Ed. Chapman & Hall/CRC. Boca Raton.

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#Detroit data example
data(Detroit)
str(Detroit)

'data.frame':	13 obs. of  14 variables:
 $ FTP.1  : num  260 270 272 273 273 ...
 $ UEMP.2 : num  11 7 5.2 4.3 3.5 3.2 4.1 3.9 3.6 7.1 ...
 $ MAN.3  : num  456 480 506 536 576 ...
 $ LIC.4  : num  178 156 198 222 302 ...
 $ GR.5   : num  216 180 210 232 298 ...
 $ CLEAR.6: num  93.4 88.5 94.4 92 91 87.4 88.3 86.1 79 73.9 ...
 $ WM.7   : num  558724 538584 519171 500457 482418 ...
 $ NMAN.8 : num  538 548 563 591 626 ...
 $ GOV.9  : num  134 138 144 150 164 ...
 $ HE.10  : num  2.98 3.09 3.23 3.33 3.46 3.6 3.73 2.91 4.25 4.47 ...
 $ WE.11  : num  117 134 142 148 160 ...
 $ ACC    : num  39.2 40.3 45.3 49.5 55 ...
 $ ASR    : num  306 315 278 234 231 ...
 $ HOM    : num  8.6 8.9 8.52 8.89 13.07 ...