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.

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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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```
'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 ...
```

gencve documentation built on May 29, 2017, 7:12 p.m.

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