% 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)
% 
% The data were collected by J.C. Fisher and used in his paper: "Homicide in
% Detroit: The Role of Firearms", Criminology, vol.14, 387-400 (1976)
% 
% The data are on the homicide rate in Detroit for the years 1961-1973.
% FTP    - Full-time police per 100,000 population
% UEMP   - % unemployed in the population
% MAN    - number of manufacturing workers in thousands
% LIC    - Number of handgun licences per 100,000 population
% GR     - Number of handgun registrations per 100,000 population
% CLEAR  - % homicides cleared by arrests
% WM     - Number of white males in the population
% NMAN   - Number of non-manufacturing workers in thousands
% GOV    - Number of government workers in thousands
% HE     - Average hourly earnings
% WE     - Average weekly earnings
% 
% HOM    - Number of homicides per 100,000 of population
% ACC    - Death rate in accidents per 100,000 population
% ASR    - Number of assaults per 100,000 population
% 
% N.B. Each case takes two lines.
% 
%  FTP    UEMP     MAN     LIC      GR    CLEAR     WM    NMAN     GOV     HE
%   WE    HOM      ACC     ASR
% 
%
%
% Information about the dataset
% CLASSTYPE: numeric
% CLASSINDEX: 12
%

@relation detroit

@attribute FTP REAL
@attribute UEMP REAL
@attribute MAN REAL
@attribute LIC REAL
@attribute GR REAL
@attribute CLEAR REAL
@attribute WM INTEGER
@attribute NMAN REAL
@attribute GOV REAL
@attribute HE REAL
@attribute WE REAL
@attribute HOM REAL
@attribute ACC REAL
@attribute ASR REAL

@data
260.35,11.0,455.5,178.15,215.98,93.4,558724,538.1,133.9,2.98,117.18,8.60,39.17,306.18
269.80,7.0,480.2,156.41,180.48,88.5,538584,547.6,137.6,3.09,134.02,8.90,40.27,315.16
272.04,5.2,506.1,198.02,209.57,94.4,519171,562.8,143.6,3.23,141.68,8.52,45.31,277.53
272.96,4.3,535.8,222.10,231.67,92.0,500457,591.0,150.3,3.33,147.98,8.89,49.51,234.07
272.51,3.5,576.0,301.92,297.65,91.0,482418,626.1,164.3,3.46,159.85,13.07,55.05,230.84
261.34,3.2,601.7,391.22,367.62,87.4,465029,659.8,179.5,3.60,157.19,14.57,53.90,217.99
268.89,4.1,577.3,665.56,616.54,88.3,448267,686.2,187.5,3.73,155.29,21.36,50.62,286.11
295.99,3.9,596.9,1131.21,1029.75,86.1,432109,699.6,195.4,2.91,131.75,28.03,51.47,291.59
319.87,3.6,613.5,837.60,786.23,79.0,416533,729.9,210.3,4.25,178.74,31.49,49.16,320.39
341.43,7.1,569.3,794.90,713.77,73.9,401518,757.8,223.8,4.47,178.30,37.39,45.80,323.03
356.59,8.4,548.8,817.74,750.43,63.4,387046,755.3,227.7,5.04,209.54,46.26,44.54,357.38
376.69,7.7,563.4,583.17,1027.38,62.5,373095,787.0,230.9,5.47,240.05,47.24,41.03,422.07
390.19,6.3,609.3,709.59,666.50,58.9,359647,819.8,230.2,5.76,258.05,52.33,44.17,473.01
