% Data from StatLib (ftp stat.cmu.edu/datasets) % % 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. @relation detroit @attribute 'FTP' real @attribute 'UEMP' real @attribute 'MAN' real @attribute 'LIC' real @attribute 'GR' real @attribute 'CLEAR' real @attribute 'WM' real @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, 558724.0, 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, 538584.0, 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, 519171.0, 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, 500457.0, 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, 482418.0, 626.1, 164.3, 3.46, 159.85, 13.0, 55.05, 230.84 261.34, 3.2 , 601.7, 391.22, 367.62, 87.4, 465029.0, 659.8, 179.5, 3.60, 157.19, 14.5, 53.90, 217.99 268.89, 4.1 , 577.3, 665.56, 616.54, 88.3, 448267.0, 686.2, 187.5, 3.73, 155.29, 21.3, 50.62, 286.11 295.99, 3.9 , 596.9, 1131.2, 1029.7, 86.1, 432109.0, 699.6, 195.4, 2.91, 131.75, 28.0, 51.47, 291.59 319.87, 3.6 , 613.5, 837.60, 786.23, 79.0, 416533.0, 729.9, 210.3, 4.25, 178.74, 31.4, 49.16, 320.39 341.43, 7.1 , 569.3, 794.90, 713.77, 73.9, 401518.0, 757.8, 223.8, 4.47, 178.30, 37.3, 45.80, 323.03 356.59, 8.4 , 548.8, 817.74, 750.43, 63.4, 387046.0, 755.3, 227.7, 5.04, 209.54, 46.2, 44.54, 357.38 376.69, 7.7 , 563.4, 583.17, 1027.3, 62.5, 373095.0, 787.0, 230.9, 5.47, 240.05, 47.2, 41.03, 422.07 390.19, 6.3 , 609.3, 709.59, 666.50, 58.9, 359647.0, 819.8, 230.2, 5.76, 258.05, 52.3, 44.17, 473.01