/* 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. N.B. Each case takes two lines. */ title 'Homicide in Detroit'; data detroit; label POLICE ='Full-time police per 100,000 pop' UNEMP ='% unemployed in the pop' MFG_WRK ='Manufacturing workers (000)' GUN_LIC ='Handgun licences per 100,000 pop' GUN_REG ='Handgun registrations per 100,000 pop' H_ARREST ='% homicides cleared by arrests' W_MALE ='White males in the population' NMFG_WRK ='Non-manufacturing workers (000)' GOV_WRK ='Government workers (000)' H_EARN ='Average hourly earnings' W_EARN ='Average weekly earnings' HOMICIDE ='Homicides per 100,000 of pop' ACC ='Death rate in accidents per 100,000 pop' ASSAULTS ='Assaults per 100,000 pop'; input POLICE UNEMP MFG_WRK GUN_LIC GUN_REG H_ARREST W_MALE NMFG_WRK GOV_WRK H_EARN #2 W_EARN HOMICIDE ACC ASSAULTS; yr = _n_; year = yr+1960; datalines; 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 ;