X-Git-Url: https://pintos-os.org/cgi-bin/gitweb.cgi?a=blobdiff_plain;f=tests%2Flanguage%2Fstats%2Flogistic.at;h=93882d336c02802750c6fe80f5f947155a436ce0;hb=5d2044f51d79aa63799eb2a7cfc2937c7a4829cf;hp=6952167fa0b58490156132038f10c2af01d3a3c8;hpb=5cab4cf3322f29c0ed7134d23740e07382914f20;p=pspp diff --git a/tests/language/stats/logistic.at b/tests/language/stats/logistic.at index 6952167fa0..93882d336c 100644 --- a/tests/language/stats/logistic.at +++ b/tests/language/stats/logistic.at @@ -1,16 +1,16 @@ dnl PSPP - a program for statistical analysis. dnl Copyright (C) 2017 Free Software Foundation, Inc. -dnl +dnl dnl This program is free software: you can redistribute it and/or modify dnl it under the terms of the GNU General Public License as published by dnl the Free Software Foundation, either version 3 of the License, or dnl (at your option) any later version. -dnl +dnl dnl This program is distributed in the hope that it will be useful, dnl but WITHOUT ANY WARRANTY; without even the implied warranty of dnl MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the dnl GNU General Public License for more details. -dnl +dnl dnl You should have received a copy of the GNU General Public License dnl along with this program. If not, see . dnl @@ -131,9 +131,9 @@ Step,-2 Log likelihood,Cox & Snell R Square,Nagelkerke R Square 1,37.323,.455,.659 Table: Classification Table -,,,Predicted,, +,Observed,,Predicted,, ,,,outcome,,Percentage Correct -,Observed,,1.000,2.000, +,,,1.000,2.000, Step 1,outcome,1.000,43,5,89.6% ,,2.000,4,14,77.8% ,Overall Percentage,,,,86.4% @@ -294,9 +294,9 @@ Step,-2 Log likelihood,Cox & Snell R Square,Nagelkerke R Square 1,275.637,.008,.011 Table: Classification Table -,,,Predicted,, +,Observed,,Predicted,, ,,,female,,Percentage Correct -,Observed,,.00,1.00, +,,,.00,1.00, Step 1,female,.00,0,91,.0% ,,1.00,0,109,100.0% ,Overall Percentage,,,,54.5% @@ -337,411 +337,411 @@ AT_CLEANUP dnl An example to check the behaviour of LOGISTIC REGRESSION with a categorical dnl variable. This examṕle was inspired from that at: -dnl http://www.ats.ucla.edu/stat/spss/dae/logit.htm +dnl http://www.ats.ucla.edu/stat/spss/dae/logit.htm AT_SETUP([LOGISTIC REGRESSION with categorical]) AT_KEYWORDS([categorical categoricals]) AT_DATA([lr-cat.data], [dnl - 620 3.07 2 4 - 800 4.00 3 9 - 580 3.40 2 4 - 600 3.13 2 4 - 540 2.70 2 4 - 660 3.31 4 4 - 480 3.58 1 9 - 620 4.00 1 9 - 680 3.98 2 9 - 580 3.40 4 4 - 760 3.35 3 4 - 700 3.72 2 4 - 460 3.64 1 9 - 540 3.28 3 4 - 680 3.48 3 4 - 740 3.31 1 4 - 460 3.77 3 4 - 740 3.54 1 4 - 600 3.63 3 4 - 620 3.05 2 4 - 560 3.04 3 4 - 520 2.70 3 4 - 640 3.35 3 4 - 620 3.58 2 4 - 660 3.70 4 9 - 500 2.86 4 4 - 640 3.50 2 4 - 720 4.00 3 4 - 720 3.94 3 4 - 400 3.65 2 4 - 800 2.90 2 4 - 520 2.90 3 4 - 440 3.24 4 4 - 580 3.51 2 4 - 500 3.31 3 4 - 440 3.22 1 4 - 540 3.17 1 9 - 420 3.02 1 4 - 780 3.22 2 9 - 440 3.13 4 4 - 800 3.66 1 9 - 580 3.32 2 9 - 480 2.67 2 9 - 700 4.00 1 9 - 740 2.97 2 9 - 700 3.83 2 4 - 640 3.93 2 4 - 800 3.90 2 4 - 400 3.38 2 4 - 700 3.52 2 4 - 680 3.00 4 9 - 540 3.20 1 4 - 580 4.00 2 4 - 780 4.00 2 9 - 220 2.83 3 4 - 580 3.20 2 9 - 580 3.50 2 4 - 620 3.30 1 4 - 520 3.65 4 9 - 600 3.38 3 9 - 660 3.77 3 4 - 580 2.86 4 9 - 580 3.46 2 9 - 560 3.36 3 4 - 740 4.00 3 9 - 480 3.44 3 4 - 640 3.19 4 9 - 600 3.54 1 9 - 540 3.38 4 4 - 500 2.81 3 4 - 360 2.56 3 4 - 460 3.15 4 4 - 460 2.63 2 4 - 440 2.76 2 4 - 740 3.62 4 4 - 380 3.38 2 4 - 640 3.63 1 9 - 800 3.73 1 4 - 660 3.67 2 4 - 760 3.00 2 9 - 420 2.96 1 4 - 740 3.74 4 4 - 800 3.75 2 4 - 620 3.40 2 4 - 660 3.67 3 9 - 400 3.35 3 4 - 680 3.14 2 4 - 660 3.47 3 9 - 660 3.63 2 9 - 420 3.41 4 4 - 660 4.00 1 4 - 680 3.70 2 4 - 620 3.23 3 9 - 520 3.35 3 4 - 500 4.00 3 4 - 400 3.36 2 4 - 700 3.56 1 9 - 540 3.81 1 9 - 520 2.68 3 9 - 540 3.50 2 4 - 700 4.00 2 4 - 600 3.64 3 9 - 800 3.31 3 4 - 520 3.29 1 4 - 580 3.69 1 4 - 380 3.43 3 4 - 560 3.19 3 4 - 760 2.81 1 9 - 540 3.13 2 4 - 660 3.14 2 9 - 520 3.81 1 9 - 680 3.19 4 4 - 540 3.78 4 4 - 500 3.57 3 4 - 660 3.49 2 4 - 340 3.00 2 9 - 400 3.15 2 9 - 420 3.92 4 4 - 760 3.35 2 9 - 700 2.94 2 4 - 540 3.04 1 4 - 780 3.87 4 4 - 560 3.78 2 4 - 700 3.82 3 4 - 400 2.93 3 4 - 440 3.45 2 9 - 800 3.47 3 4 - 340 3.15 3 4 - 520 4.00 1 9 - 520 3.15 3 4 - 600 2.98 2 9 - 420 2.69 2 4 - 460 3.44 2 4 - 620 3.71 1 9 - 480 3.13 2 4 - 580 3.40 3 4 - 540 3.39 3 9 - 540 3.94 3 4 - 440 2.98 3 4 - 380 3.59 4 4 - 500 2.97 4 4 - 340 2.92 3 4 - 440 3.15 2 4 - 600 3.48 2 4 - 420 2.67 3 4 - 460 3.07 2 4 - 460 3.45 3 9 - 480 3.39 4 4 - 480 2.78 3 4 - 720 3.42 2 9 - 680 3.67 2 9 - 800 3.89 2 4 - 360 3.00 3 4 - 620 3.17 2 9 - 700 3.52 4 9 - 540 3.19 2 4 - 580 3.30 2 4 - 800 4.00 3 9 - 660 3.33 2 4 - 380 3.34 3 4 - 720 3.84 3 4 - 600 3.59 2 4 - 500 3.03 3 4 - 640 3.81 2 4 - 540 3.49 1 9 - 680 3.85 3 9 - 540 3.84 2 9 - 460 2.93 3 4 - 380 2.94 3 4 - 620 3.22 2 4 - 740 3.37 4 4 - 620 4.00 2 4 - 800 3.74 1 9 - 400 3.31 3 4 - 540 3.46 4 4 - 620 3.18 2 9 - 480 2.91 1 9 - 300 2.84 2 9 - 440 2.48 4 4 - 640 2.79 2 4 - 400 3.23 4 9 - 680 3.46 2 9 - 620 3.37 1 9 - 700 3.92 2 4 - 620 3.37 2 9 - 620 3.63 2 4 - 620 3.95 3 9 - 560 2.52 2 4 - 520 2.62 2 4 - 600 3.35 2 4 - 700 4.00 1 4 - 640 3.67 3 4 - 640 4.00 3 4 - 520 2.93 4 4 - 620 3.21 4 4 - 680 3.99 3 4 - 660 3.34 3 4 - 700 3.45 3 4 - 560 3.36 1 9 - 800 2.78 2 4 - 500 3.88 4 4 - 700 3.65 2 4 - 680 3.76 3 9 - 660 3.07 3 4 - 580 3.46 4 4 - 460 2.87 2 4 - 600 3.31 4 4 - 620 3.94 4 4 - 400 3.05 2 4 - 800 3.43 2 9 - 600 3.58 1 9 - 580 3.36 2 4 - 540 3.16 3 4 - 500 2.71 2 4 - 600 3.28 3 4 - 600 2.82 4 4 - 460 3.58 2 4 - 520 2.85 3 4 - 740 3.52 4 9 - 500 3.95 4 4 - 560 3.61 3 4 - 620 3.45 2 9 - 640 3.51 2 4 - 660 3.44 2 9 - 660 2.91 3 9 - 540 3.28 1 4 - 560 2.98 1 9 - 800 3.97 1 4 - 720 3.77 3 4 - 720 3.64 1 9 - 480 3.71 4 9 - 680 3.34 2 4 - 680 3.11 2 4 - 540 2.81 3 4 - 620 3.75 2 9 - 540 3.12 1 4 - 560 3.48 2 9 - 720 3.40 3 4 - 680 3.90 1 4 - 640 3.76 3 4 - 560 3.16 1 4 - 520 3.30 2 9 - 640 3.12 3 4 - 580 3.57 3 4 - 540 3.55 4 9 - 780 3.63 4 9 - 600 3.89 1 9 - 800 4.00 1 9 - 580 3.29 4 4 - 360 3.27 3 4 - 800 4.00 2 9 - 640 3.52 4 4 - 720 3.45 4 4 - 580 3.06 2 4 - 580 3.02 2 4 - 500 3.60 3 9 - 580 3.12 3 9 - 600 2.82 4 4 - 620 3.99 3 4 - 700 4.00 3 4 - 480 4.00 2 4 - 560 2.95 2 4 - 560 4.00 3 4 - 560 2.65 3 9 - 400 3.08 2 4 - 480 2.62 2 9 - 640 3.86 3 4 - 480 3.57 2 4 - 540 3.51 2 4 - 380 3.33 4 4 - 680 3.64 3 4 - 400 3.51 3 4 - 340 2.90 1 4 - 700 3.08 2 4 - 480 3.02 1 9 - 600 3.15 2 9 - 780 3.80 3 9 - 520 3.74 2 9 - 520 3.51 2 4 - 640 3.73 3 4 - 560 3.32 4 4 - 620 2.85 2 4 - 700 3.28 1 4 - 760 4.00 1 9 - 800 3.60 2 4 - 580 3.34 2 4 - 540 3.77 2 9 - 640 3.17 2 4 - 540 3.02 4 4 - 680 3.08 4 4 - 680 3.31 2 4 - 680 2.96 3 9 - 700 2.88 2 4 - 580 3.77 4 4 - 540 3.49 2 9 - 700 3.56 2 9 - 600 3.56 2 9 - 560 3.59 2 4 - 640 2.94 2 9 - 560 3.33 4 4 - 620 3.69 3 4 - 680 3.27 2 9 - 460 3.14 3 4 - 500 3.53 4 4 - 620 3.33 3 4 - 600 3.62 3 4 - 500 3.01 4 4 - 740 3.34 4 4 - 560 3.69 3 9 - 620 3.95 3 9 - 740 3.86 2 9 - 800 3.53 1 9 - 620 3.78 3 4 - 700 3.27 2 4 - 540 3.78 2 9 - 700 3.65 2 4 - 800 3.22 1 9 - 560 3.59 2 9 - 800 3.15 4 4 - 520 3.90 3 9 - 520 3.74 4 9 - 480 2.55 1 4 - 800 4.00 4 4 - 620 3.09 4 4 - 560 3.49 4 4 - 500 3.17 3 4 - 480 3.40 2 4 - 460 2.98 1 4 - 580 3.58 1 9 - 640 3.30 2 4 - 480 3.45 2 4 - 440 3.17 2 4 - 660 3.32 1 4 - 500 3.08 3 4 - 660 3.94 2 4 - 720 3.31 1 4 - 460 3.64 3 9 - 500 2.93 4 4 - 800 3.54 3 4 - 580 2.93 2 4 - 620 3.61 1 9 - 500 2.98 3 4 - 660 4.00 2 9 - 560 3.24 4 4 - 560 2.42 2 4 - 580 3.80 2 4 - 500 3.23 4 4 - 680 2.42 1 9 - 580 3.46 3 4 - 800 3.91 3 4 - 700 2.90 4 4 - 520 3.12 2 4 - 300 2.92 4 4 - 560 3.43 3 4 - 620 3.63 3 4 - 500 2.79 4 4 - 360 3.14 1 4 - 640 3.94 2 9 - 460 3.99 3 9 - 300 3.01 3 4 - 520 2.73 2 4 - 600 3.47 2 9 - 580 3.25 1 4 - 520 3.10 4 4 - 620 3.43 3 4 - 380 2.91 4 4 - 660 3.59 3 4 - 660 3.95 2 9 - 540 3.33 3 4 - 740 4.00 3 4 - 640 3.38 3 4 - 600 3.89 3 4 - 720 3.88 3 4 - 580 4.00 3 4 - 420 2.26 4 4 - 520 4.00 2 9 - 800 3.70 1 9 - 700 4.00 1 9 - 480 3.43 2 4 - 660 3.45 4 4 - 520 3.25 3 4 - 560 2.71 3 4 - 600 3.32 2 4 - 580 2.88 2 4 - 660 3.88 2 9 - 600 3.22 1 4 - 580 4.00 1 4 - 660 3.60 3 9 - 500 3.35 2 4 - 520 2.98 2 4 - 660 3.49 2 9 - 560 3.07 2 4 - 500 3.13 2 9 - 720 3.50 3 9 - 440 3.39 2 9 - 640 3.95 2 9 - 380 3.61 3 4 - 800 3.05 2 9 - 520 3.19 3 9 - 600 3.40 3 4 + 620 3.07 2 4 + 800 4.00 3 9 + 580 3.40 2 4 + 600 3.13 2 4 + 540 2.70 2 4 + 660 3.31 4 4 + 480 3.58 1 9 + 620 4.00 1 9 + 680 3.98 2 9 + 580 3.40 4 4 + 760 3.35 3 4 + 700 3.72 2 4 + 460 3.64 1 9 + 540 3.28 3 4 + 680 3.48 3 4 + 740 3.31 1 4 + 460 3.77 3 4 + 740 3.54 1 4 + 600 3.63 3 4 + 620 3.05 2 4 + 560 3.04 3 4 + 520 2.70 3 4 + 640 3.35 3 4 + 620 3.58 2 4 + 660 3.70 4 9 + 500 2.86 4 4 + 640 3.50 2 4 + 720 4.00 3 4 + 720 3.94 3 4 + 400 3.65 2 4 + 800 2.90 2 4 + 520 2.90 3 4 + 440 3.24 4 4 + 580 3.51 2 4 + 500 3.31 3 4 + 440 3.22 1 4 + 540 3.17 1 9 + 420 3.02 1 4 + 780 3.22 2 9 + 440 3.13 4 4 + 800 3.66 1 9 + 580 3.32 2 9 + 480 2.67 2 9 + 700 4.00 1 9 + 740 2.97 2 9 + 700 3.83 2 4 + 640 3.93 2 4 + 800 3.90 2 4 + 400 3.38 2 4 + 700 3.52 2 4 + 680 3.00 4 9 + 540 3.20 1 4 + 580 4.00 2 4 + 780 4.00 2 9 + 220 2.83 3 4 + 580 3.20 2 9 + 580 3.50 2 4 + 620 3.30 1 4 + 520 3.65 4 9 + 600 3.38 3 9 + 660 3.77 3 4 + 580 2.86 4 9 + 580 3.46 2 9 + 560 3.36 3 4 + 740 4.00 3 9 + 480 3.44 3 4 + 640 3.19 4 9 + 600 3.54 1 9 + 540 3.38 4 4 + 500 2.81 3 4 + 360 2.56 3 4 + 460 3.15 4 4 + 460 2.63 2 4 + 440 2.76 2 4 + 740 3.62 4 4 + 380 3.38 2 4 + 640 3.63 1 9 + 800 3.73 1 4 + 660 3.67 2 4 + 760 3.00 2 9 + 420 2.96 1 4 + 740 3.74 4 4 + 800 3.75 2 4 + 620 3.40 2 4 + 660 3.67 3 9 + 400 3.35 3 4 + 680 3.14 2 4 + 660 3.47 3 9 + 660 3.63 2 9 + 420 3.41 4 4 + 660 4.00 1 4 + 680 3.70 2 4 + 620 3.23 3 9 + 520 3.35 3 4 + 500 4.00 3 4 + 400 3.36 2 4 + 700 3.56 1 9 + 540 3.81 1 9 + 520 2.68 3 9 + 540 3.50 2 4 + 700 4.00 2 4 + 600 3.64 3 9 + 800 3.31 3 4 + 520 3.29 1 4 + 580 3.69 1 4 + 380 3.43 3 4 + 560 3.19 3 4 + 760 2.81 1 9 + 540 3.13 2 4 + 660 3.14 2 9 + 520 3.81 1 9 + 680 3.19 4 4 + 540 3.78 4 4 + 500 3.57 3 4 + 660 3.49 2 4 + 340 3.00 2 9 + 400 3.15 2 9 + 420 3.92 4 4 + 760 3.35 2 9 + 700 2.94 2 4 + 540 3.04 1 4 + 780 3.87 4 4 + 560 3.78 2 4 + 700 3.82 3 4 + 400 2.93 3 4 + 440 3.45 2 9 + 800 3.47 3 4 + 340 3.15 3 4 + 520 4.00 1 9 + 520 3.15 3 4 + 600 2.98 2 9 + 420 2.69 2 4 + 460 3.44 2 4 + 620 3.71 1 9 + 480 3.13 2 4 + 580 3.40 3 4 + 540 3.39 3 9 + 540 3.94 3 4 + 440 2.98 3 4 + 380 3.59 4 4 + 500 2.97 4 4 + 340 2.92 3 4 + 440 3.15 2 4 + 600 3.48 2 4 + 420 2.67 3 4 + 460 3.07 2 4 + 460 3.45 3 9 + 480 3.39 4 4 + 480 2.78 3 4 + 720 3.42 2 9 + 680 3.67 2 9 + 800 3.89 2 4 + 360 3.00 3 4 + 620 3.17 2 9 + 700 3.52 4 9 + 540 3.19 2 4 + 580 3.30 2 4 + 800 4.00 3 9 + 660 3.33 2 4 + 380 3.34 3 4 + 720 3.84 3 4 + 600 3.59 2 4 + 500 3.03 3 4 + 640 3.81 2 4 + 540 3.49 1 9 + 680 3.85 3 9 + 540 3.84 2 9 + 460 2.93 3 4 + 380 2.94 3 4 + 620 3.22 2 4 + 740 3.37 4 4 + 620 4.00 2 4 + 800 3.74 1 9 + 400 3.31 3 4 + 540 3.46 4 4 + 620 3.18 2 9 + 480 2.91 1 9 + 300 2.84 2 9 + 440 2.48 4 4 + 640 2.79 2 4 + 400 3.23 4 9 + 680 3.46 2 9 + 620 3.37 1 9 + 700 3.92 2 4 + 620 3.37 2 9 + 620 3.63 2 4 + 620 3.95 3 9 + 560 2.52 2 4 + 520 2.62 2 4 + 600 3.35 2 4 + 700 4.00 1 4 + 640 3.67 3 4 + 640 4.00 3 4 + 520 2.93 4 4 + 620 3.21 4 4 + 680 3.99 3 4 + 660 3.34 3 4 + 700 3.45 3 4 + 560 3.36 1 9 + 800 2.78 2 4 + 500 3.88 4 4 + 700 3.65 2 4 + 680 3.76 3 9 + 660 3.07 3 4 + 580 3.46 4 4 + 460 2.87 2 4 + 600 3.31 4 4 + 620 3.94 4 4 + 400 3.05 2 4 + 800 3.43 2 9 + 600 3.58 1 9 + 580 3.36 2 4 + 540 3.16 3 4 + 500 2.71 2 4 + 600 3.28 3 4 + 600 2.82 4 4 + 460 3.58 2 4 + 520 2.85 3 4 + 740 3.52 4 9 + 500 3.95 4 4 + 560 3.61 3 4 + 620 3.45 2 9 + 640 3.51 2 4 + 660 3.44 2 9 + 660 2.91 3 9 + 540 3.28 1 4 + 560 2.98 1 9 + 800 3.97 1 4 + 720 3.77 3 4 + 720 3.64 1 9 + 480 3.71 4 9 + 680 3.34 2 4 + 680 3.11 2 4 + 540 2.81 3 4 + 620 3.75 2 9 + 540 3.12 1 4 + 560 3.48 2 9 + 720 3.40 3 4 + 680 3.90 1 4 + 640 3.76 3 4 + 560 3.16 1 4 + 520 3.30 2 9 + 640 3.12 3 4 + 580 3.57 3 4 + 540 3.55 4 9 + 780 3.63 4 9 + 600 3.89 1 9 + 800 4.00 1 9 + 580 3.29 4 4 + 360 3.27 3 4 + 800 4.00 2 9 + 640 3.52 4 4 + 720 3.45 4 4 + 580 3.06 2 4 + 580 3.02 2 4 + 500 3.60 3 9 + 580 3.12 3 9 + 600 2.82 4 4 + 620 3.99 3 4 + 700 4.00 3 4 + 480 4.00 2 4 + 560 2.95 2 4 + 560 4.00 3 4 + 560 2.65 3 9 + 400 3.08 2 4 + 480 2.62 2 9 + 640 3.86 3 4 + 480 3.57 2 4 + 540 3.51 2 4 + 380 3.33 4 4 + 680 3.64 3 4 + 400 3.51 3 4 + 340 2.90 1 4 + 700 3.08 2 4 + 480 3.02 1 9 + 600 3.15 2 9 + 780 3.80 3 9 + 520 3.74 2 9 + 520 3.51 2 4 + 640 3.73 3 4 + 560 3.32 4 4 + 620 2.85 2 4 + 700 3.28 1 4 + 760 4.00 1 9 + 800 3.60 2 4 + 580 3.34 2 4 + 540 3.77 2 9 + 640 3.17 2 4 + 540 3.02 4 4 + 680 3.08 4 4 + 680 3.31 2 4 + 680 2.96 3 9 + 700 2.88 2 4 + 580 3.77 4 4 + 540 3.49 2 9 + 700 3.56 2 9 + 600 3.56 2 9 + 560 3.59 2 4 + 640 2.94 2 9 + 560 3.33 4 4 + 620 3.69 3 4 + 680 3.27 2 9 + 460 3.14 3 4 + 500 3.53 4 4 + 620 3.33 3 4 + 600 3.62 3 4 + 500 3.01 4 4 + 740 3.34 4 4 + 560 3.69 3 9 + 620 3.95 3 9 + 740 3.86 2 9 + 800 3.53 1 9 + 620 3.78 3 4 + 700 3.27 2 4 + 540 3.78 2 9 + 700 3.65 2 4 + 800 3.22 1 9 + 560 3.59 2 9 + 800 3.15 4 4 + 520 3.90 3 9 + 520 3.74 4 9 + 480 2.55 1 4 + 800 4.00 4 4 + 620 3.09 4 4 + 560 3.49 4 4 + 500 3.17 3 4 + 480 3.40 2 4 + 460 2.98 1 4 + 580 3.58 1 9 + 640 3.30 2 4 + 480 3.45 2 4 + 440 3.17 2 4 + 660 3.32 1 4 + 500 3.08 3 4 + 660 3.94 2 4 + 720 3.31 1 4 + 460 3.64 3 9 + 500 2.93 4 4 + 800 3.54 3 4 + 580 2.93 2 4 + 620 3.61 1 9 + 500 2.98 3 4 + 660 4.00 2 9 + 560 3.24 4 4 + 560 2.42 2 4 + 580 3.80 2 4 + 500 3.23 4 4 + 680 2.42 1 9 + 580 3.46 3 4 + 800 3.91 3 4 + 700 2.90 4 4 + 520 3.12 2 4 + 300 2.92 4 4 + 560 3.43 3 4 + 620 3.63 3 4 + 500 2.79 4 4 + 360 3.14 1 4 + 640 3.94 2 9 + 460 3.99 3 9 + 300 3.01 3 4 + 520 2.73 2 4 + 600 3.47 2 9 + 580 3.25 1 4 + 520 3.10 4 4 + 620 3.43 3 4 + 380 2.91 4 4 + 660 3.59 3 4 + 660 3.95 2 9 + 540 3.33 3 4 + 740 4.00 3 4 + 640 3.38 3 4 + 600 3.89 3 4 + 720 3.88 3 4 + 580 4.00 3 4 + 420 2.26 4 4 + 520 4.00 2 9 + 800 3.70 1 9 + 700 4.00 1 9 + 480 3.43 2 4 + 660 3.45 4 4 + 520 3.25 3 4 + 560 2.71 3 4 + 600 3.32 2 4 + 580 2.88 2 4 + 660 3.88 2 9 + 600 3.22 1 4 + 580 4.00 1 4 + 660 3.60 3 9 + 500 3.35 2 4 + 520 2.98 2 4 + 660 3.49 2 9 + 560 3.07 2 4 + 500 3.13 2 9 + 720 3.50 3 9 + 440 3.39 2 9 + 640 3.95 2 9 + 380 3.61 3 4 + 800 3.05 2 9 + 520 3.19 3 9 + 600 3.40 3 4 ]) AT_DATA([lr-cat.sps], [dnl @@ -782,9 +782,9 @@ bcat,1.000,61,1,0,0 ,4.000,67,0,0,0 Table: Classification Table -,,,Predicted,, +,Observed,,Predicted,, ,,,y,,Percentage Correct -,Observed,,4.000,9.000, +,,,4.000,9.000, Step 1,y,4.000,254,19,93.0% ,,9.000,97,30,23.6% ,Overall Percentage,,,,71.0% @@ -808,210 +808,210 @@ AT_SETUP([LOGISTIC REGRESSION with cat var 2]) AT_KEYWORDS([categorical categoricals]) AT_DATA([lr-cat2.data], [dnl - 60.00 1.00 8.00 50.00 - 47.00 .00 9.00 42.00 - 57.00 1.00 7.00 53.00 - 60.00 .00 8.00 53.00 - 68.00 .00 8.00 66.00 - 63.00 .00 8.00 55.00 - 65.00 .00 8.00 63.00 - 52.00 .00 8.00 61.00 - 34.00 .00 9.00 42.00 - 37.00 .00 8.00 39.00 - 68.00 1.00 9.00 69.00 - 60.00 .00 9.00 61.00 - 44.00 .00 9.00 58.00 - 42.00 .00 8.00 47.00 - 57.00 1.00 7.00 61.00 - 55.00 1.00 8.00 50.00 - 55.00 .00 9.00 58.00 - 44.00 .00 8.00 63.00 - 50.00 1.00 9.00 66.00 - 44.00 .00 8.00 39.00 - 55.00 .00 8.00 58.00 - 44.00 .00 8.00 50.00 - 47.00 1.00 7.00 34.00 - 48.00 .00 8.00 44.00 - 45.00 .00 7.00 31.00 - 43.00 .00 8.00 50.00 - 39.00 .00 8.00 42.00 - 63.00 .00 9.00 50.00 - 47.00 .00 8.00 58.00 - 42.00 .00 7.00 50.00 - 50.00 .00 9.00 36.00 - 47.00 .00 7.00 33.00 - 60.00 .00 9.00 61.00 - 47.00 .00 7.00 42.00 - 68.00 1.00 9.00 69.00 - 52.00 .00 8.00 54.00 - 63.00 1.00 9.00 61.00 - 65.00 1.00 9.00 61.00 - 63.00 1.00 9.00 53.00 - 57.00 .00 8.00 51.00 - 34.00 .00 8.00 36.00 - 50.00 .00 8.00 39.00 - 52.00 1.00 7.00 56.00 - 45.00 .00 7.00 34.00 - 47.00 1.00 7.00 53.00 - 34.00 .00 7.00 39.00 - 50.00 1.00 8.00 55.00 - 60.00 .00 9.00 58.00 - 63.00 .00 8.00 58.00 - 35.00 .00 7.00 51.00 - 50.00 .00 8.00 58.00 - 68.00 .00 8.00 63.00 - 41.00 .00 9.00 34.00 - 47.00 .00 8.00 47.00 - 76.00 .00 9.00 64.00 - 44.00 .00 8.00 44.00 - 36.00 .00 9.00 50.00 - 68.00 1.00 9.00 55.00 - 47.00 1.00 8.00 50.00 - 50.00 .00 7.00 53.00 - 68.00 .00 8.00 74.00 - 39.00 .00 7.00 44.00 - 50.00 .00 8.00 55.00 - 52.00 .00 9.00 61.00 - 47.00 .00 8.00 53.00 - 39.00 .00 7.00 47.00 - 55.00 1.00 9.00 49.00 - 68.00 1.00 8.00 50.00 - 52.00 1.00 9.00 63.00 - 55.00 .00 8.00 58.00 - 57.00 .00 8.00 55.00 - 66.00 1.00 9.00 61.00 - 65.00 1.00 7.00 58.00 - 42.00 .00 7.00 42.00 - 68.00 1.00 7.00 59.00 - 60.00 1.00 9.00 61.00 - 52.00 .00 8.00 55.00 - 57.00 1.00 7.00 54.00 - 42.00 .00 9.00 50.00 - 42.00 .00 8.00 47.00 - 57.00 .00 8.00 50.00 - 47.00 .00 7.00 45.00 - 44.00 .00 7.00 40.00 - 43.00 .00 9.00 55.00 - 31.00 .00 8.00 39.00 - 37.00 .00 7.00 33.00 - 63.00 1.00 7.00 63.00 - 47.00 .00 8.00 39.00 - 57.00 1.00 8.00 63.00 - 52.00 .00 8.00 44.00 - 44.00 .00 7.00 35.00 - 52.00 .00 7.00 55.00 - 55.00 .00 7.00 69.00 - 52.00 .00 8.00 53.00 - 55.00 .00 9.00 61.00 - 65.00 1.00 9.00 63.00 - 55.00 .00 8.00 44.00 - 63.00 .00 7.00 65.00 - 44.00 .00 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55.00 1.00 9.00 63.00 + 42.00 .00 7.00 50.00 + 50.00 .00 8.00 44.00 + 55.00 .00 8.00 42.00 + 42.00 .00 7.00 50.00 + 34.00 .00 8.00 39.00 + 65.00 .00 9.00 46.00 + 52.00 .00 7.00 58.00 + 44.00 .00 8.00 39.00 + 65.00 1.00 9.00 66.00 + 47.00 .00 8.00 42.00 + 41.00 .00 7.00 39.00 + 68.00 .00 9.00 63.00 + 63.00 1.00 8.00 72.00 + 52.00 .00 8.00 53.00 + 57.00 .00 8.00 50.00 + 68.00 .00 8.00 55.00 + 42.00 .00 8.00 56.00 + 47.00 .00 8.00 48.00 + 73.00 1.00 9.00 58.00 + 39.00 .00 8.00 50.00 + 63.00 1.00 9.00 69.00 + 60.00 .00 8.00 55.00 + 65.00 1.00 9.00 66.00 + 73.00 1.00 8.00 63.00 + 52.00 .00 8.00 55.00 + 36.00 .00 8.00 42.00 + 28.00 .00 7.00 44.00 + 47.00 .00 8.00 44.00 + 57.00 .00 7.00 47.00 + 34.00 .00 7.00 29.00 + 47.00 .00 9.00 66.00 + 57.00 .00 8.00 58.00 + 60.00 1.00 9.00 50.00 + 50.00 .00 9.00 47.00 + 73.00 1.00 9.00 55.00 + 52.00 1.00 8.00 47.00 + 55.00 .00 8.00 53.00 + 47.00 .00 8.00 53.00 + 50.00 .00 8.00 61.00 + 61.00 .00 7.00 44.00 + 52.00 .00 9.00 53.00 + 47.00 .00 7.00 40.00 + 47.00 .00 7.00 50.00 ]) AT_DATA([stringcat.sps], [dnl -set format=F20.3. +set format=F20.3 /small=0. data list notable file='lr-cat2.data' list /read honcomp wiz science *. string ses(a1). @@ -1048,9 +1048,9 @@ ses,a,47,1,0 ,c,58,0,0 Table: Classification Table -,,,Predicted,, +,Observed,,Predicted,, ,,,honcomp,,Percentage Correct -,Observed,,.000,1.000, +,,,.000,1.000, Step 1,honcomp,.000,132,15,89.8% ,,1.000,26,27,50.9% ,Overall Percentage,,,,79.5% @@ -1095,106 +1095,106 @@ AT_SETUP([LOGISTIC REGRESSION missing categoricals]) AT_KEYWORDS([categorical categoricals]) AT_DATA([data.txt], [dnl - .00 3.69 .00 - .00 1.16 1.00 - 1.00 -12.99 .00 - .00 2.97 1.00 - .00 20.48 .00 - .00 4.90 .00 - 1.00 -4.38 .00 - .00 -1.69 1.00 - 1.00 -5.71 .00 - 1.00 -14.28 .00 - .00 9.00 .00 - .00 2.89 1.00 - .00 13.51 1.00 - .00 23.32 1.00 - .00 2.31 1.00 - .00 -2.07 1.00 - 1.00 -4.52 1.00 - 1.00 -5.83 .00 - 1.00 -1.91 .00 - 1.00 -11.12 1.00 - .00 -1.51 .00 - .00 6.59 1.00 - .00 19.28 1.00 - .00 5.94 .00 - 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1.00 + 1.00 -6.24 1.00 + .00 1.25 1.00 + .00 4.39 1.00 + .00 3.17 .00 + .00 19.39 1.00 + .00 13.03 1.00 + .00 2.43 .00 + 1.00 -14.73 1.00 + .00 8.25 1.00 + 1.00 -13.28 1.00 + .00 5.27 1.00 + 1.00 -3.46 1.00 + .00 13.81 1.00 + .00 1.35 1.00 + 1.00 -3.94 1.00 + .00 20.73 1.00 + 1.00 -15.40 .00 + 1.00 -11.01 1.00 + .00 4.56 .00 + 1.00 -15.35 1.00 + .00 15.21 .00 + .00 5.34 1.00 + 1.00 -21.55 1.00 + .00 10.12 1.00 + .00 -.73 1.00 + .00 15.28 1.00 + .00 11.08 1.00 + 1.00 -8.24 .00 + .00 2.46 .00 + .00 9.60 .00 + .00 11.24 .00 + .00 14.13 1.00 + .00 19.72 1.00 + .00 5.58 .00 + .00 26.23 1.00 + .00 7.25 .00 + 1.00 -.79 .00 + .00 6.24 .00 + 1.00 1.16 .00 + 1.00 -7.89 1.00 + 1.00 -1.86 1.00 + 1.00 -10.80 1.00 + 1.00 -5.51 .00 + .00 7.51 .00 + .00 11.18 .00 + .00 8.73 .00 + 1.00 -11.21 1.00 + 1.00 -13.24 .00 + .00 19.34 .00 + .00 9.32 1.00 + .00 17.97 1.00 + 1.00 -1.56 1.00 + 1.00 -3.13 .00 + .00 3.98 .00 + .00 -1.21 1.00 + .00 2.37 .00 + 1.00 -18.03 1.00 ]) AT_DATA([miss.sps], [dnl @@ -1236,205 +1236,205 @@ AT_DATA([ci.sps], [dnl set FORMAT=F20.3 data list notable list /disease age sciostat sector savings *. begin data. -0 33 1 1 1 -0 35 1 1 1 -0 6 1 1 0 -0 60 1 1 1 -1 18 3 1 0 -0 26 3 1 0 -0 6 3 1 0 -1 31 2 1 1 -1 26 2 1 0 -0 37 2 1 0 -0 23 1 1 0 -0 23 1 1 0 -0 27 1 1 1 -1 9 1 1 1 -1 37 1 2 1 -1 22 1 2 1 -1 67 1 2 1 -0 8 1 2 1 -1 6 1 2 1 -1 15 1 2 1 -1 21 2 2 1 -1 32 2 2 1 -1 16 1 2 1 -0 11 2 2 0 -0 14 3 2 0 -0 9 2 2 0 -0 18 2 2 0 -0 2 3 1 0 -0 61 3 1 1 -0 20 3 1 0 -0 16 3 1 0 -0 9 2 1 0 -0 35 2 1 1 -0 4 1 1 1 -0 44 3 2 0 -1 11 3 2 0 -0 3 2 2 1 -0 6 3 2 0 -1 17 2 2 0 -0 1 3 2 1 -1 53 2 2 1 -1 13 1 2 0 -0 24 1 2 0 -1 70 1 2 1 -1 16 3 2 1 -0 12 2 2 1 -1 20 3 2 1 -0 65 3 2 1 -1 40 2 2 0 -1 38 2 2 1 -1 68 2 2 1 -1 74 1 2 1 -1 14 1 2 1 -1 27 1 2 1 -0 31 1 2 1 -0 18 1 2 1 -0 39 1 2 0 -0 50 1 2 1 -0 31 1 2 1 -0 61 1 2 1 -0 18 3 1 0 -0 5 3 1 0 -0 2 3 1 1 -0 16 3 1 0 -1 59 3 1 1 -0 22 3 1 0 -0 24 1 1 1 -0 30 1 1 1 -0 46 1 1 1 -0 28 1 1 0 -0 27 1 1 1 -1 27 1 1 0 -0 28 1 1 1 -1 52 1 1 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+0 15 2 2 1 +1 35 2 2 0 +0 2 2 2 1 +0 17 3 2 0 +1 43 3 2 1 +0 30 2 2 1 +0 11 1 2 1 +1 39 1 2 1 +0 32 1 2 1 +0 17 1 2 1 +0 3 3 2 1 +0 7 3 2 0 +0 2 2 2 0 +1 64 2 2 1 +1 13 1 2 2 +1 15 2 2 1 +0 48 2 2 1 +0 23 1 2 1 +1 48 1 2 0 +0 25 1 2 1 +0 12 1 2 1 +1 46 1 2 1 +0 79 1 2 1 +0 56 1 2 1 +0 8 1 2 1 +1 29 3 1 0 +1 35 3 1 0 +1 11 3 1 0 +0 69 3 1 1 +1 21 3 1 0 +0 13 3 1 0 +0 21 1 1 1 +1 32 1 1 1 +1 24 1 1 0 +0 24 1 1 1 +0 73 1 1 1 +0 42 1 1 1 +1 34 1 1 1 +0 30 2 1 0 +0 7 2 1 0 +1 29 3 1 0 +1 22 3 1 0 +0 38 2 1 1 +0 13 2 1 1 +0 12 2 1 1 +0 42 3 1 0 +1 17 3 1 0 +0 21 3 1 1 +0 34 1 1 1 +0 1 3 1 0 +0 14 2 1 0 +0 16 2 1 0 +0 9 3 1 0 +0 53 3 1 0 +0 27 3 1 0 +0 15 3 1 0 +0 9 3 1 0 +0 4 2 1 1 +0 10 3 1 1 +0 31 3 1 0 +0 85 3 1 1 +0 24 2 1 0 end data. -logistic regression +logistic regression disease WITH age sciostat sector savings /categorical = sciostat sector /print = ci(95). @@ -1468,9 +1468,9 @@ sector,1.000,117,1, ,2.000,79,0, Table: Classification Table -,,,Predicted,, +,Observed,,Predicted,, ,,,disease,,Percentage Correct -,Observed,,.000,1.000, +,,,.000,1.000, Step 1,disease,.000,131,8,94.2% ,,1.000,41,16,28.1% ,Overall Percentage,,,,75.0%