+
AT_BANNER([LOGISTIC REGRESSION])
dnl These examples are adapted from
error: Dependent variable's values are not dichotomous.
])
-AT_CLEANUP
\ No newline at end of file
+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
+AT_SETUP([LOGISTIC REGRESSION with categorical])
+
+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
+])
+
+AT_DATA([lr-cat.sps], [dnl
+set format=F20.3.
+
+data list notable list file='lr-cat.data' /b1 b2 bcat y.
+
+logistic regression
+ y with b1 b2 bcat
+ /categorical = bcat
+ .
+])
+
+AT_CHECK([pspp -O format=csv lr-cat.sps], [0],
+ [dnl
+Table: Dependent Variable Encoding
+Original Value,Internal Value
+4.000,0
+9.000,1
+
+Table: Case Processing Summary
+Unweighted Cases,N,Percent
+Included in Analysis,400,100.000
+Missing Cases,0,.000
+Total,400,100.000
+
+note: Estimation terminated at iteration number 4 because parameter estimates changed by less than 0.001
+
+Table: Model Summary
+Step 1,-2 Log likelihood,Cox & Snell R Square,Nagelkerke R Square
+,458.517,.098,.138
+
+Table: Categorical Variables' Codings
+,,,Parameter coding,,
+,,Frequency,(1),(2),(3)
+bcat,1.000,61,1,0,0
+,2.000,151,0,1,0
+,3.000,121,0,0,1
+,4.000,67,0,0,0
+
+Table: Variables in the Equation
+,,B,S.E.,Wald,df,Sig.,Exp(B)
+Step 1,b1,.002,.001,4.284,1,.038,1.002
+,b2,.804,.332,5.872,1,.015,2.235
+,bcat,,,20.895,3,.000,
+,bcat(1),1.551,.418,13.788,1,.000,4.718
+,bcat(2),.876,.367,5.706,1,.017,2.401
+,bcat(3),.211,.393,.289,1,.591,1.235
+,Constant,-5.541,1.138,23.709,1,.000,.004
+])
+
+AT_CLEANUP
+
+
+
+dnl This example is inspired by http://www.ats.ucla.edu/stat/spss/output/logistic.htm
+AT_SETUP([LOGISTIC REGRESSION with cat var 2])
+
+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 7.00 39.00
+ 47.00 .00 7.00 36.00
+ 63.00 1.00 9.00 55.00
+ 68.00 .00 8.00 66.00
+ 34.00 .00 8.00 39.00
+ 47.00 .00 9.00 50.00
+ 50.00 .00 9.00 58.00
+ 63.00 .00 8.00 66.00
+ 44.00 .00 7.00 34.00
+ 44.00 .00 8.00 50.00
+ 50.00 .00 8.00 53.00
+ 47.00 1.00 9.00 69.00
+ 65.00 .00 9.00 58.00
+ 57.00 .00 8.00 47.00
+ 39.00 .00 8.00 39.00
+ 47.00 .00 8.00 53.00
+ 50.00 1.00 7.00 63.00
+ 50.00 .00 8.00 50.00
+ 63.00 .00 9.00 53.00
+ 73.00 1.00 9.00 61.00
+ 44.00 .00 7.00 47.00
+ 47.00 .00 8.00 42.00
+ 47.00 .00 8.00 58.00
+ 36.00 .00 7.00 61.00
+ 57.00 1.00 8.00 55.00
+ 53.00 1.00 8.00 57.00
+ 63.00 .00 7.00 66.00
+ 50.00 .00 8.00 34.00
+ 47.00 .00 9.00 48.00
+ 57.00 1.00 8.00 58.00
+ 39.00 .00 8.00 53.00
+ 42.00 .00 8.00 42.00
+ 42.00 .00 9.00 31.00
+ 42.00 .00 8.00 72.00
+ 46.00 .00 8.00 44.00
+ 55.00 .00 8.00 42.00
+ 42.00 .00 8.00 47.00
+ 50.00 .00 8.00 44.00
+ 44.00 .00 9.00 39.00
+ 73.00 1.00 8.00 69.00
+ 71.00 1.00 9.00 58.00
+ 50.00 .00 9.00 49.00
+ 63.00 1.00 7.00 54.00
+ 42.00 .00 8.00 36.00
+ 47.00 .00 7.00 42.00
+ 39.00 .00 9.00 26.00
+ 63.00 .00 8.00 58.00
+ 50.00 .00 8.00 55.00
+ 65.00 1.00 8.00 55.00
+ 76.00 1.00 9.00 67.00
+ 71.00 1.00 8.00 66.00
+ 39.00 .00 9.00 47.00
+ 47.00 1.00 9.00 63.00
+ 60.00 .00 7.00 50.00
+ 63.00 .00 9.00 55.00
+ 54.00 1.00 9.00 55.00
+ 55.00 1.00 8.00 58.00
+ 57.00 .00 8.00 61.00
+ 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.
+data list notable file='lr-cat2.data' list /read honcomp wiz science *.
+
+string ses(a1).
+recode wiz (7 = "a") (8 = "b") (9 = "c") into ses.
+
+logistic regression honcomp with read science ses
+ /categorical = ses.
+
+])
+
+AT_CHECK([pspp -O format=csv stringcat.sps], [0],
+ [dnl
+Table: Dependent Variable Encoding
+Original Value,Internal Value
+.000,0
+1.000,1
+
+Table: Case Processing Summary
+Unweighted Cases,N,Percent
+Included in Analysis,200,100.000
+Missing Cases,0,.000
+Total,200,100.000
+
+note: Estimation terminated at iteration number 5 because parameter estimates changed by less than 0.001
+
+Table: Model Summary
+Step 1,-2 Log likelihood,Cox & Snell R Square,Nagelkerke R Square
+,165.701,.280,.408
+
+Table: Categorical Variables' Codings
+,,,Parameter coding,
+,,Frequency,(1),(2)
+ses,a,47,1,0
+,b,95,0,1
+,c,58,0,0
+
+Table: Variables in the Equation
+,,B,S.E.,Wald,df,Sig.,Exp(B)
+Step 1,read,.098,.025,15.199,1,.000,1.103
+,science,.066,.027,5.867,1,.015,1.068
+,ses,,,6.690,2,.035,
+,ses(1),.058,.532,.012,1,.913,1.060
+,ses(2),-1.013,.444,5.212,1,.022,.363
+,Constant,-9.561,1.662,33.113,1,.000,.000
+])
+
+AT_CLEANUP
projects / pspp / blobdiff