SPSS Textbook Examples
Regression with Graphics by Lawrence Hamilton
Chapter 7: Logit regression
Limitations of linear regression
Page 218 Figure 7.1 Linear regression of a dichotomous Y
variable (0 = open schools, 1 = close schools) on a measurement X variable (years lived in
town).
GET FILE 'd:\apps\rwgdata\toxic.sav'.
formats lived (f2.0) close (f2.1).
GGRAPH
/GRAPHDATASET NAME="graphdataset" VARIABLES=lived close
/GRAPHSPEC SOURCE=INLINE.
BEGIN GPL
SOURCE: s=userSource(id("graphdataset"))
DATA: lived=col(source(s), name("lived"))
DATA: close=col(source(s), name("close"))
GUIDE: text.title( label( "Figure 7.1" ) )
GUIDE: form.line(position(*, 1), shape(shape.half_dash))
GUIDE: form.line(position(*, 0), shape(shape.half_dash))
GUIDE: axis(dim(1), label("Years Lived in Town"), delta(10))
GUIDE: axis(dim(2), label("Favor Closing Schools"), delta(.2))
SCALE: linear(dim(1), min(0), max(80))
SCALE: linear(dim(2), min(-.2), max(1))
ELEMENT: point(position(lived*close))
ELEMENT: line(position(smooth.linear(lived*close)), shape(shape.dash))
END GPL.
Page 219 Figure 7.2 Boxplots and oneway scatterplots of years
lived in town, for respondents favoring closed and open schools.
compute const=.01.
execute.
EXAMINE VARIABLES=lived BY close
/PLOT=BOXPLOT
/STATISTICS=NONE.
Case Processing Summary
|
Cases |
| Valid |
Missing |
Total |
| N |
Percent |
N |
Percent |
N |
Percent |
| years lived in Williamstown |
153 |
100.0% |
0 |
.0% |
153 |
100.0% |
Case Processing Summary
|
Cases |
| Valid |
Missing |
Total |
|
schools should close |
N |
Percent |
N |
Percent |
N |
Percent |
| years lived in
Williamstown |
open |
87 |
100.0% |
0 |
.0% |
87 |
100.0% |
| close |
66 |
100.0% |
0 |
.0% |
66 |
100.0% |
Page 222 Figure 7.4 Logit regression of school-closing opinion
on years lived in town, also showing linear regression line.
GGRAPH
/GRAPHDATASET NAME="graphdataset" VARIABLES=lived close
/GRAPHSPEC SOURCE=INLINE.
BEGIN GPL
SOURCE: s=userSource(id("graphdataset"))
DATA: lived=col(source(s), name("lived"))
DATA: close=col(source(s), name("close"))
GUIDE: text.title( label( "Figure 7.4" ) )
GUIDE: form.line(position(*, 1), shape(shape.half_dash))
GUIDE: form.line(position(*, 0), shape(shape.half_dash))
GUIDE: axis(dim(1), label("Years Lived in Town"), delta(10))
GUIDE: axis(dim(2), label("Favor Closing Schools"), delta(.2))
SCALE: linear(dim(1), min(0), max(80))
SCALE: linear(dim(2), min(-.2), max(1))
ELEMENT: point(position(lived*close))
ELEMENT: line(position(smooth.linear(lived*close)), shape(shape.dash))
ELEMENT: line(position(smooth.quadratic(lived*close)))
END GPL.
Page 224 Table 7.1 Logit regression of school-closing opinion on years lived in
town.
LOGISTIC REGRESSION VAR=close
/METHOD=ENTER lived.
Case Processing Summary
| Unweighted Cases(a) |
N |
Percent |
| Selected Cases |
Included in Analysis |
153 |
100.0 |
| Missing Cases |
0 |
.0 |
| Total |
153 |
100.0 |
| Unselected Cases |
0 |
.0 |
| Total |
153 |
100.0 |
| a If weight is in
effect, see classification table for the total number of cases. |
Dependent Variable Encoding
| Original Value |
Internal Value |
| open |
0 |
| close |
1 |
Classification Table(a,b)
|
Predicted |
| schools should close |
Percentage Correct |
|
Observed |
open |
close |
|
| Step 0 |
schools should close |
open |
87 |
0 |
100.0 |
| close |
66 |
0 |
.0 |
| Overall Percentage |
|
|
56.9 |
| a Constant is
included in the model. |
| b The cut value is
.500 |
Variables in the Equation
|
B |
S.E. |
Wald |
df |
Sig. |
Exp(B) |
| Step 0 |
Constant |
-.276 |
.163 |
2.864 |
1 |
.091 |
.759 |
Variables not in the Equation
|
Score |
df |
Sig. |
| Step 0 |
Variables |
LIVED |
12.683 |
1 |
.000 |
| Overall Statistics |
12.683 |
1 |
.000 |
Omnibus Tests of Model Coefficients
|
Chi-square |
df |
Sig. |
| Step 1 |
Step |
13.944 |
1 |
.000 |
| Block |
13.944 |
1 |
.000 |
| Model |
13.944 |
1 |
.000 |
Model Summary
| Step |
-2 Log likelihood |
Cox & Snell R Square |
Nagelkerke R Square |
| 1 |
195.267 |
.087 |
.117 |
Classification Table(a)
|
Predicted |
| schools should close |
Percentage Correct |
|
Observed |
open |
close |
|
| Step 1 |
schools should close |
open |
59 |
28 |
67.8 |
| close |
29 |
37 |
56.1 |
| Overall Percentage |
|
|
62.7 |
| a The cut value is
.500 |
Variables in the Equation
|
B |
S.E. |
Wald |
df |
Sig. |
Exp(B) |
| Step 1(a) |
LIVED |
-.041 |
.012 |
11.398 |
1 |
.001 |
.960 |
| Constant |
.460 |
.263 |
3.069 |
1 |
.080 |
1.584 |
| a Variable(s) entered
on step 1: LIVED. |
Page 226 Table 7.2 Logit regression of school-closing
opinion on years lived in town, education, contamination, and HSC meetings.
LOGISTIC REGRESSION VAR=close
/METHOD=ENTER lived educ contam hsc.
Case Processing Summary
| Unweighted Cases(a) |
N |
Percent |
| Selected Cases |
Included in Analysis |
153 |
100.0 |
| Missing Cases |
0 |
.0 |
| Total |
153 |
100.0 |
| Unselected Cases |
0 |
.0 |
| Total |
153 |
100.0 |
| a If weight is in
effect, see classification table for the total number of cases. |
Dependent Variable Encoding
| Original Value |
Internal Value |
| open |
0 |
| close |
1 |
Classification Table(a,b)
|
Predicted |
| schools should close |
Percentage Correct |
|
Observed |
open |
close |
|
| Step 0 |
schools should close |
open |
87 |
0 |
100.0 |
| close |
66 |
0 |
.0 |
| Overall Percentage |
|
|
56.9 |
| a Constant is
included in the model. |
| b The cut value is
.500 |
Variables in the Equation
|
B |
S.E. |
Wald |
df |
Sig. |
Exp(B) |
| Step 0 |
Constant |
-.276 |
.163 |
2.864 |
1 |
.091 |
.759 |
Variables not in the Equation
|
Score |
df |
Sig. |
| Step 0 |
Variables |
LIVED |
12.683 |
1 |
.000 |
| EDUC |
.221 |
1 |
.638 |
| CONTAM |
17.292 |
1 |
.000 |
| HSC |
39.337 |
1 |
.000 |
| Overall Statistics |
52.845 |
4 |
.000 |
Omnibus Tests of Model Coefficients
|
Chi-square |
df |
Sig. |
| Step 1 |
Step |
59.830 |
4 |
.000 |
| Block |
59.830 |
4 |
.000 |
| Model |
59.830 |
4 |
.000 |
Model Summary
| Step |
-2 Log likelihood |
Cox & Snell R Square |
Nagelkerke R Square |
| 1 |
149.382 |
.324 |
.434 |
Classification Table(a)
|
Predicted |
| schools should close |
Percentage Correct |
|
Observed |
open |
close |
|
| Step 1 |
schools should close |
open |
75 |
12 |
86.2 |
| close |
24 |
42 |
63.6 |
| Overall Percentage |
|
|
76.5 |
| a The cut value is
.500 |
Variables in the Equation
|
B |
S.E. |
Wald |
df |
Sig. |
Exp(B) |
| Step 1(a) |
LIVED |
-.046 |
.015 |
9.698 |
1 |
.002 |
.955 |
| EDUC |
-.166 |
.090 |
3.404 |
1 |
.065 |
.847 |
| CONTAM |
1.208 |
.465 |
6.739 |
1 |
.009 |
3.347 |
| HSC |
2.173 |
.464 |
21.919 |
1 |
.000 |
8.784 |
| Constant |
1.731 |
1.302 |
1.768 |
1 |
.184 |
5.649 |
| a Variable(s) entered
on step 1: LIVED, EDUC, CONTAM, HSC. |
Page 227 Table 7.3 Logit regression of school-closing
opinion on seven background variables.
LOGISTIC REGRESSION VAR=close
/METHOD=ENTER lived educ contam hsc female kids nodad
/PRINT=ITER(1) SUMMARY.
Case Processing Summary
| Unweighted Cases(a) |
N |
Percent |
| Selected Cases |
Included in Analysis |
153 |
100.0 |
| Missing Cases |
0 |
.0 |
| Total |
153 |
100.0 |
| Unselected Cases |
0 |
.0 |
| Total |
153 |
100.0 |
| a If weight is in
effect, see classification table for the total number of cases. |
Dependent Variable Encoding
| Original Value |
Internal Value |
| open |
0 |
| close |
1 |
Iteration History(a,b,c)
|
-2 Log likelihood |
Coefficients |
| Iteration |
|
Constant |
| Step 0 |
1 |
209.212 |
-.275 |
| 2 |
209.212 |
-.276 |
| a Constant is
included in the model. |
| b Initial -2 Log
Likelihood: 209.212 |
| c Estimation
terminated at iteration number 2 because log-likelihood decreased by less than .010
percent. |
Classification Table(a,b)
|
Predicted |
| schools should close |
Percentage Correct |
|
Observed |
open |
close |
|
| Step 0 |
schools should close |
open |
87 |
0 |
100.0 |
| close |
66 |
0 |
.0 |
| Overall Percentage |
|
|
56.9 |
| a Constant is
included in the model. |
| b The cut value is
.500 |
Variables in the Equation
|
B |
S.E. |
Wald |
df |
Sig. |
Exp(B) |
| Step 0 |
Constant |
-.276 |
.163 |
2.864 |
1 |
.091 |
.759 |
Variables not in the Equation
|
Score |
df |
Sig. |
| Step 0 |
Variables |
LIVED |
12.683 |
1 |
.000 |
| EDUC |
.221 |
1 |
.638 |
| CONTAM |
17.292 |
1 |
.000 |
| HSC |
39.337 |
1 |
.000 |
| FEMALE |
3.868 |
1 |
.049 |
| KIDS |
5.666 |
1 |
.017 |
| NODAD |
9.835 |
1 |
.002 |
| Overall Statistics |
57.038 |
7 |
.000 |
Iteration History(a,b,c,d)
|
-2 Log likelihood |
Coefficients |
| Iteration |
|
Constant |
LIVED |
EDUC |
CONTAM |
HSC |
FEMALE |
KIDS |
NODAD |
| Step 1 |
1 |
147.028 |
1.565 |
-.027 |
-.130 |
.782 |
1.764 |
-.015 |
-.365 |
-1.074 |
| 2 |
141.482 |
2.538 |
-.041 |
-.187 |
1.147 |
2.239 |
-.037 |
-.580 |
-1.844 |
| 3 |
141.054 |
2.859 |
-.046 |
-.204 |
1.269 |
2.401 |
-.050 |
-.662 |
-2.184 |
| 4 |
141.049 |
2.893 |
-.047 |
-.206 |
1.282 |
2.418 |
-.052 |
-.671 |
-2.225 |
| a Method: Enter |
| b Constant is
included in the model. |
| c Initial -2 Log
Likelihood: 209.212 |
| d Estimation
terminated at iteration number 4 because log-likelihood decreased by less than .010
percent. |
Omnibus Tests of Model Coefficients
|
Chi-square |
df |
Sig. |
| Step 1 |
Step |
68.162 |
7 |
.000 |
| Block |
68.162 |
7 |
.000 |
| Model |
68.162 |
7 |
.000 |
Model Summary
| Step |
-2 Log likelihood |
Cox & Snell R Square |
Nagelkerke R Square |
| 1 |
141.049 |
.359 |
.482 |
Classification Table(a)
|
Predicted |
| schools should close |
Percentage Correct |
|
Observed |
open |
close |
|
| Step 1 |
schools should close |
open |
77 |
10 |
88.5 |
| close |
25 |
41 |
62.1 |
| Overall Percentage |
|
|
77.1 |
| a The cut value is
.500 |
Variables in the Equation
|
B |
S.E. |
Wald |
df |
Sig. |
Exp(B) |
| Step 1(a) |
LIVED |
-.047 |
.017 |
7.549 |
1 |
.006 |
.954 |
| EDUC |
-.206 |
.093 |
4.886 |
1 |
.027 |
.814 |
| CONTAM |
1.282 |
.481 |
7.093 |
1 |
.008 |
3.604 |
| HSC |
2.418 |
.510 |
22.507 |
1 |
.000 |
11.221 |
| FEMALE |
-.052 |
.557 |
.009 |
1 |
.926 |
.950 |
| KIDS |
-.671 |
.566 |
1.405 |
1 |
.236 |
.511 |
| NODAD |
-2.225 |
.999 |
4.962 |
1 |
.026 |
.108 |
| Constant |
2.893 |
1.603 |
3.258 |
1 |
.071 |
18.054 |
| a Variable(s) entered
on step 1: LIVED, EDUC, CONTAM, HSC, FEMALE, KIDS, NODAD. |
Page 228 Table 7.4 Reduced model with male/nonparent
interaction term.
LOGISTIC REGRESSION VAR=close
/METHOD=ENTER lived educ contam hsc nodad.
Case Processing Summary
| Unweighted Cases(a) |
N |
Percent |
| Selected Cases |
Included in Analysis |
153 |
100.0 |
| Missing Cases |
0 |
.0 |
| Total |
153 |
100.0 |
| Unselected Cases |
0 |
.0 |
| Total |
153 |
100.0 |
| a If weight is in
effect, see classification table for the total number of cases. |
Dependent Variable Encoding
| Original Value |
Internal Value |
| open |
0 |
| close |
1 |
Classification Table(a,b)
|
Predicted |
| schools should close |
Percentage Correct |
|
Observed |
open |
close |
|
| Step 0 |
schools should close |
open |
87 |
0 |
100.0 |
| close |
66 |
0 |
.0 |
| Overall Percentage |
|
|
56.9 |
| a Constant is
included in the model. |
| b The cut value is
.500 |
Variables in the Equation
|
B |
S.E. |
Wald |
df |
Sig. |
Exp(B) |
| Step 0 |
Constant |
-.276 |
.163 |
2.864 |
1 |
.091 |
.759 |
Variables not in the Equation
|
Score |
df |
Sig. |
| Step 0 |
Variables |
LIVED |
12.683 |
1 |
.000 |
| EDUC |
.221 |
1 |
.638 |
| CONTAM |
17.292 |
1 |
.000 |
| HSC |
39.337 |
1 |
.000 |
| NODAD |
9.835 |
1 |
.002 |
| Overall Statistics |
56.279 |
5 |
.000 |
Omnibus Tests of Model Coefficients
|
Chi-square |
df |
Sig. |
| Step 1 |
Step |
66.559 |
5 |
.000 |
| Block |
66.559 |
5 |
.000 |
| Model |
66.559 |
5 |
.000 |
Model Summary
| Step |
-2 Log likelihood |
Cox & Snell R Square |
Nagelkerke R Square |
| 1 |
142.652 |
.353 |
.473 |
Classification Table(a)
|
Predicted |
| schools should close |
Percentage Correct |
|
Observed |
open |
close |
|
| Step 1 |
schools should close |
open |
76 |
11 |
87.4 |
| close |
25 |
41 |
62.1 |
| Overall Percentage |
|
|
76.5 |
| a The cut value is
.500 |
Variables in the Equation
|
B |
S.E. |
Wald |
df |
Sig. |
Exp(B) |
| Step 1(a) |
LIVED |
-.040 |
.015 |
6.559 |
1 |
.010 |
.961 |
| EDUC |
-.197 |
.093 |
4.509 |
1 |
.034 |
.821 |
| CONTAM |
1.298 |
.477 |
7.422 |
1 |
.006 |
3.664 |
| HSC |
2.278 |
.490 |
21.590 |
1 |
.000 |
9.762 |
| NODAD |
-1.731 |
.725 |
5.695 |
1 |
.017 |
.177 |
| Constant |
2.182 |
1.330 |
2.691 |
1 |
.101 |
8.865 |
| a Variable(s) entered
on step 1: LIVED, EDUC, CONTAM, HSC, NODAD. |
Page 232 Figure 7.5 Conditional effects of years lived in
town, at proclosing (top), average, and anticlosing levels of other X variables.
LOGISTIC REGRESSION VAR=close
/METHOD=ENTER lived educ contam hsc nodad.
Case Processing Summary
| Unweighted Cases(a) |
N |
Percent |
| Selected Cases |
Included in Analysis |
153 |
100.0 |
| Missing Cases |
0 |
.0 |
| Total |
153 |
100.0 |
| Unselected Cases |
0 |
.0 |
| Total |
153 |
100.0 |
| a If weight is in
effect, see classification table for the total number of cases. |
Dependent Variable Encoding
| Original Value |
Internal Value |
| open |
0 |
| close |
1 |
Classification Table(a,b)
|
Predicted |
| schools should close |
Percentage Correct |
|
Observed |
open |
close |
|
| Step 0 |
schools should close |
open |
87 |
0 |
100.0 |
| close |
66 |
0 |
.0 |
| Overall Percentage |
|
|
56.9 |
| a Constant is
included in the model. |
| b The cut value is
.500 |
Variables in the Equation
|
B |
S.E. |
Wald |
df |
Sig. |
Exp(B) |
| Step 0 |
Constant |
-.276 |
.163 |
2.864 |
1 |
.091 |
.759 |
Variables not in the Equation
|
Score |
df |
Sig. |
| Step 0 |
Variables |
LIVED |
12.683 |
1 |
.000 |
| EDUC |
.221 |
1 |
.638 |
| CONTAM |
17.292 |
1 |
.000 |
| HSC |
39.337 |
1 |
.000 |
| NODAD |
9.835 |
1 |
.002 |
| Overall Statistics |
56.279 |
5 |
.000 |
Omnibus Tests of Model Coefficients
|
Chi-square |
df |
Sig. |
| Step 1 |
Step |
66.559 |
5 |
.000 |
| Block |
66.559 |
5 |
.000 |
| Model |
66.559 |
5 |
.000 |
Model Summary
| Step |
-2 Log likelihood |
Cox & Snell R Square |
Nagelkerke R Square |
| 1 |
142.652 |
.353 |
.473 |
Classification Table(a)
|
Predicted |
| schools should close |
Percentage Correct |
|
Observed |
open |
close |
|
| Step 1 |
schools should close |
open |
76 |
11 |
87.4 |
| close |
25 |
41 |
62.1 |
| Overall Percentage |
|
|
76.5 |
| a The cut value is
.500 |
Variables in the Equation
|
B |
S.E. |
Wald |
df |
Sig. |
Exp(B) |
| Step 1(a) |
LIVED |
-.040 |
.015 |
6.559 |
1 |
.010 |
.961 |
| EDUC |
-.197 |
.093 |
4.509 |
1 |
.034 |
.821 |
| CONTAM |
1.298 |
.477 |
7.422 |
1 |
.006 |
3.664 |
| HSC |
2.278 |
.490 |
21.590 |
1 |
.000 |
9.762 |
| NODAD |
-1.731 |
.725 |
5.695 |
1 |
.017 |
.177 |
| Constant |
2.182 |
1.330 |
2.691 |
1 |
.101 |
8.865 |
| a Variable(s) entered
on step 1: LIVED, EDUC, CONTAM, HSC, NODAD. |
SORT CASES BY
lived (A).
compute lhat1 = 3.17-.04*lived.
compute phat1 = 1/(1+exp(-lhat1)).
compute lhat2 = .387-.04*(lived).
compute phat2 = 1/(1+exp(-lhat2)).
compute lhat3 = -2.14-.04*(lived).
compute phat3 = 1/(1+exp(-lhat3)).
execute.
formats lived (f2.0) phat1 phat2 phat3 (f2.1).
GGRAPH
/GRAPHDATASET NAME="graphdataset" VARIABLES=lived phat1 phat2 phat3
/GRAPHSPEC SOURCE=INLINE.
BEGIN GPL
SOURCE: s=userSource(id("graphdataset"))
DATA: lived=col(source(s), name("lived"))
DATA: phat1=col(source(s), name("phat1"))
DATA: phat2=col(source(s), name("phat2"))
DATA: phat3=col(source(s), name("phat3"))
GUIDE: text.title( label( "Figure 7.5" ) )
GUIDE: axis(dim(1), label("Years Lived in Town"), delta(10))
GUIDE: axis(dim(2), label("Probability of Favoring School Closing"), delta(.2))
SCALE: linear(dim(1), min(0), max(80))
SCALE: linear(dim(2), min(0), max(1))
ELEMENT: line(position(smooth.spline(lived*phat1)), shape(shape.dash))
ELEMENT: line(position(smooth.spline(lived*phat2)))
ELEMENT: line(position(smooth.spline(lived*phat3)), shape(shape.half_dash))
END GPL.
Page 232 Figure 7.6 Conditional effects of contamination, at
proclosing, average, and anticlosing levels of other X variables.
SORT CASES BY contam (A).
compute lhat4 = 3.22+1.3*(contam).
compute phat4 = 1/(1+exp(-lhat4)).
compute lhat5 = -.7681+1.3*(contam).
compute phat5 = 1/(1+exp(-lhat5)).
compute lhat6 = -6.79+1.3*(contam).
compute phat6 = 1/(1+exp(-lhat6)).
execute.
SORT CASES BY
contam (A).
value labels contam 0 "Not contaminated" 1 "Contaminated".
formats contam (f1.0) phat4 phat5 phat6 (f2.1).
GGRAPH
/GRAPHDATASET NAME="graphdataset" VARIABLES=contam phat4 phat5 phat6
/GRAPHSPEC SOURCE=INLINE.
BEGIN GPL
SOURCE: s=userSource(id("graphdataset"))
DATA: contam=col(source(s), name("contam"), unit.category() )
DATA: phat4=col(source(s), name("phat4"))
DATA: phat5=col(source(s), name("phat5"))
DATA: phat6=col(source(s), name("phat6"))
GUIDE: text.title( label( "Figure 7.6" ) )
GUIDE: axis(dim(1), label(" "))
GUIDE: axis(dim(2), label("Probability of Favoring School Closing"), delta(.2))
SCALE: linear(dim(2), min(-.2), max(1))
ELEMENT: line(position(smooth.spline(contam*phat4)), shape(shape.dash))
ELEMENT: line(position(smooth.spline(contam*phat5)))
ELEMENT: line(position(smooth.spline(contam*phat6)), shape(shape.half_dash))
END GPL.
Page 239 Figure 7.7 Poorness-of-fit statistic delta-chi-square(P) versus predicted
probability of favoring closed schools; X patterns 131 and 3 are poorly fit (high
delta-chi-square(P) values).
LOGISTIC REGRESSION VAR=close
/METHOD=ENTER lived educ contam hsc nodad
/SAVE PRED COOK LEVER ZRESID DEV.
Case Processing Summary
| Unweighted Cases(a) |
N |
Percent |
| Selected Cases |
Included in Analysis |
153 |
100.0 |
| Missing Cases |
0 |
.0 |
| Total |
153 |
100.0 |
| Unselected Cases |
0 |
.0 |
| Total |
153 |
100.0 |
| a If weight is in
effect, see classification table for the total number of cases. |
Dependent Variable Encoding
| Original Value |
Internal Value |
| open |
0 |
| close |
1 |
Classification Table(a,b)
|
Predicted |
| schools should close |
Percentage Correct |
|
Observed |
open |
close |
|
| Step 0 |
schools should close |
open |
87 |
0 |
100.0 |
| close |
66 |
0 |
.0 |
| Overall Percentage |
|
|
56.9 |
| a Constant is
included in the model. |
| b The cut value is
.500 |
Variables in the Equation
|
B |
S.E. |
Wald |
df |
Sig. |
Exp(B) |
| Step 0 |
Constant |
-.276 |
.163 |
2.864 |
1 |
.091 |
.759 |
Variables not in the Equation
|
Score |
df |
Sig. |
| Step 0 |
Variables |
LIVED |
12.683 |
1 |
.000 |
| EDUC |
.221 |
1 |
.638 |
| CONTAM |
17.292 |
1 |
.000 |
| HSC |
39.337 |
1 |
.000 |
| NODAD |
9.835 |
1 |
.002 |
| Overall Statistics |
56.279 |
5 |
.000 |
Omnibus Tests of Model Coefficients
|
Chi-square |
df |
Sig. |
| Step 1 |
Step |
66.559 |
5 |
.000 |
| Block |
66.559 |
5 |
.000 |
| Model |
66.559 |
5 |
.000 |
Model Summary
| Step |
-2 Log likelihood |
Cox & Snell R Square |
Nagelkerke R Square |
| 1 |
142.652 |
.353 |
.473 |
Classification Table(a)
|
Predicted |
| schools should close |
Percentage Correct |
|
Observed |
open |
close |
|
| Step 1 |
schools should close |
open |
76 |
11 |
87.4 |
| close |
25 |
41 |
62.1 |
| Overall Percentage |
|
|
76.5 |
| a The cut value is
.500 |
Variables in the Equation
|
B |
S.E. |
Wald |
df |
Sig. |
Exp(B) |
| Step 1(a) |
LIVED |
-.040 |
.015 |
6.559 |
1 |
.010 |
.961 |
| EDUC |
-.197 |
.093 |
4.509 |
1 |
.034 |
.821 |
| CONTAM |
1.298 |
.477 |
7.422 |
1 |
.006 |
3.664 |
| HSC |
2.278 |
.490 |
21.590 |
1 |
.000 |
9.762 |
| NODAD |
-1.731 |
.725 |
5.695 |
1 |
.017 |
.177 |
| Constant |
2.182 |
1.330 |
2.691 |
1 |
.101 |
8.865 |
| a Variable(s) entered
on step 1: LIVED, EDUC, CONTAM, HSC, NODAD. |
compute deltap=(zre_1)**2/(1-lev_1).
execute.
formats pre_1 (f2.1) deltap (f2.0).
GGRAPH
/GRAPHDATASET NAME="graphdataset" VARIABLES=pre_1 deltap
/GRAPHSPEC SOURCE=INLINE.
BEGIN GPL
SOURCE: s=userSource(id("graphdataset"))
DATA: deltap=col(source(s), name("deltap"))
DATA: pre_1=col(source(s), name("pre_1"))
GUIDE: text.title( label( "Figure 7.7" ) )
GUIDE: axis(dim(1), label("P-hat"), delta(.2))
GUIDE: axis(dim(2), label("Delta P"), delta(5))
SCALE: linear(dim(1), min(0), max(1))
SCALE: linear(dim(2), min(0), max(30))
ELEMENT: point(position(pre_1*deltap))
END GPL.
Page 240 Figure 7.8 Poorness-of-fit statistic
delta-chi-square(D) versus predicted probability of favoring closed schools; X patterns
131, 3, 27, 62, 115 are poorly fit (high delta-chi-square(D) values).
compute deltad=(dev_1)**2/(1-lev_1).
execute.
formats deltad (f2.0).
GGRAPH
/GRAPHDATASET NAME="graphdataset" VARIABLES=pre_1 deltad
/GRAPHSPEC SOURCE=INLINE.
BEGIN GPL
SOURCE: s=userSource(id("graphdataset"))
DATA: deltad=col(source(s), name("deltad"))
DATA: pre_1=col(source(s), name("pre_1"))
GUIDE: text.title( label( "Figure 7.8" ) )
GUIDE: axis(dim(1), label("P-hat"), delta(.2))
GUIDE: axis(dim(2), label("Delta D"), delta(1))
SCALE: linear(dim(1), min(0), max(1))
SCALE: linear(dim(2), min(0), max(7))
ELEMENT: point(position(pre_1*deltad))
END GPL.
Page 241 Figure 7.9 Influence statistic delta-B versus
predicted probability of favoring closed schools; patterns 131, 3, 115, 44, and 94 are
most influential (high delta-B values).
NOTE: Delta-B is the Cook's D statistic.
formats coo_1 (f2.1).
GGRAPH
/GRAPHDATASET NAME="graphdataset" VARIABLES=pre_1 coo_1
/GRAPHSPEC SOURCE=INLINE.
BEGIN GPL
SOURCE: s=userSource(id("graphdataset"))
DATA: coo_1=col(source(s), name("coo_1"))
DATA: pre_1=col(source(s), name("pre_1"))
GUIDE: text.title( label( "Figure 7.9" ) )
GUIDE: axis(dim(1), label("P-hat"), delta(.2))
GUIDE: axis(dim(2), label("Delta B"), delta(.1))
SCALE: linear(dim(1), min(0), max(1))
SCALE: linear(dim(2), min(0), max(.7))
ELEMENT: point(position(pre_1*coo_1))
END GPL.
Page 242 Figure 7.10 Delta-chi-square(D) versus P-hat with
symbols proportional to delta-B; large, high circles indicate influential, poorly fit X
patterns.
GGRAPH
/GRAPHDATASET NAME="graphdataset" VARIABLES=pre_1 deltad coo_1
/GRAPHSPEC SOURCE=INLINE.
BEGIN GPL
SOURCE: s=userSource(id("graphdataset"))
DATA: deltad=col(source(s), name("deltad"))
DATA: pre_1=col(source(s), name("pre_1"))
DATA: coo_1=col(source(s), name("coo_1"))
GUIDE: text.title( label( "Figure 7.10" ) )
GUIDE: axis(dim(1), label("P-hat"), delta(.2))
GUIDE: axis(dim(2), label("Delta D"), delta(1))
SCALE: linear(dim(1), min(0), max(1))
SCALE: linear(dim(2), min(0), max(7))
ELEMENT: point(position(pre_1*deltad), size(coo_1))
END GPL.
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