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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). We will use the toxic data set.
symbol1 i=r; proc gplot data=toxic; plot close*lived=1; run; quit;
page 222 Figure 7.4 Logit regression of school-closing opinion on years lived in town, also showing linear regression line.
proc logistic data=toxic descending; model close=lived; output out=toxic1 predicted=pred; run;
The LOGISTIC Procedure
Model Information
Data Set WORK.TOXIC
Response Variable close
Number of Response Levels 2
Number of Observations 153
Link Function Logit
Optimization Technique Fisher's scoring
Response Profile
Ordered Total
Value close Frequency
1 1 66
2 0 87
Model Convergence Status
Convergence criterion (GCONV=1E-8) satisfied.
Model Fit Statistics
Intercept
Intercept and
Criterion Only Covariates
AIC 211.212 199.267
SC 214.242 205.328
-2 Log L 209.212 195.267
Testing Global Null Hypothesis: BETA=0
Test Chi-Square DF Pr > ChiSq
Likelihood Ratio 13.9444 1 0.0002
Score 12.6827 1 0.0004
Wald 11.3985 1 0.0007
Analysis of Maximum Likelihood Estimates
Standard
Parameter DF Estimate Error Chi-Square Pr > ChiSq
Intercept 1 0.4600 0.2626 3.0688 0.0798
lived 1 -0.0410 0.0121 11.3985 0.0007
The LOGISTIC Procedure
Odds Ratio Estimates
Point 95% Wald
Effect Estimate Confidence Limits
lived 0.960 0.937 0.983
Association of Predicted Probabilities and Observed Responses
Percent Concordant 65.2 Somers' D 0.328
Percent Discordant 32.4 Gamma 0.336
Percent Tied 2.4 Tau-a 0.162
Pairs 5742 c 0.664
proc sort data=toxic1; by lived; run; symbol1 v=circle i=r c=black; symbol2 i=join c=blue; proc gplot data=toxic1; plot close*lived=1 pred*lived=2 / overlay; run; quit;
Figure 7.4
page 224 Table 7.1 Logit regression of school-closing opinion on years lived in town.
proc logistic data=toxic descending; model close=lived; run; quit;
The LOGISTIC Procedure
Model Information
Data Set WORK.TOXIC
Response Variable close
Number of Response Levels 2
Number of Observations 153
Link Function Logit
Optimization Technique Fisher's scoring
Response Profile
Ordered Total
Value close Frequency
1 1 66
2 0 87
Model Convergence Status
Convergence criterion (GCONV=1E-8) satisfied.
Model Fit Statistics
Intercept
Intercept and
Criterion Only Covariates
AIC 211.212 199.267
SC 214.242 205.328
-2 Log L 209.212 195.267
Testing Global Null Hypothesis: BETA=0
Test Chi-Square DF Pr > ChiSq
Likelihood Ratio 13.9444 1 0.0002
Score 12.6827 1 0.0004
Wald 11.3985 1 0.0007
Analysis of Maximum Likelihood Estimates
Standard
Parameter DF Estimate Error Chi-Square Pr > ChiSq
Intercept 1 0.4600 0.2626 3.0688 0.0798
lived 1 -0.0410 0.0121 11.3985 0.0007
The LOGISTIC Procedure
Odds Ratio Estimates
Point 95% Wald
Effect Estimate Confidence Limits
lived 0.960 0.937 0.983
Association of Predicted Probabilities and Observed Responses
Percent Concordant 65.2 Somers' D 0.328
Percent Discordant 32.4 Gamma 0.336
Percent Tied 2.4 Tau-a 0.162
Pairs 5742 c 0.664
page 226 Table 7.2 Logit regression of school-closing opinion on years lived in town, education, contamination, and HSC meetings.
proc logistic data=toxic descending; model close=lived educ contam hsc; run;
The LOGISTIC Procedure
Model Information
Data Set WORK.TOXIC
Response Variable close
Number of Response Levels 2
Number of Observations 153
Link Function Logit
Optimization Technique Fisher's scoring
Response Profile
Ordered Total
Value close Frequency
1 1 66
2 0 87
Model Convergence Status
Convergence criterion (GCONV=1E-8) satisfied.
Model Fit Statistics
Intercept
Intercept and
Criterion Only Covariates
AIC 211.212 159.382
SC 214.242 174.534
-2 Log L 209.212 149.382
Testing Global Null Hypothesis: BETA=0
Test Chi-Square DF Pr > ChiSq
Likelihood Ratio 59.8299 4 <.0001
Score 52.8451 4 <.0001
Wald 37.6991 4 <.0001
The LOGISTIC Procedure
Analysis of Maximum Likelihood Estimates
Standard
Parameter DF Estimate Error Chi-Square Pr > ChiSq
Intercept 1 1.7314 1.3020 1.7684 0.1836
lived 1 -0.0465 0.0149 9.6978 0.0018
educ 1 -0.1659 0.0899 3.4039 0.0650
contam 1 1.2081 0.4654 6.7389 0.0094
hsc 1 2.1729 0.4641 21.9187 <.0001
Odds Ratio Estimates
Point 95% Wald
Effect Estimate Confidence Limits
lived 0.955 0.927 0.983
educ 0.847 0.710 1.010
contam 3.347 1.344 8.334
hsc 8.784 3.537 21.814
Association of Predicted Probabilities and Observed Responses
Percent Concordant 83.8 Somers' D 0.679
Percent Discordant 15.9 Gamma 0.681
Percent Tied 0.3 Tau-a 0.335
Pairs 5742 c 0.840
page 227 Table 7.3 Logit regression of school-closing on seven background variables.
proc logistic data=toxic descending; model close=lived educ contam hsc female kids nodad; run;
The LOGISTIC Procedure
Model Information
Data Set WORK.TOXIC
Response Variable close
Number of Response Levels 2
Number of Observations 153
Link Function Logit
Optimization Technique Fisher's scoring
Response Profile
Ordered Total
Value close Frequency
1 1 66
2 0 87
Model Convergence Status
Convergence criterion (GCONV=1E-8) satisfied.
> Model Fit Statistics
Intercept
Intercept and
Criterion Only Covariates
AIC 211.212 157.049
SC 214.242 181.293
-2 Log L 209.212 141.049
Testing Global Null Hypothesis: BETA=0
Test Chi-Square DF Pr > ChiSq
Likelihood Ratio 68.1622 7 <.0001
Score 57.0383 7 <.0001
Wald 36.0921 7 <.0001
The LOGISTIC Procedure
Analysis of Maximum Likelihood Estimates
Standard
Parameter DF Estimate Error Chi-Square Pr > ChiSq
Intercept 1 2.8934 1.6029 3.2583 0.0711
lived 1 -0.0466 0.0170 7.5489 0.0060
educ 1 -0.2060 0.0932 4.8863 0.0271
contam 1 1.2820 0.4814 7.0929 0.0077
hsc 1 2.4179 0.5096 22.5077 <.0001
female 1 -0.0515 0.5571 0.0086 0.9263
kids 1 -0.6705 0.5656 1.4055 0.2358
nodad 1 -2.2256 0.9991 4.9626 0.0259
Odds Ratio Estimates
Point 95% Wald
Effect Estimate Confidence Limits
lived 0.954 0.923 0.987
educ 0.814 0.678 0.977
contam 3.604 1.403 9.257
hsc 11.222 4.133 30.470
female 0.950 0.319 2.830
kids 0.511 0.169 1.550
nodad 0.108 0.015 0.765
Association of Predicted Probabilities and Observed Responses
Percent Concordant 85.4 Somers' D 0.709
Percent Discordant 14.5 Gamma 0.710
Percent Tied 0.1 Tau-a 0.350
Pairs 5742 c 0.855
page 228 Table 7.4 Reduced model with male/nonparent interaction term.
proc logistic data=toxic descending; model close=lived educ contam hsc nodad; run;
The LOGISTIC Procedure
Model Information
Data Set WORK.TOXIC
Response Variable close
Number of Response Levels 2
Number of Observations 153
Link Function Logit
Optimization Technique Fisher's scoring
Response Profile
Ordered Total
Value close Frequency
1 1 66
2 0 87
Model Convergence Status
Convergence criterion (GCONV=1E-8) satisfied.
Model Fit Statistics
Intercept
Intercept and
Criterion Only Covariates
AIC 211.212 154.652
SC 214.242 172.835
-2 Log L 209.212 142.652
Testing Global Null Hypothesis: BETA=0
Test Chi-Square DF Pr > ChiSq
Likelihood Ratio 66.5591 5 <.0001
Score 56.2791 5 <.0001
Wald 36.3426 5 <.0001
The LOGISTIC Procedure
Analysis of Maximum Likelihood Estimates
Standard
Parameter DF Estimate Error Chi-Square Pr > ChiSq
Intercept 1 2.1822 1.3301 2.6915 0.1009
lived 1 -0.0396 0.0155 6.5589 0.0104
educ 1 -0.1967 0.0926 4.5092 0.0337
contam 1 1.2985 0.4766 7.4221 0.0064
hsc 1 2.2785 0.4904 21.5904 <.0001
nodad 1 -1.7308 0.7252 5.6952 0.0170
Odds Ratio Estimates
Point 95% Wald
Effect Estimate Confidence Limits
lived 0.961 0.932 0.991
educ 0.821 0.685 0.985
contam 3.664 1.440 9.325
hsc 9.762 3.734 25.523
nodad 0.177 0.043 0.734
Association of Predicted Probabilities and Observed Responses
Percent Concordant 85.2 Somers' D 0.706
Percent Discordant 14.6 Gamma 0.707
Percent Tied 0.2 Tau-a 0.349
Pairs 5742 c 0.853
page 232 Figure 7.5 Conditional effects of years lived in town, at proclosing (top), average, and anticlosing levels of other X variables.
proc logistic data=toxic descending; model close=lived educ contam hsc nodad; run;
The LOGISTIC Procedure
Model Information
Data Set WORK.TOXIC
Response Variable close
Number of Response Levels 2
Number of Observations 153
Link Function Logit
Optimization Technique Fisher's scoring
Response Profile
Ordered Total
Value close Frequency
1 1 66
2 0 87
Model Convergence Status
Convergence criterion (GCONV=1E-8) satisfied.
Model Fit Statistics
Intercept
Intercept and
Criterion Only Covariates
AIC 211.212 154.652
SC 214.242 172.835
-2 Log L 209.212 142.652
Testing Global Null Hypothesis: BETA=0
Test Chi-Square DF Pr > ChiSq
Likelihood Ratio 66.5591 5 <.0001
Score 56.2791 5 <.0001
Wald 36.3426 5 <.0001
The LOGISTIC Procedure
Analysis of Maximum Likelihood Estimates
Standard
Parameter DF Estimate Error Chi-Square Pr > ChiSq
Intercept 1 2.1822 1.3301 2.6915 0.1009
lived 1 -0.0396 0.0155 6.5589 0.0104
educ 1 -0.1967 0.0926 4.5092 0.0337
contam 1 1.2985 0.4766 7.4221 0.0064
hsc 1 2.2785 0.4904 21.5904 <.0001
nodad 1 -1.7308 0.7252 5.6952 0.0170
Odds Ratio Estimates
Point 95% Wald
Effect Estimate Confidence Limits
lived 0.961 0.932 0.991
educ 0.821 0.685 0.985
contam 3.664 1.440 9.325
hsc 9.762 3.734 25.523
nodad 0.177 0.043 0.734
Association of Predicted Probabilities and Observed Responses
Percent Concordant 85.2 Somers' D 0.706
Percent Discordant 14.6 Gamma 0.707
Percent Tied 0.2 Tau-a 0.349
Pairs 5742 c 0.853
data toxic5; set toxic; lhat1=3.17-.04*(lived); phat1=1/(1+exp(-lhat1)); lhat2=.387-.04*(lived); phat2=1/(1+exp(-lhat2)); lhat3=-2.14-.04*(lived); phat3=1/(1+exp(-lhat3)); run; proc sort data=toxic5; by lived; run; axis1 order=(0 to 90 by 10) minor=none; axis2 label =(a=90 'Probability of Favoring School Closing') minor=none; symbol1 i=join w=2 c =red line=23; symbol2 i=join w=2 c =black line=1; symbol3 i=join w=2 c =green line=2; proc gplot data=toxic5; plot phat1*lived phat2*lived phat3*lived / overlay vaxis=axis2 haxis=axis1; run; quit;
Figure 7.5
page 232 Figure 7.6 Conditional effects of contamination, at proclosing, average, and anticlosing levels of other X variables.
proc logistic data=toxic descending; model close=lived educ contam hsc nodad; run;
The LOGISTIC Procedure
Model Information
Data Set WORK.TOXIC
Response Variable close
Number of Response Levels 2
Number of Observations 153
Link Function Logit
Optimization Technique Fisher's scoring
Response Profile
Ordered Total
Value close Frequency
1 1 66
2 0 87
Model Convergence Status
Convergence criterion (GCONV=1E-8) satisfied.
Model Fit Statistics
Intercept
Intercept and
Criterion Only Covariates
AIC 211.212 154.652
SC 214.242 172.835
-2 Log L 209.212 142.652
Testing Global Null Hypothesis: BETA=0
Test Chi-Square DF Pr > ChiSq
Likelihood Ratio 66.5591 5 <.0001
Score 56.2791 5 <.0001
Wald 36.3426 5 <.0001
The LOGISTIC Procedure
Analysis of Maximum Likelihood Estimates
Standard
Parameter DF Estimate Error Chi-Square Pr > ChiSq
Intercept 1 2.1822 1.3301 2.6915 0.1009
lived 1 -0.0396 0.0155 6.5589 0.0104
educ 1 -0.1967 0.0926 4.5092 0.0337
contam 1 1.2985 0.4766 7.4221 0.0064
hsc 1 2.2785 0.4904 21.5904 <.0001
nodad 1 -1.7308 0.7252 5.6952 0.0170
Odds Ratio Estimates
Point 95% Wald
Effect Estimate Confidence Limits
lived 0.961 0.932 0.991
educ 0.821 0.685 0.985
contam 3.664 1.440 9.325
hsc 9.762 3.734 25.523
nodad 0.177 0.043 0.734
Association of Predicted Probabilities and Observed Responses
Percent Concordant 85.2 Somers' D 0.706
Percent Discordant 14.6 Gamma 0.707
Percent Tied 0.2 Tau-a 0.349
Pairs 5742 c 0.853
data toxic6; set toxic; lhat1=3.22+1.3*(contam); phat1=1/(1+exp(-lhat1)); lhat2=-.7681+1.3*(contam); phat2=1/(1+exp(-lhat2)); lhat3=-6.79+1.3*(contam); phat3=1/(1+exp(-lhat3)); run; proc sort data=toxic6; by contam; run; axis1 order=(0 1) minor=none; axis2 label =(a=90 'Probability of Favoring School Closing') minor=none; symbol1 w=2 c=red i=join line=23; symbol2 w=2 c=black i=join line=1; symbol3 w=2 c=green i=join line=2; proc gplot data=toxic6; plot phat1*contam=1 phat2*contam=2 phat3*contam=3 / overlay vaxis=axis2 haxis=axis1; run; quit;
Figure 7.6
page 234 Table 7.5 Cross-tabulation and logit analysis with perfect one-way discrimination.
data powd; input less mother cnt; cards; 0 0 202 0 1 79 1 0 44 1 1 0 ; run; proc logistic data=powd descending; model less=mother; weight cnt; run;
The LOGISTIC Procedure
Model Information
Data Set WORK.POWD
Response Variable less
Number of Response Levels 2
Number of Observations 3
Weight Variable cnt
Sum of Weights 325
Link Function Logit
Optimization Technique Fisher's scoring
Response Profile
Ordered Total Total
Value less Frequency Weight
1 1 1 44.00000
2 0 2 281.00000
NOTE: 1 observation having zero frequency or weight was excluded since it does not contribute
to the analysis.
Model Convergence Status
Convergence criterion (GCONV=1E-8) satisfied.
Model Fit Statistics
Intercept
Intercept and
Criterion Only Covariates
AIC 259.722 235.074
SC 258.821 233.272
-2 Log L 257.722 231.074
Testing Global Null Hypothesis: BETA=0
Test Chi-Square DF Pr > ChiSq
Likelihood Ratio 26.6481 1 <.0001
Score 16.3426 1 <.0001
Wald 0.0003 1 0.9861
The LOGISTIC Procedure
Analysis of Maximum Likelihood Estimates
Standard
Parameter DF Estimate Error Chi-Square Pr > ChiSq
Intercept 1 -1.5241 0.1664 83.9235 <.0001
mother 1 -16.5630 952.2 0.0003 0.9861
Odds Ratio Estimates
Point 95% Wald
Effect Estimate Confidence Limits
mother <0.001 <0.001 >999.999
Association of Predicted Probabilities and Observed Responses
Percent Concordant 50.0 Somers' D 0.500
Percent Discordant 0.0 Gamma 1.000
Percent Tied 50.0 Tau-a 0.333
Pairs 2 c 0.750
page 239 Figure 7.7 Poorness-of-fit statistic delta-chi-squared p versus predicted probability of favoring closed schools - X patterns 131 and 3 are poorly fit (high delta-chi-squared p values).
proc logistic data=toxic descending; model close=lived educ contam hsc nodad; output out=toxic7 prob=p resdev=dr h=pii reschi=pr difchisq=dfg; run;
The LOGISTIC Procedure
Model Information
Data Set WORK.TOXIC
Response Variable close
Number of Response Levels 2
Number of Observations 153
Link Function Logit
Optimization Technique Fisher's scoring
Response Profile
Ordered Total
Value close Frequency
1 1 66
2 0 87
Model Convergence Status
Convergence criterion (GCONV=1E-8) satisfied.
Model Fit Statistics
Intercept
Intercept and
Criterion Only Covariates
AIC 211.212 154.652
SC 214.242 172.835
-2 Log L 209.212 142.652
Testing Global Null Hypothesis: BETA=0
Test Chi-Square DF Pr > ChiSq
Likelihood Ratio 66.5591 5 <.0001
Score 56.2791 5 <.0001
Wald 36.3426 5 <.0001
The LOGISTIC Procedure
Analysis of Maximum Likelihood Estimates
Standard
Parameter DF Estimate Error Chi-Square Pr > ChiSq
Intercept 1 2.1822 1.3301 2.6915 0.1009
lived 1 -0.0396 0.0155 6.5589 0.0104
educ 1 -0.1967 0.0926 4.5092 0.0337
contam 1 1.2985 0.4766 7.4221 0.0064
hsc 1 2.2785 0.4904 21.5904 <.0001
nodad 1 -1.7308 0.7252 5.6952 0.0170
Odds Ratio Estimates
Point 95% Wald
Effect Estimate Confidence Limits
lived 0.961 0.932 0.991
educ 0.821 0.685 0.985
contam 3.664 1.440 9.325
hsc 9.762 3.734 25.523
nodad 0.177 0.043 0.734
Association of Predicted Probabilities and Observed Responses
Percent Concordant 85.2 Somers' D 0.706
Percent Discordant 14.6 Gamma 0.707
Percent Tied 0.2 Tau-a 0.349
Pairs 5742 c 0.853
symbol1 v=circle i=none; axis1 order=(0 to 1 by .2); axis2 order=(0 to 30 by 5)label=(r=0 a=90); proc gplot data=toxic7; plot dfg*p / haxis=axis1 vaxis=axis2 hminor=1 vminor=0; run; quit;
Figure 7.7
page 240 Figure 7.8 Poorness-of-fit statistic delta-chi-squared d versus predicted probability of favoring closed schools - X patterns 131, 3, 27, 62, and 115 are poorly fit (high delta-chi-squared d values).
proc logistic data=toxic descending; model close=lived educ contam hsc nodad / influence; ods output Influence=toxic9; run;
The LOGISTIC Procedure
Model Information
Data Set WORK.TOXIC
Response Variable close
Number of Response Levels 2
Number of Observations 153
Link Function Logit
Optimization Technique Fisher's scoring
Response Profile
Ordered Total
Value close Frequency
1 1 66
2 0 87
Model Convergence Status
Convergence criterion (GCONV=1E-8) satisfied.
Model Fit Statistics
Intercept
Intercept and
Criterion Only Covariates
AIC 211.212 154.652
SC 214.242 172.835
-2 Log L 209.212 142.652
Testing Global Null Hypothesis: BETA=0
Test Chi-Square DF Pr > ChiSq
Likelihood Ratio 66.5591 5 <.0001
Score 56.2791 5 <.0001
Wald 36.3426 5 <.0001
The LOGISTIC Procedure
Analysis of Maximum Likelihood Estimates
Standard
Parameter DF Estimate Error Chi-Square Pr > ChiSq
Intercept 1 2.1822 1.3301 2.6915 0.1009
lived 1 -0.0396 0.0155 6.5589 0.0104
educ 1 -0.1967 0.0926 4.5092 0.0337
contam 1 1.2985 0.4766 7.4221 0.0064
hsc 1 2.2785 0.4904 21.5904 <.0001
nodad 1 -1.7308 0.7252 5.6952 0.0170
Odds Ratio Estimates
Point 95% Wald
Effect Estimate Confidence Limits
lived 0.961 0.932 0.991
educ 0.821 0.685 0.985
contam 3.664 1.440 9.325
hsc 9.762 3.734 25.523
nodad 0.177 0.043 0.734
Association of Predicted Probabilities and Observed Responses
Percent Concordant 85.2 Somers' D 0.706
Percent Discordant 14.6 Gamma 0.707
Percent Tied 0.2 Tau-a 0.349
Pairs 5742 c 0.853
The LOGISTIC Procedure
Regression Diagnostics
Pearson Residual
Covariates
Case (1 unit = 0.67)
Number lived educ contam hsc nodad Value -8 -4 0 2 4 6 8
1 15.0000 12.0000 0 0 0 1.4714 | | * |
2 15.0000 13.0000 0 1.0000 0 0.5196 | |* |
3 5.0000 16.0000 0 0 0 1.7884 | | * |
4 10.0000 12.0000 1.0000 1.0000 0 0.2228 | * |
5 3.0000 18.0000 0 1.0000 0 0.6697 | |* |
6 13.0000 12.0000 1.0000 1.0000 0 0.2365 | * |
7 3.0000 12.0000 0 1.0000 0 0.3712 | |* |
8 3.0000 17.0000 1.0000 1.0000 1.0000 0.7534 | |* |
9 13.0000 14.0000 0 0 0 1.7216 | | * |
10 13.0000 12.0000 0 1.0000 0 0.4526 | |* |
11 3.0000 13.0000 1.0000 1.0000 0 0.2140 | * |
12 7.0000 12.0000 0 1.0000 0 0.4019 | |* |
13 10.0000 12.0000 1.0000 1.0000 0 0.2228 | * |
14 5.0000 12.0000 0 1.0000 0 0.3863 | |* |
15 3.0000 13.0000 1.0000 1.0000 0 0.2140 | * |
16 11.0000 16.0000 1.0000 0 0 -0.9503 | *| |
17 4.0000 16.0000 0 0 0 -0.5704 | *| |
18 1.0000 12.0000 0 1.0000 0 0.3568 | |* |
19 55.0000 12.0000 0 0 0 -0.3075 | * |
20 49.0000 16.0000 1.0000 0 0 -0.4474 | *| |
21 10.0000 12.0000 1.0000 1.0000 0 0.2228 | * |
22 50.0000 12.0000 0 0 0 -0.3396 | *| |
23 4.0000 14.0000 0 0 0 1.4403 | | * |
24 6.0000 16.0000 0 0 1.0000 -0.2307 | * |
25 5.0000 14.0000 0 0 0 1.4691 | | * |
26 6.0000 13.0000 1.0000 1.0000 0 0.2271 | * |
27 32.0000 13.0000 1.0000 0 0 1.1880 | | * |
28 5.0000 12.0000 0 0 0 -0.8286 | *| |
29 21.0000 12.0000 0 0 0 -0.6034 | *| |
30 24.0000 12.0000 0 0 0 -0.5686 | *| |
31 24.0000 12.0000 0 0 1.0000 -0.2393 | * |
32 30.0000 13.0000 0 0 0 2.1856 | | * |
33 37.0000 12.0000 0 0 0 -0.4394 | *| |
34 7.0000 10.0000 1.0000 1.0000 0 0.1725 | * |
35 3.0000 20.0000 0 0 0 -0.3926 | *| |
36 35.0000 12.0000 0 0 0 -0.4572 | *| |
37 18.0000 16.0000 0 0 0 -0.4321 | *| |
38 15.0000 12.0000 0 0 0 1.4714 | | * |
39 16.0000 16.0000 0 0 0 -0.4496 | *| |
40 10.0000 8.0000 1.0000 0 1.0000 -0.8959 | *| |
41 4.0000 12.0000 1.0000 1.0000 0 0.1978 | * |
42 56.0000 14.0000 1.0000 1.0000 1.0000 -0.6234 | *| |
43 28.0000 12.0000 0 0 0 -0.5252 | *| |
44 51.0000 8.0000 1.0000 0 0 1.0590 | | * |
45 4.0000 12.0000 0 0 0 -0.8452 | *| |
The LOGISTIC Procedure
Regression Diagnostics
Deviance Residual Hat Matrix Diagonal
Case (1 unit = 0.33) (1 unit = 0.01)
Number Value -8 -4 0 2 4 6 8 Value 0 2 4 6 8 12 16
1 1.5180 | | * | 0.0177 | * |
2 0.6914 | | * | 0.0350 | * |
3 1.6939 | | * | 0.0269 | * |
4 0.3113 | |* | 0.0167 | * |
5 0.8608 | | * | 0.0720 | * |
6 0.3299 | |* | 0.0180 | * |
7 0.5082 | | * | 0.0307 | * |
8 0.9482 | | * | 0.1718 | * |
9 1.6596 | | * | 0.0152 | * |
10 0.6105 | | * | 0.0334 | * |
11 0.2993 | |* | 0.0159 | * |
12 0.5472 | | * | 0.0314 | * |
13 0.3113 | |* | 0.0167 | * |
14 0.5274 | | * | 0.0310 | * |
15 0.2993 | |* | 0.0159 | * |
16 -1.1344 | * | | 0.0629 | * |
17 -0.7505 | * | | 0.0280 | * |
18 0.4896 | | * | 0.0305 | * |
19 -0.4251 | *| | 0.0326 | * |
20 -0.6041 | * | | 0.0807 | * |
21 0.3113 | |* | 0.0167 | * |
22 -0.4672 | *| | 0.0306 | * |
23 1.4987 | | * | 0.0227 | * |
24 -0.3221 | *| | 0.0343 | * |
25 1.5166 | | * | 0.0215 | * |
26 0.3171 | |* | 0.0167 | * |
27 1.3268 | | * | 0.0582 | * |
28 -1.0225 | * | | 0.0286 | * |
29 -0.7880 | * | | 0.0161 | * |
30 -0.7485 | * | | 0.0165 | * |
31 -0.3337 | *| | 0.0274 | * |
32 1.8729 | | * | 0.0174 | * |
33 -0.5942 | * | | 0.0229 | * |
34 0.2421 | |* | 0.0144 | * |
35 -0.5354 | * | | 0.0521 | * |
36 -0.6161 | * | | 0.0217 | * |
37 -0.5852 | * | | 0.0210 | * |
38 1.5180 | | * | 0.0177 | * |
39 -0.6068 | * | | 0.0212 | * |
40 -1.0856 | * | | 0.1812 | *|
41 0.2771 | |* | 0.0149 | * |
42 -0.8103 | * | | 0.1739 | * |
43 -0.6980 | * | | 0.0178 | * |
44 1.2265 | | * | 0.1317 | * |
45 -1.0383 | * | | 0.0304 | * |
The LOGISTIC Procedure
Regression Diagnostics
Intercept lived
Case DfBeta (1 unit = 0.06) DfBeta (1 unit = 0.1)
Number Value -8 -4 0 2 4 6 8 Value -8 -4 0 2 4 6 8
1 0.1028 | | * | -0.0293 | * |
2 0.0205 | * | -0.00590 | * |
3 -0.1057 | * | | -0.0591 | *| |
4 0.0101 | * | -0.00802 | * |
5 -0.0881 | *| | -0.0203 | * |
6 0.0102 | * | -0.00649 | * |
7 0.0339 | |* | -0.0304 | * |
8 -0.1303 | * | | -0.0807 | *| |
9 -0.0121 | * | -0.00935 | * |
10 0.0347 | |* | -0.0155 | * |
11 0.00772 | * | -0.0109 | * |
12 0.0347 | |* | -0.0260 | * |
13 0.0101 | * | -0.00802 | * |
14 0.0344 | |* | -0.0284 | * |
15 0.00772 | * | -0.0109 | * |
16 0.0953 | | * | 0.00749 | * |
17 0.0322 | |* | 0.0230 | * |
18 0.0334 | |* | -0.0319 | * |
19 0.0134 | * | -0.0516 | *| |
20 0.0854 | |* | -0.1040 | *| |
21 0.0101 | * | -0.00802 | * |
22 0.0120 | * | -0.0533 | *| |
23 0.0351 | |* | -0.1047 | *| |
24 0.0135 | * | 0.00635 | * |
25 0.0303 | |* | -0.0951 | *| |
26 0.00755 | * | -0.00989 | * |
27 -0.0476 | *| | 0.1363 | |* |
28 -0.0930 | * | | 0.0825 | |* |
29 -0.0280 | * | -0.0138 | * |
30 -0.0201 | * | -0.0242 | * |
31 0.00188 | * | -0.00201 | * |
32 -0.0447 | *| | 0.1856 | | * |
33 0.00262 | * | -0.0494 | *| |
34 0.0121 | * | -0.00803 | * |
35 0.0672 | |* | -0.00211 | * |
36 0.000172 | * | -0.0473 | * |
37 0.0372 | |* | -0.0202 | * |
38 0.1028 | | * | -0.0293 | * |
39 0.0372 | |* | -0.0163 | * |
40 -0.2277 | * | | 0.1818 | | * |
41 0.00977 | * | -0.00993 | * |
42 0.1437 | | * | -0.1846 | * | |
43 -0.0112 | * | -0.0350 | * |
44 0.1391 | | * | 0.2202 | | * |
45 -0.0986 | * | | 0.0913 | |* |
The LOGISTIC Procedure
Regression Diagnostics
educ contam
Case DfBeta (1 unit = 0.05) DfBeta (1 unit = 0.05)
Number Value -8 -4 0 2 4 6 8 Value -8 -4 0 2 4 6 8
1 -0.0658 | *| | -0.0737 | * | |
2 -0.0177 | * | -0.0262 | *| |
3 0.1764 | | * | -0.0920 | * | |
4 -0.0109 | * | 0.0177 | * |
5 0.1073 | | * | -0.0447 | *| |
6 -0.0114 | * | 0.0197 | * |
7 -0.0292 | *| | -0.0126 | * |
8 0.1407 | | * | 0.0970 | | * |
9 0.0612 | |* | -0.0872 | * | |
10 -0.0322 | *| | -0.0196 | * |
11 -0.00755 | * | 0.0165 | * |
12 -0.0307 | *| | -0.0151 | * |
13 -0.0109 | * | 0.0177 | * |
14 -0.0300 | *| | -0.0138 | * |
15 -0.00755 | * | 0.0165 | * |
16 -0.1151 | * | | -0.1787 | * | |
17 -0.0556 | *| | 0.0294 | |* |
18 -0.0284 | *| | -0.0115 | * |
19 -0.00969 | * | 0.0132 | * |
20 -0.0762 | *| | -0.0568 | *| |
21 -0.0109 | * | 0.0177 | * |
22 -0.00887 | * | 0.0152 | * |
23 0.0242 | * | -0.0728 | * | |
24 -0.0184 | * | 0.0128 | * |
25 0.0280 | |* | -0.0743 | * | |
26 -0.00771 | * | 0.0183 | * |
27 0.0348 | |* | 0.2159 | | * |
28 0.0608 | |* | 0.0400 | |* |
29 0.0174 | * | 0.0304 | |* |
30 0.0121 | * | 0.0286 | |* |
31 -0.00403 | * | 0.0135 | * |
32 0.0625 | |* | -0.1069 | * | |
33 -0.00284 | * | 0.0213 | * |
34 -0.0125 | * | 0.0112 | * |
35 -0.0825 | * | | 0.0194 | * |
36 -0.00124 | * | 0.0224 | * |
37 -0.0468 | *| | 0.0211 | * |
38 -0.0658 | *| | -0.0737 | * | |
39 -0.0483 | *| | 0.0222 | * |
40 0.2161 | | * | -0.1477 | * | |
41 -0.00984 | * | 0.0143 | * |
42 -0.1060 | * | | -0.0619 | *| |
43 0.00627 | * | 0.0263 | |* |
44 -0.1891 | * | | 0.2162 | | * |
45 0.0646 | |* | 0.0406 | |* |
The LOGISTIC Procedure
Regression Diagnostics
hsc nodad
Case DfBeta (1 unit = 0.05) DfBeta (1 unit = 0.05)
Number Value -8 -4 0 2 4 6 8 Value -8 -4 0 2 4 6 8
1 -0.0637 | *| | -0.0306 | *| |
2 0.0846 | | * | -0.0250 | *| |
3 -0.1144 | * | | 0.0242 | |* |
4 0.0190 | * | -0.00989 | * |
5 0.1053 | | * | -0.00686 | * |
6 0.0212 | * | -0.0113 | * |
7 0.0506 | |* | -0.0142 | * |
8 0.0862 | | * | 0.2650 | | * |
9 -0.0913 | * | | -0.00793 | * |
10 0.0703 | |* | -0.0222 | * |
11 0.0170 | * | -0.00805 | * |
12 0.0578 | |* | -0.0170 | * |
13 0.0190 | * | -0.00989 | * |
14 0.0541 | |* | -0.0155 | * |
15 0.0170 | * | -0.00805 | * |
16 0.0792 | | * | 0.0304 | |* |
17 0.0368 | |* | -0.00815 | * |
18 0.0472 | |* | -0.0129 | * |
19 0.00987 | * | 0.00860 | * |
20 0.0316 | |* | 0.0199 | * |
21 0.0190 | * | -0.00989 | * |
22 0.0115 | * | 0.00957 | * |
23 -0.0800 | * | | 0.000911 | * |
24 0.0143 | * | -0.0395 | *| |
25 -0.0813 | * | | 8.4E-6 | * |
26 0.0190 | * | -0.00922 | * |
27 -0.0757 | * | | -0.0832 | * | |
28 0.0363 | |* | 0.0130 | * |
29 0.0256 | |* | 0.0141 | * |
30 0.0238 | * | 0.0139 | * |
31 0.0123 | * | -0.0373 | *| |
32 -0.0955 | * | | -0.0405 | *| |
33 0.0169 | * | 0.0121 | * |
34 0.0125 | * | -0.00682 | * |
35 0.0288 | |* | -0.0134 | * |
36 0.0178 | * | 0.0124 | * |
37 0.0245 | |* | -0.00224 | * |
38 -0.0637 | *| | -0.0306 | *| |
39 0.0260 | |* | -0.00285 | * |
40 0.0885 | | * | -0.2952 | * | |
41 0.0152 | * | -0.00756 | * |
42 -0.0734 | * | | -0.1662 | * | |
43 0.0215 | * | 0.0134 | * |
44 -0.0344 | *| | -0.1437 | * | |
45 0.0371 | |* | 0.0127 | * |
The LOGISTIC Procedure
Regression Diagnostics
Confidence Interval Displacement C Confidence Interval Displacement CBar
Case (1 unit = 0.04) (1 unit = 0.04)
Number Value 0 2 4 6 8 12 16 Value 0 2 4 6 8 12 16
1 0.0396 | * | 0.0389 | * |
2 0.0101 |* | 0.00979 |* |
3 0.0909 | * | 0.0885 | * |
4 0.000859 |* | 0.000845 |* |
5 0.0375 | * | 0.0348 | * |
6 0.00104 |* | 0.00102 |* |
7 0.00450 |* | 0.00436 |* |
8 0.1422 | * | 0.1178 | * |
9 0.0466 | * | 0.0459 | * |
10 0.00733 |* | 0.00709 |* |
11 0.000751 |* | 0.000739 |* |
12 0.00540 |* | 0.00523 |* |
13 0.000859 |* | 0.000845 |* |
14 0.00492 |* | 0.00477 |* |
15 0.000751 |* | 0.000739 |* |
16 0.0647 | * | 0.0606 | * |
17 0.00962 |* | 0.00935 |* |
18 0.00413 |* | 0.00400 |* |
19 0.00330 |* | 0.00319 |* |
20 0.0191 |* | 0.0176 |* |
21 0.000859 |* | 0.000845 |* |
22 0.00375 |* | 0.00364 |* |
23 0.0494 | * | 0.0483 | * |
24 0.00196 |* | 0.00189 |* |
25 0.0484 | * | 0.0473 | * |
26 0.000892 |* | 0.000877 |* |
27 0.0927 | * | 0.0873 | * |
28 0.0208 | * | 0.0202 | * |
29 0.00607 |* | 0.00597 |* |
30 0.00551 |* | 0.00542 |* |
31 0.00166 |* | 0.00161 |* |
32 0.0859 | * | 0.0844 | * |
33 0.00464 |* | 0.00453 |* |
34 0.000441 |* | 0.000434 |* |
35 0.00894 |* | 0.00848 |* |
36 0.00473 |* | 0.00463 |* |
37 0.00409 |* | 0.00401 |* |
38 0.0396 | * | 0.0389 | * |
39 0.00448 |* | 0.00438 |* |
40 0.2170 | * | 0.1777 | * |
41 0.000601 |* | 0.000592 |* |
42 0.0991 | * | 0.0818 | * |
43 0.00510 |* | 0.00501 |* |
44 0.1959 | * | 0.1701 | * |
45 0.0231 | * | 0.0224 | * |
The LOGISTIC Procedure
Regression Diagnostics
Delta Deviance Delta Chi-Square
Case (1 unit = 0.45) (1 unit = 1.82)
Number Value 0 2 4 6 8 12 16 Value 0 2 4 6 8 12 16
1 2.3432 | * | 2.2040 | * |
2 0.4878 | * | 0.2798 |* |
3 2.9578 | * | 3.2867 | * |
4 0.0978 |* | 0.0505 |* |
5 0.7759 | * | 0.4833 |* |
6 0.1098 |* | 0.0569 |* |
7 0.2626 | * | 0.1422 |* |
8 1.0169 | * | 0.6854 |* |
9 2.8003 | * | 3.0097 | * |
10 0.3799 | * | 0.2120 |* |
11 0.0903 |* | 0.0465 |* |
12 0.3047 | * | 0.1667 |* |
13 0.0978 |* | 0.0505 |* |
14 0.2829 | * | 0.1540 |* |
15 0.0903 |* | 0.0465 |* |
16 1.3475 | * | 0.9636 | * |
17 0.5727 | * | 0.3347 |* |
18 0.2437 | * | 0.1313 |* |
19 0.1839 |* | 0.0978 |* |
20 0.3825 | * | 0.2177 |* |
21 0.0978 |* | 0.0505 |* |
22 0.2219 |* | 0.1190 |* |
23 2.2945 | * | 2.1226 | * |
24 0.1056 |* | 0.0551 |* |
25 2.3474 | * | 2.2056 | * |
26 0.1015 |* | 0.0525 |* |
27 1.8477 | * | 1.4987 | * |
28 1.0657 | * | 0.7069 |* |
29 0.6270 | * | 0.3701 |* |
30 0.5656 | * | 0.3287 |* |
31 0.1130 |* | 0.0589 |* |
32 3.5921 | * | 4.8613 | * |
33 0.3576 | * | 0.1976 |* |
34 0.0591 |* | 0.0302 |* |
35 0.2952 | * | 0.1626 |* |
36 0.3842 | * | 0.2136 |* |
37 0.3464 | * | 0.1908 |* |
38 2.3432 | * | 2.2040 | * |
39 0.3726 | * | 0.2065 |* |
40 1.3562 | * | 0.9803 | * |
41 0.0774 |* | 0.0397 |* |
42 0.7385 | * | 0.4705 |* |
43 0.4923 | * | 0.2809 |* |
44 1.6743 | * | 1.2916 | * |
45 1.1005 | * | 0.7368 |* |
The LOGISTIC Procedure
Regression Diagnostics
Pearson Residual
Covariates
Case (1 unit = 0.67)
Number lived educ contam hsc nodad Value -8 -4 0 2 4 6 8
46 29.0000 12.0000 1.0000 1.0000 0 0.3247 | * |
47 20.0000 12.0000 0 0 0 1.6247 | | * |
48 39.0000 14.0000 0 0 1.0000 -0.1460 | * |
49 27.0000 12.0000 0 0 0 -0.5357 | *| |
50 10.0000 12.0000 0 0 0 -0.7504 | *| |
51 20.0000 12.0000 0 0 1.0000 -0.2591 | * |
52 5.0000 14.0000 0 0 0 1.4691 | | * |
53 24.0000 12.0000 0 0 1.0000 -0.2393 | * |
54 12.0000 18.0000 0 0 0 -0.3998 | *| |
55 4.0000 12.0000 0 0 1.0000 -0.3557 | *| |
56 9.0000 8.0000 1.0000 0 1.0000 1.0943 | | * |
57 35.0000 12.0000 1.0000 1.0000 0 0.3658 | |* |
58 22.0000 12.0000 1.0000 1.0000 0 0.2827 | * |
59 40.0000 12.0000 1.0000 0 0 1.2618 | | * |
60 27.0000 12.0000 1.0000 1.0000 0 0.3121 | * |
61 65.0000 9.0000 0 0 1.0000 -0.1426 | * |
62 38.0000 12.0000 0 0 0 -0.4308 | *| |
63 54.0000 12.0000 0 0 1.0000 -0.1320 | * |
64 30.0000 9.0000 0 0 0 1.4749 | | * |
65 40.0000 12.0000 1.0000 1.0000 0 -2.4761 | * | |
66 29.0000 10.0000 0 1.0000 1.0000 1.2132 | | * |
67 3.0000 16.0000 0 1.0000 0 0.5501 | |* |
68 65.0000 12.0000 0 0 1.0000 -0.1062 | * |
69 15.0000 16.0000 1.0000 0 0 1.1392 | | * |
70 25.0000 14.0000 1.0000 1.0000 0 0.3652 | |* |
71 22.0000 16.0000 0 0 0 -0.3992 | *| |
72 5.0000 12.0000 0 1.0000 0 0.3863 | |* |
73 45.0000 13.0000 0 0 0 -0.3398 | *| |
74 6.0000 12.0000 0 1.0000 0 0.3940 | |* |
75 15.0000 14.0000 1.0000 0 0 0.9358 | |* |
76 36.0000 12.0000 1.0000 0 0 -0.8579 | *| |
77 20.0000 16.0000 0 0 0 -0.4153 | *| |
78 3.0000 12.0000 1.0000 1.0000 0 0.1940 | * |
79 13.0000 17.0000 1.0000 0 0 -0.8278 | *| |
80 42.0000 12.0000 0 0 0 -0.3979 | *| |
81 18.0000 14.0000 0 0 0 1.9009 | | * |
82 6.0000 12.0000 0 0 0 -0.8124 | *| |
83 4.0000 16.0000 0 0 0 1.7533 | | * |
84 30.0000 12.0000 0 1.0000 0 -1.5772 | * | |
85 8.0000 16.0000 1.0000 0 0 -1.0085 | * | |
86 36.0000 9.0000 0 0 0 -0.6020 | *| |
87 4.0000 12.0000 0 0 0 -0.8452 | *| |
88 35.0000 8.0000 0 0 1.0000 -0.2851 | * |
89 36.0000 13.0000 0 0 1.0000 -0.1710 | * |
90 5.0000 16.0000 0 0 0 -0.5592 | *| |
The LOGISTIC Procedure
Regression Diagnostics
Deviance Residual Hat Matrix Diagonal
Case (1 unit = 0.33) (1 unit = 0.01)
Number Value -8 -4 0 2 4 6 8 Value 0 2 4 6 8 12 16
46 0.4478 | |* | 0.0310 | * |
47 1.6074 | | * | 0.0162 | * |
48 -0.2054 | *| | 0.0142 | * |
49 -0.7104 | * | | 0.0174 | * |
50 -0.9452 | * | | 0.0217 | * |
51 -0.3604 | *| | 0.0315 | * |
52 1.5166 | | * | 0.0215 | * |
53 -0.3337 | *| | 0.0274 | * |
54 -0.5446 | * | | 0.0326 | * |
55 -0.4882 | *| | 0.0606 | * |
56 1.2548 | | * | 0.1846 | *|
57 0.5011 | | * | 0.0408 | * |
58 0.3921 | |* | 0.0236 | * |
59 1.3802 | | * | 0.0711 | * |
60 0.4312 | |* | 0.0285 | * |
61 -0.2006 | *| | 0.0177 | * |
62 -0.5835 | * | | 0.0236 | * |
63 -0.1859 | *| | 0.0132 | * |
64 1.5201 | | * | 0.0439 | * |
65 -1.9821 | * | | 0.0521 | * |
66 1.3453 | | * | 0.1431 | * |
67 0.7272 | | * | 0.0450 | * |
68 -0.1497 | * | 0.0108 | * |
69 1.2899 | | * | 0.0625 | * |
70 0.5004 | | * | 0.0339 | * |
71 -0.5438 | * | | 0.0211 | * |
72 0.5274 | | * | 0.0310 | * |
73 -0.4675 | *| | 0.0258 | * |
74 0.5372 | | * | 0.0311 | * |
75 1.1216 | | * | 0.0490 | * |
76 -1.0503 | * | | 0.0648 | * |
77 -0.5642 | * | | 0.0210 | * |
78 0.2718 | |* | 0.0146 | * |
79 -1.0217 | * | | 0.0735 | * |
80 -0.5422 | * | | 0.0261 | * |
81 1.7487 | | * | 0.0144 | * |
82 -1.0068 | * | | 0.0270 | * |
83 1.6761 | | * | 0.0280 | * |
84 -1.5806 | * | | 0.0531 | * |
85 -1.1846 | * | | 0.0639 | * |
86 -0.7864 | * | | 0.0450 | * |
87 -1.0383 | * | | 0.0304 | * |
88 -0.3954 | *| | 0.0430 | * |
89 -0.2401 | *| | 0.0170 | * |
90 -0.7376 | * | | 0.0269 | * |
The LOGISTIC Procedure
Regression Diagnostics
Intercept lived
Case DfBeta (1 unit = 0.06) DfBeta (1 unit = 0.1)
Number Value -8 -4 0 2 4 6 8 Value -8 -4 0 2 4 6 8
46 0.00686 | * | 0.0125 | * |
47 0.0815 | |* | 0.0261 | * |
48 0.00686 | * | -0.00697 | * |
49 -0.0133 | * | -0.0326 | * |
50 -0.0681 | *| | 0.0440 | * |
51 0.000263 | * | 0.00168 | * |
52 0.0303 | |* | -0.0951 | *| |
53 0.00188 | * | -0.00201 | * |
54 0.0521 | |* | -0.0133 | * |
55 -0.0137 | * | 0.0327 | * |
56 0.2847 | | * | -0.2338 | * | |
57 0.00308 | * | 0.0271 | * |
58 0.00922 | * | 0.00144 | * |
59 -0.0309 | *| | 0.2068 | | * |
60 0.00772 | * | 0.00881 | * |
61 0.00143 | * | -0.0118 | * |
62 0.00373 | * | -0.0503 | *| |
63 0.00446 | * | -0.00869 | * |
64 0.2178 | | * | 0.0710 | |* |
65 0.0110 | * | -0.2700 | * | |
66 0.0988 | | * | 0.0262 | * |
67 -0.0208 | * | -0.0296 | * |
68 0.00381 | * | -0.00756 | * |
69 -0.1313 | * | | 0.0284 | * |
70 -0.00964 | * | 0.0154 | * |
71 0.0367 | |* | -0.0264 | * |
72 0.0344 | |* | -0.0284 | * |
73 0.0175 | * | -0.0481 | *| |
74 0.0346 | |* | -0.0272 | * |
75 -0.0194 | * | -0.00666 | * |
76 0.00772 | * | -0.1137 | *| |
77 0.0370 | |* | -0.0236 | * |
78 0.00967 | * | -0.0101 | * |
79 0.1283 | | * | -0.0204 | * |
80 0.00744 | * | -0.0528 | *| |
81 -0.0422 | *| | 0.0517 | |* |
82 -0.0877 | *| | 0.0741 | |* |
83 -0.0991 | * | | -0.0706 | *| |
84 -0.0534 | *| | -0.1319 | *| |
85 0.0894 | | * | 0.0330 | * |
86 -0.0722 | *| | -0.0535 | *| |
87 -0.0986 | * | | 0.0913 | |* |
88 -0.0190 | * | -0.00667 | * |
89 0.00613 | * | -0.00729 | * |
90 0.0331 | |* | 0.0185 | * |
The LOGISTIC Procedure
Regression Diagnostics
educ contam
Case DfBeta (1 unit = 0.05) DfBeta (1 unit = 0.05)
Number Value -8 -4 0 2 4 6 8 Value -8 -4 0 2 4 6 8
46 -0.0128 | * | 0.0343 | |* |
47 -0.0509 | *| | -0.0819 | * | |
48 -0.00707 | * | 0.00568 | * |
49 0.00762 | * | 0.0269 | |* |
50 0.0441 | |* | 0.0371 | |* |
51 -0.00338 | * | 0.0154 | * |
52 0.0280 | |* | -0.0743 | * | |
53 -0.00403 | * | 0.0135 | * |
54 -0.0635 | *| | 0.0193 | * |
55 0.00348 | * | 0.0264 | |* |
56 -0.2690 | * | | 0.1822 | | * |
57 -0.0123 | * | 0.0421 | |* |
58 -0.0126 | * | 0.0270 | |* |
59 0.00267 | * | 0.2279 | | * |
60 -0.0128 | * | 0.0320 | |* |
61 -0.00005 | * | 0.00544 | * |
62 -0.00357 | * | 0.0208 | * |
63 -0.00390 | * | 0.00474 | * |
64 -0.2122 | * | | -0.0753 | * | |
65 0.0677 | |* | -0.3057 | * | |
66 -0.1155 | * | | -0.1692 | * | |
67 0.0332 | |* | -0.0299 | *| |
68 -0.00316 | * | 0.00321 | * |
69 0.1492 | | * | 0.2101 | | * |
70 0.00420 | * | 0.0416 | |* |
71 -0.0437 | *| | 0.0190 | * |
72 -0.0300 | *| | -0.0138 | * |
73 -0.0160 | * | 0.0152 | * |
74 -0.0303 | *| | -0.0144 | * |
75 0.0306 | |* | 0.1766 | | * |
76 0.00737 | * | -0.1581 | * | |
77 -0.0453 | *| | 0.0200 | * |
78 -0.00965 | * | 0.0138 | * |
79 -0.1449 | * | | -0.1515 | * | |
80 -0.00597 | * | 0.0188 | * |
81 0.0842 | | * | -0.0954 | * | |
82 0.0572 | |* | 0.0394 | |* |
83 0.1709 | | * | -0.0904 | * | |
84 0.0731 | |* | 0.0973 | | * |
85 -0.1143 | * | | -0.1918 | * | |
86 0.0740 | |* | 0.0310 | |* |
87 0.0646 | |* | 0.0406 | |* |
88 0.0190 | * | 0.0182 | * |
89 -0.00653 | * | 0.00751 | * |
90 -0.0551 | *| | 0.0288 | |* |
The LOGISTIC Procedure
Regression Diagnostics
hsc nodad
Case DfBeta (1 unit = 0.05) DfBeta (1 unit = 0.05)
Number Value -8 -4 0 2 4 6 8 Value -8 -4 0 2 4 6 8
46 0.0379 | |* | -0.0227 | * |
47 -0.0692 | *| | -0.0373 | *| |
48 0.00547 | * | -0.0144 | * |
49 0.0221 | * | 0.0135 | * |
50 0.0328 | |* | 0.0138 | * |
51 0.0143 | * | -0.0438 | *| |
52 -0.0813 | * | | 8.4E-6 | * |
53 0.0123 | * | -0.0373 | *| |
54 0.0255 | |* | -0.00752 | * |
55 0.0254 | |* | -0.0832 | * | |
56 -0.1085 | * | | 0.3636 | | *|
57 0.0470 | |* | -0.0293 | *| |
58 0.0294 | |* | -0.0168 | * |
59 -0.0732 | *| | -0.1057 | * | |
60 0.0353 | |* | -0.0208 | * |
61 0.00398 | * | -0.0116 | * |
62 0.0164 | * | 0.0119 | * |
63 0.00409 | * | -0.0109 | * |
64 -0.0387 | *| | -0.0788 | * | |
65 -0.3444 | * | | 0.2221 | | * |
66 0.2058 | | * | 0.3801 | | *|
67 0.0840 | | * | -0.0125 | * |
68 0.00272 | * | -0.00694 | * |
69 -0.0953 | * | | -0.0393 | *| |
70 0.0435 | |* | -0.0237 | * |
71 0.0217 | * | -0.00123 | * |
72 0.0541 | |* | -0.0155 | * |
73 0.0128 | * | 0.00720 | * |
74 0.0559 | |* | -0.0163 | * |
75 -0.0631 | *| | -0.0472 | *| |
76 0.0493 | |* | 0.0701 | |* |
77 0.0230 | * | -0.00171 | * |
78 0.0146 | * | -0.00723 | * |
79 0.0756 | | * | 0.0207 | * |
80 0.0146 | * | 0.0111 | * |
81 -0.0974 | * | | -0.0135 | * |
82 0.0356 | |* | 0.0132 | * |
83 -0.1131 | * | | 0.0250 | |* |
84 -0.2951 | * | | 0.1120 | | * |
85 0.0834 | | * | 0.0302 | |* |
86 0.0158 | * | 0.0328 | |* |
87 0.0371 | |* | 0.0127 | * |
88 0.0131 | * | -0.0466 | *| |
89 0.00700 | * | -0.0193 | * |
90 0.0358 | |* | -0.00758 | * |
The LOGISTIC Procedure
Regression Diagnostics
Confidence Interval Displacement C Confidence Interval Displacement CBar
Case (1 unit = 0.04) (1 unit = 0.04)
Number Value 0 2 4 6 8 12 16 Value 0 2 4 6 8 12 16
46 0.00349 |* | 0.00338 |* |
47 0.0441 | * | 0.0434 | * |
48 0.000311 |* | 0.000307 |* |
49 0.00518 |* | 0.00509 |* |
50 0.0128 |* | 0.0125 |* |
51 0.00225 |* | 0.00218 |* |
52 0.0484 | * | 0.0473 | * |
53 0.00166 |* | 0.00161 |* |
54 0.00558 |* | 0.00539 |* |
55 0.00870 |* | 0.00817 |* |
56 0.3324 | * | 0.2711 | * |
57 0.00593 |* | 0.00569 |* |
58 0.00197 |* | 0.00193 |* |
59 0.1313 | * | 0.1219 | * |
60 0.00295 |* | 0.00286 |* |
61 0.000372 |* | 0.000366 |* |
62 0.00459 |* | 0.00448 |* |
63 0.000236 |* | 0.000233 |* |
64 0.1045 | * | 0.0999 | * |
65 0.3555 | * | 0.3370 | * |
66 0.2869 | * | 0.2458 | * |
67 0.0149 |* | 0.0142 |* |
68 0.000125 |* | 0.000123 |* |
69 0.0922 | * | 0.0865 | * |
70 0.00484 |* | 0.00468 |* |
71 0.00351 |* | 0.00343 |* |
72 0.00492 |* | 0.00477 |* |
73 0.00314 |* | 0.00305 |* |
74 0.00515 |* | 0.00499 |* |
75 0.0474 | * | 0.0451 | * |
76 0.0545 | * | 0.0510 | * |
77 0.00378 |* | 0.00370 |* |
78 0.000567 |* | 0.000559 |* |
79 0.0587 | * | 0.0544 | * |
80 0.00436 |* | 0.00425 |* |
81 0.0536 | * | 0.0528 | * |
82 0.0188 |* | 0.0183 |* |
83 0.0909 | * | 0.0884 | * |
84 0.1474 | * | 0.1396 | * |
85 0.0742 | * | 0.0695 | * |
86 0.0179 |* | 0.0171 |* |
87 0.0231 | * | 0.0224 | * |
88 0.00381 |* | 0.00365 |* |
89 0.000514 |* | 0.000505 |* |
90 0.00889 |* | 0.00865 |* |
The LOGISTIC Procedure
Regression Diagnostics
Delta Deviance Delta Chi-Square
Case (1 unit = 0.45) (1 unit = 1.82)
Number Value 0 2 4 6 8 12 16 Value 0 2 4 6 8 12 16
46 0.2039 |* | 0.1088 |* |
47 2.6272 | * | 2.6831 | * |
48 0.0425 |* | 0.0216 |* |
49 0.5097 | * | 0.2921 |* |
50 0.9059 | * | 0.5756 |* |
51 0.1321 |* | 0.0693 |* |
52 2.3474 | * | 2.2056 | * |
53 0.1130 |* | 0.0589 |* |
54 0.3020 | * | 0.1653 |* |
55 0.2465 | * | 0.1347 |* |
56 1.8457 | * | 1.4685 | * |
57 0.2568 | * | 0.1395 |* |
58 0.1557 |* | 0.0818 |* |
59 2.0270 | * | 1.7142 | * |
60 0.1888 |* | 0.1003 |* |
61 0.0406 |* | 0.0207 |* |
62 0.3449 | * | 0.1900 |* |
63 0.0348 |* | 0.0177 |* |
64 2.4107 | * | 2.2752 | * |
65 4.2658 | * | 6.4679 | * |
66 2.0557 | * | 1.7176 | * |
67 0.5431 | * | 0.3169 |* |
68 0.0225 |* | 0.0114 |* |
69 1.7503 | * | 1.3842 | * |
70 0.2550 | * | 0.1380 |* |
71 0.2992 | * | 0.1628 |* |
72 0.2829 | * | 0.1540 |* |
73 0.2217 |* | 0.1186 |* |
74 0.2936 | * | 0.1602 |* |
75 1.3031 | * | 0.9208 | * |
76 1.1541 | * | 0.7870 |* |
77 0.3220 | * | 0.1762 |* |
78 0.0744 |* | 0.0382 |* |
79 1.0982 | * | 0.7397 |* |
80 0.2982 | * | 0.1626 |* |
81 3.1109 | * | 3.6664 | * |
82 1.0319 | * | 0.6783 |* |
83 2.8976 | * | 3.1623 | * |
84 2.6380 | * | 2.6272 | * |
85 1.4728 | * | 1.0865 | * |
86 0.6355 | * | 0.3795 |* |
87 1.1005 | * | 0.7368 |* |
88 0.1600 |* | 0.0850 |* |
89 0.0581 |* | 0.0297 |* |
90 0.5528 | * | 0.3213 |* |
The LOGISTIC Procedure
Regression Diagnostics
Pearson Residual
Covariates
Case (1 unit = 0.67)
Number lived educ contam hsc nodad Value -8 -4 0 2 4 6 8
91 19.0000 16.0000 0 1.0000 1.0000 -0.5571 | *| |
92 18.0000 15.0000 0 0 0 -0.4768 | *| |
93 81.0000 6.0000 0 0 1.0000 -0.1395 | * |
94 6.0000 18.0000 0 1.0000 0 -1.4070 | * | |
95 2.0000 14.0000 0 1.0000 0 0.4431 | |* |
96 1.0000 12.0000 1.0000 1.0000 0 -5.3644 |* | |
97 21.0000 12.0000 1.0000 1.0000 1.0000 0.6584 | |* |
98 21.0000 12.0000 1.0000 1.0000 0 0.2771 | * |
99 68.0000 12.0000 0 0 0 4.2075 | | * |
100 41.0000 12.0000 0 0 0 -0.4059 | *| |
101 34.0000 12.0000 0 0 1.0000 -0.1963 | * |
102 9.0000 12.0000 1.0000 1.0000 0 0.2184 | * |
103 9.0000 12.0000 1.0000 1.0000 0 0.2184 | * |
104 35.0000 15.0000 0 1.0000 0 -1.0635 | * | |
105 6.0000 7.0000 0 0 0 -1.3282 | * | |
106 14.0000 16.0000 0 0 0 -0.4678 | *| |
107 6.0000 16.0000 0 1.0000 0 -1.7128 | * | |
108 20.0000 8.0000 0 0 0 -0.9121 | *| |
109 21.0000 9.0000 0 0 0 1.2339 | | * |
110 19.0000 12.0000 0 0 0 -0.6278 | *| |
111 12.0000 12.0000 0 1.0000 0 0.4438 | |* |
112 10.0000 12.0000 0 0 0 1.3326 | | * |
113 9.0000 12.0000 0 0 0 -0.7655 | *| |
114 8.0000 15.0000 1.0000 0 0 -1.1127 | * | |
115 12.0000 16.0000 0 0 0 -0.4867 | *| |
116 20.0000 12.0000 0 0 0 -0.6155 | *| |
117 17.0000 12.0000 0 0 0 -0.6532 | *| |
118 6.0000 14.0000 1.0000 0 0 0.7829 | |* |
119 13.0000 15.0000 0 0 0 -0.5265 | *| |
120 55.0000 12.0000 0 1.0000 0 -0.9609 | *| |
121 2.0000 12.0000 0 0 0 -0.8794 | *| |
122 53.0000 12.0000 0 0 0 -0.3200 | * |
123 31.0000 13.0000 0 0 1.0000 -0.1888 | * |
124 20.0000 12.0000 0 0 0 -0.6155 | *| |
125 5.0000 14.0000 0 1.0000 0 0.4702 | |* |
126 24.0000 12.0000 0 0 0 -0.5686 | *| |
127 65.0000 9.0000 1.0000 0 1.0000 -0.2729 | * |
128 21.0000 12.0000 0 0 1.0000 -0.2540 | * |
129 28.0000 14.0000 0 0 0 -0.4315 | *| |
130 1.0000 15.0000 1.0000 0 0 0.7823 | |* |
131 1.0000 15.0000 0 0 0 -0.6679 | *| |
132 15.0000 15.0000 1.0000 0 0 1.0325 | | * |
133 5.0000 12.0000 0 0 0 -0.8286 | *| |
134 1.0000 16.0000 0 1.0000 0 0.5288 | |* |
135 30.0000 10.0000 0 1.0000 0 0.5208 | |* |
The LOGISTIC Procedure
Regression Diagnostics
Deviance Residual Hat Matrix Diagonal
Case (1 unit = 0.33) (1 unit = 0.01)
Number Value -8 -4 0 2 4 6 8 Value 0 2 4 6 8 12 16
91 -0.7353 | * | | 0.1219 | * |
92 -0.6401 | * | | 0.0171 | * |
93 -0.1963 | *| | 0.0240 | * |
94 -1.4777 | * | | 0.0743 | * |
95 0.5987 | | * | 0.0331 | * |
96 -2.6053 |* | | 0.0142 | * |
97 0.8487 | | * | 0.1161 | * |
98 0.3847 | |* | 0.0228 | * |
99 2.4202 | | * | 0.0347 | * |
100 -0.5523 | * | | 0.0255 | * |
101 -0.2750 | *| | 0.0203 | * |
102 0.3053 | |* | 0.0164 | * |
103 0.3053 | |* | 0.0164 | * |
104 -1.2301 | * | | 0.0823 | * |
105 -1.4260 | * | | 0.1055 | * |
106 -0.6291 | * | | 0.0216 | * |
107 -1.6550 | * | | 0.0461 | * |
108 -1.1003 | * | | 0.0668 | * |
109 1.3603 | | * | 0.0467 | * |
110 -0.8152 | * | | 0.0163 | * |
111 0.5996 | | * | 0.0330 | * |
112 1.4289 | | * | 0.0217 | * |
113 -0.9604 | * | | 0.0229 | * |
114 -1.2694 | * | | 0.0556 | * |
115 -0.6521 | * | | 0.0223 | * |
116 -0.8015 | * | | 0.0162 | * |
117 -0.8430 | * | | 0.0168 | * |
118 0.9778 | | * | 0.0518 | * |
119 -0.6995 | * | | 0.0177 | * |
120 -1.1437 | * | | 0.1361 | * |
121 -1.0704 | * | | 0.0342 | * |
122 -0.4415 | *| | 0.0319 | * |
123 -0.2647 | *| | 0.0194 | * |
124 -0.8015 | * | | 0.0162 | * |
125 0.6321 | | * | 0.0334 | * |
126 -0.7485 | * | | 0.0165 | * |
127 -0.3791 | *| | 0.0565 | * |
128 -0.3536 | *| | 0.0304 | * |
129 -0.5843 | * | | 0.0171 | * |
130 0.9772 | | * | 0.0604 | * |
131 -0.8589 | * | | 0.0282 | * |
132 1.2047 | | * | 0.0542 | * |
133 -1.0225 | * | | 0.0286 | * |
134 0.7022 | | * | 0.0445 | * |
135 0.6928 | | * | 0.0554 | * |
The LOGISTIC Procedure
Regression Diagnostics
Intercept lived
Case DfBeta (1 unit = 0.06) DfBeta (1 unit = 0.1)
Number Value -8 -4 0 2 4 6 8 Value -8 -4 0 2 4 6 8
91 0.0860 | |* | -0.0175 | * |
92 0.0268 | * | -0.0180 | * |
93 -0.00153 | * | -0.0144 | * |
94 0.2055 | | * | 0.0103 | * |
95 0.0170 | * | -0.0335 | * |
96 -0.2723 | * | | 0.3000 | | * |
97 0.00499 | * | -0.0197 | * |
98 0.00942 | * | 0.000259 | * |
99 -0.2414 | * | | 0.7633 | | *|
100 0.00661 | * | -0.0523 | *| |
101 0.00409 | * | -0.00723 | * |
102 0.0101 | * | -0.00844 | * |
103 0.0101 | * | -0.00844 | * |
104 0.1354 | | * | -0.1987 | * | |
105 -0.4715 |* | | 0.2377 | | * |
106 0.0371 | |* | -0.0117 | * |
107 0.0843 | |* | 0.0587 | |* |
108 -0.2275 | * | | 0.0419 | * |
109 0.2348 | | * | -0.0255 | * |
110 -0.0339 | *| | -0.00571 | * |
111 0.0348 | |* | -0.0176 | * |
112 0.1208 | | * | -0.0781 | *| |
113 -0.0727 | *| | 0.0510 | |* |
114 0.0449 | |* | 0.0536 | |* |
115 0.0366 | |* | -0.00645 | * |
116 -0.0309 | *| | -0.00987 | * |
117 -0.0404 | *| | 0.00337 | * |
118 0.0110 | * | -0.0613 | *| |
119 0.0233 | * | -0.00398 | * |
120 0.0639 | |* | -0.3123 | * | |
121 -0.1103 | * | | 0.1099 | |* |
122 0.0129 | * | -0.0524 | *| |
123 0.00614 | * | -0.00613 | * |
124 -0.0309 | *| | -0.00987 | * |
125 0.0146 | * | -0.0283 | * |
126 -0.0201 | * | -0.0242 | * |
127 0.00583 | * | -0.0402 | * |
128 0.000717 | * | 0.000650 | * |
129 0.0204 | * | -0.0351 | * |
130 -0.00998 | * | -0.0808 | *| |
131 0.00510 | * | 0.0532 | |* |
132 -0.0702 | *| | 0.00915 | * |
133 -0.0930 | * | | 0.0825 | |* |
134 -0.0162 | * | -0.0349 | * |
135 0.0563 | |* | 0.0258 | * |
The LOGISTIC Procedure
Regression Diagnostics
educ contam
Case DfBeta (1 unit = 0.05) DfBeta (1 unit = 0.05)
Number Value -8 -4 0 2 4 6 8 Value -8 -4 0 2 4 6 8
91 -0.0914 | * | | 0.0718 | | * |
92 -0.0374 | *| | 0.0237 | |* |
93 0.00379 | * | 0.00525 | * |
94 -0.2418 | * | | 0.0984 | | * |
95 -0.00914 | * | -0.0188 | * |
96 0.2671 | | * | -0.3704 |* | |
97 -0.0171 | * | 0.0788 | | * |
98 -0.0125 | * | 0.0260 | |* |
99 0.1711 | | * | -0.1582 | * | |
100 -0.00544 | * | 0.0193 | * |
101 -0.00466 | * | 0.00955 | * |
102 -0.0107 | * | 0.0171 | * |
103 -0.0107 | * | 0.0171 | * |
104 -0.1192 | * | | 0.0873 | | * |
105 0.4304 | | *| 0.0492 | |* |
106 -0.0497 | *| | 0.0233 | |* |
107 -0.1186 | * | | 0.0986 | | * |
108 0.2139 | | * | 0.0433 | |* |
109 -0.2164 | * | | -0.0605 | *| |
110 0.0213 | * | 0.0316 | |* |
111 -0.0320 | *| | -0.0188 | * |
112 -0.0783 | *| | -0.0658 | *| |
113 0.0472 | |* | 0.0377 | |* |
114 -0.0700 | *| | -0.2118 | * | |
115 -0.0511 | *| | 0.0244 | |* |
116 0.0193 | * | 0.0310 | |* |
117 0.0256 | * | 0.0328 | |* |
118 0.00672 | * | 0.1479 | | * |
119 -0.0383 | *| | 0.0266 | |* |
120 -0.0166 | * | 0.0863 | | * |
121 0.0724 | |* | 0.0417 | |* |
122 -0.00943 | * | 0.0140 | * |
123 -0.00704 | * | 0.00893 | * |
124 0.0193 | * | 0.0310 | |* |
125 -0.00724 | * | -0.0214 | * |
126 0.0121 | * | 0.0286 | |* |
127 0.00190 | * | -0.0153 | * |
128 -0.00357 | * | 0.0149 | * |
129 -0.0250 | * | 0.0208 | * |
130 0.0339 | |* | 0.1490 | | * |
131 -0.0357 | *| | 0.0342 | |* |
132 0.0846 | | * | 0.1932 | | * |
133 0.0608 | |* | 0.0400 | |* |
134 0.0289 | |* | -0.0276 | *| |
135 -0.0638 | *| | -0.0267 | *| |
The LOGISTIC Procedure
Regression Diagnostics
hsc nodad
Case DfBeta (1 unit = 0.05) DfBeta (1 unit = 0.05)
Number Value -8 -4 0 2 4 6 8 Value -8 -4 0 2 4 6 8
91 -0.0571 | *| | -0.1761 | * | |
92 0.0259 | |* | 0.000285 | * |
93 0.00312 | * | -0.00982 | * |
94 -0.2254 | * | | 0.0176 | * |
95 0.0637 | |* | -0.0138 | * |
96 -0.3904 |* | | 0.1900 | | * |
97 0.0899 | | * | 0.1729 | | * |
98 0.0284 | |* | -0.0161 | * |
99 -0.1144 | * | | -0.1111 | * | |
100 0.0151 | * | 0.0113 | * |
101 0.00857 | * | -0.0249 | *| |
102 0.0183 | * | -0.00946 | * |
103 0.0183 | * | -0.00946 | * |
104 -0.1994 | * | | 0.0629 | |* |
105 0.00917 | * | 0.0782 | | * |
106 0.0276 | |* | -0.00352 | * |
107 -0.2687 | * | | 0.0433 | |* |
108 0.0167 | * | 0.0547 | |* |
109 -0.0315 | *| | -0.0622 | *| |
110 0.0268 | |* | 0.0141 | * |
111 0.0681 | |* | -0.0213 | * |
112 -0.0582 | *| | -0.0245 | *| |
113 0.0335 | |* | 0.0137 | * |
114 0.0820 | | * | 0.0419 | |* |
115 0.0293 | |* | -0.00427 | * |
116 0.0262 | |* | 0.0141 | * |
117 0.0281 | |* | 0.0142 | * |
118 -0.0498 | *| | -0.0338 | *| |
119 0.0298 | |* | -0.00111 | * |
120 -0.2111 | * | | 0.1008 | | * |
121 0.0385 | |* | 0.0122 | * |
122 0.0105 | * | 0.00899 | * |
123 0.00841 | * | -0.0237 | * |
124 0.0262 | |* | 0.0141 | * |
125 0.0700 | |* | -0.0159 | * |
126 0.0238 | * | 0.0139 | * |
127 0.0150 | * | -0.0357 | *| |
128 0.0138 | * | -0.0421 | *| |
129 0.0203 | * | 0.00480 | * |
130 -0.0552 | *| | -0.0252 | *| |
131 0.0411 | |* | -0.00657 | * |
132 -0.0782 | * | | -0.0440 | *| |
133 0.0363 | |* | 0.0130 | * |
134 0.0791 | | * | -0.0112 | * |
135 0.0952 | | * | -0.0406 | *| |
The LOGISTIC Procedure
Regression Diagnostics
Confidence Interval Displacement C Confidence Interval Displacement CBar
Case (1 unit = 0.04) (1 unit = 0.04)
Number Value 0 2 4 6 8 12 16 Value 0 2 4 6 8 12 16
91 0.0490 | * | 0.0431 | * |
92 0.00402 |* | 0.00395 |* |
93 0.000490 |* | 0.000479 |* |
94 0.1717 | * | 0.1590 | * |
95 0.00696 |* | 0.00673 |* |
96 0.4201 | * | 0.4142 | * |
97 0.0644 | * | 0.0569 | * |
98 0.00183 |* | 0.00179 |* |
99 0.6595 | *| 0.6366 | *|
100 0.00442 |* | 0.00431 |* |
101 0.000817 |* | 0.000800 |* |
102 0.000808 |* | 0.000794 |* |
103 0.000808 |* | 0.000794 |* |
104 0.1105 | * | 0.1014 | * |
105 0.2326 | * | 0.2081 | * |
106 0.00494 |* | 0.00483 |* |
107 0.1485 | * | 0.1416 | * |
108 0.0638 | * | 0.0596 | * |
109 0.0783 | * | 0.0746 | * |
110 0.00663 |* | 0.00652 |* |
111 0.00694 |* | 0.00671 |* |
112 0.0403 | * | 0.0394 | * |
113 0.0140 |* | 0.0137 |* |
114 0.0772 | * | 0.0729 | * |
115 0.00551 |* | 0.00539 |* |
116 0.00633 |* | 0.00623 |* |
117 0.00740 |* | 0.00728 |* |
118 0.0353 | * | 0.0335 | * |
119 0.00509 |* | 0.00500 |* |
120 0.1683 | * | 0.1454 | * |
121 0.0284 | * | 0.0274 | * |
122 0.00348 |* | 0.00337 |* |
123 0.000718 |* | 0.000704 |* |
124 0.00633 |* | 0.00623 |* |
125 0.00791 |* | 0.00765 |* |
126 0.00551 |* | 0.00542 |* |
127 0.00473 |* | 0.00446 |* |
128 0.00208 |* | 0.00202 |* |
129 0.00329 |* | 0.00323 |* |
130 0.0419 | * | 0.0394 | * |
131 0.0133 |* | 0.0129 |* |
132 0.0645 | * | 0.0610 | * |
133 0.0208 | * | 0.0202 | * |
134 0.0136 |* | 0.0130 |* |
135 0.0168 |* | 0.0159 |* |
The LOGISTIC Procedure
Regression Diagnostics
Delta Deviance Delta Chi-Square
Case (1 unit = 0.45) (1 unit = 1.82)
Number Value 0 2 4 6 8 12 16 Value 0 2 4 6 8 12 16
91 0.5837 | * | 0.3535 |* |
92 0.4136 | * | 0.2313 |* |
93 0.0390 |* | 0.0199 |* |
94 2.3426 | * | 2.1386 | * |
95 0.3652 | * | 0.2030 |* |
96 7.2016 | *| 29.1911 | *|
97 0.7772 | * | 0.4904 |* |
98 0.1498 |* | 0.0786 |* |
99 6.4939 | * | 18.3396 | * |
100 0.3093 | * | 0.1691 |* |
101 0.0764 |* | 0.0393 |* |
102 0.0940 |* | 0.0485 |* |
103 0.0940 |* | 0.0485 |* |
104 1.6146 | * | 1.2324 | * |
105 2.2416 | * | 1.9722 | * |
106 0.4006 | * | 0.2237 |* |
107 2.8807 | * | 3.0752 | * |
108 1.2703 | * | 0.8915 |* |
109 1.9251 | * | 1.5971 | * |
110 0.6711 | * | 0.4007 |* |
111 0.3662 | * | 0.2036 |* |
112 2.0812 | * | 1.8151 | * |
113 0.9360 | * | 0.5996 |* |
114 1.6842 | * | 1.3110 | * |
115 0.4306 | * | 0.2423 |* |
116 0.6487 | * | 0.3851 |* |
117 0.7180 | * | 0.4340 |* |
118 0.9896 | * | 0.6464 |* |
119 0.4943 | * | 0.2822 |* |
120 1.4535 | * | 1.0687 | * |
121 1.1731 | * | 0.8007 |* |
122 0.1983 |* | 0.1058 |* |
123 0.0707 |* | 0.0363 |* |
124 0.6487 | * | 0.3851 |* |
125 0.4071 | * | 0.2287 |* |
126 0.5656 | * | 0.3287 |* |
127 0.1482 |* | 0.0790 |* |
128 0.1270 |* | 0.0665 |* |
129 0.3447 | * | 0.1894 |* |
130 0.9942 | * | 0.6513 |* |
131 0.7506 | * | 0.4590 |* |
132 1.5123 | * | 1.1271 | * |
133 1.0657 | * | 0.7069 |* |
134 0.5061 | * | 0.2926 |* |
135 0.4959 | * | 0.2872 |* |
The LOGISTIC Procedure
Regression Diagnostics
Pearson Residual
Covariates
Case (1 unit = 0.67)
Number lived educ contam hsc nodad Value -8 -4 0 2 4 6 8
136 11.0000 12.0000 0 0 0 1.3592 | | * |
137 5.0000 16.0000 0 1.0000 0 0.5724 | |* |
138 4.0000 12.0000 0 0 0 -0.8452 | *| |
139 5.0000 16.0000 0 1.0000 0 0.5724 | |* |
140 13.0000 12.0000 0 0 0 -0.7071 | *| |
141 17.0000 12.0000 0 0 0 -0.6532 | *| |
142 2.0000 13.0000 1.0000 0 0 0.6555 | |* |
143 5.0000 16.0000 0 0 0 -0.5592 | *| |
144 1.0000 16.0000 0 0 0 1.6520 | | * |
145 30.0000 12.0000 0 0 0 -0.5048 | *| |
146 1.0000 12.0000 1.0000 0 0 -1.7169 | * | |
147 50.0000 8.0000 0 0 1.0000 -0.2118 | * |
148 2.0000 12.0000 1.0000 0 0 -1.6832 | * | |
149 2.0000 15.0000 0 0 0 -0.6547 | *| |
150 12.0000 18.0000 0 0 0 -0.3998 | *| |
151 7.0000 20.0000 0 0 1.0000 -0.1526 | * |
152 26.0000 12.0000 0 0 1.0000 -0.2300 | * |
153 22.0000 12.0000 0 0 0 1.6904 | | * |
Regression Diagnostics
Deviance Residual Hat Matrix Diagonal
Case (1 unit = 0.33) (1 unit = 0.01)
Number Value -8 -4 0 2 4 6 8 Value 0 2 4 6 8 12 16
136 1.4467 | | * | 0.0207 | * |
137 0.7529 | | * | 0.0456 | * |
138 -1.0383 | * | | 0.0304 | * |
139 0.7529 | | * | 0.0456 | * |
140 -0.9005 | * | | 0.0189 | * |
141 -0.8430 | * | | 0.0168 | * |
142 0.8455 | | * | 0.0530 | * |
143 -0.7376 | * | | 0.0269 | * |
144 1.6225 | | * | 0.0317 | * |
145 -0.6738 | * | | 0.0188 | * |
146 -1.6572 | * | | 0.0543 | * |
147 -0.2963 | *| | 0.0292 | * |
148 -1.6393 | * | | 0.0537 | * |
149 -0.8447 | * | | 0.0267 | * |
150 -0.5446 | * | | 0.0326 | * |
151 -0.2146 | *| | 0.0248 | * |
152 -0.3211 | *| | 0.0257 | * |
153 1.6432 | | * | 0.0162 | * |
The LOGISTIC Procedure
Regression Diagnostics
Intercept lived
Case DfBeta (1 unit = 0.06) DfBeta (1 unit = 0.1)
Number Value -8 -4 0 2 4 6 8 Value -8 -4 0 2 4 6 8
136 0.1175 | | * | -0.0689 | *| |
137 -0.0260 | * | -0.0234 | * |
138 -0.0986 | * | | 0.0913 | |* |
139 -0.0260 | * | -0.0234 | * |
140 -0.0552 | *| | 0.0248 | * |
141 -0.0404 | *| | 0.00337 | * |
142 0.0464 | |* | -0.0778 | *| |
143 0.0331 | |* | 0.0185 | * |
144 -0.0796 | *| | -0.1038 | *| |
145 -0.00749 | * | -0.0394 | * |
146 -0.1919 | * | | 0.2298 | | * |
147 -0.00575 | * | -0.0140 | * |
148 -0.1840 | * | | 0.2152 | | * |
149 0.00725 | * | 0.0469 | * |
150 0.0521 | |* | -0.0133 | * |
151 0.0145 | * | -0.00037 | * |
152 0.00251 | * | -0.00345 | * |
153 0.0720 | |* | 0.0499 | |* |
Regression Diagnostics
educ contam
Case DfBeta (1 unit = 0.05) DfBeta (1 unit = 0.05)
Number Value -8 -4 0 2 4 6 8 Value -8 -4 0 2 4 6 8
136 -0.0760 | *| | -0.0674 | *| |
137 0.0379 | |* | -0.0323 | *| |
138 0.0646 | |* | 0.0406 | |* |
139 0.0379 | |* | -0.0323 | *| |
140 0.0355 | |* | 0.0353 | |* |
141 0.0256 | * | 0.0328 | |* |
142 -0.0305 | *| | 0.1202 | | * |
143 -0.0551 | *| | 0.0288 | |* |
144 0.1549 | | * | -0.0856 | * | |
145 0.00380 | * | 0.0252 | |* |
146 0.1530 | | * | -0.3043 | * | |
147 0.00735 | * | 0.0109 | * |
148 0.1474 | | * | -0.3002 | * | |
149 -0.0362 | *| | 0.0335 | |* |
150 -0.0635 | *| | 0.0193 | * |
151 -0.0170 | * | 0.00626 | * |
152 -0.00426 | * | 0.0126 | * |
153 -0.0444 | *| | -0.0852 | * | |
The LOGISTIC Procedure
Regression Diagnostics
hsc nodad
Case DfBeta (1 unit = 0.05) DfBeta (1 unit = 0.05)
Number Value -8 -4 0 2 4 6 8 Value -8 -4 0 2 4 6 8
136 -0.0593 | *| | -0.0257 | *| |
137 0.0890 | | * | -0.0140 | * |
138 0.0371 | |* | 0.0127 | * |
139 0.0890 | | * | -0.0140 | * |
140 0.0307 | |* | 0.0141 | * |
141 0.0281 | |* | 0.0142 | * |
142 -0.0341 | *| | -0.0300 | *| |
143 0.0358 | |* | -0.00758 | * |
144 -0.1090 | * | | 0.0273 | |* |
145 0.0204 | * | 0.0132 | * |
146 0.0727 | |* | 0.0859 | | * |
147 0.00772 | * | -0.0254 | *| |
148 0.0723 | |* | 0.0860 | | * |
149 0.0400 | |* | -0.00598 | * |
150 0.0255 | |* | -0.00752 | * |
151 0.00776 | * | -0.0190 | * |
152 0.0115 | * | -0.0344 | *| |
153 -0.0714 | *| | -0.0400 | *| |
Regression Diagnostics
Confidence Interval Displacement C Confidence Interval Displacement CBar
Case (1 unit = 0.04) (1 unit = 0.04)
Number Value 0 2 4 6 8 12 16 Value 0 2 4 6 8 12 16
136 0.0398 | * | 0.0390 | * |
137 0.0164 |* | 0.0157 |* |
138 0.0231 | * | 0.0224 | * |
139 0.0164 |* | 0.0157 |* |
140 0.00984 |* | 0.00965 |* |
141 0.00740 |* | 0.00728 |* |
142 0.0254 | * | 0.0240 | * |
143 0.00889 |* | 0.00865 |* |
144 0.0923 | * | 0.0893 | * |
145 0.00497 |* | 0.00488 |* |
146 0.1790 | * | 0.1693 | * |
147 0.00139 |* | 0.00135 |* |
148 0.1698 | * | 0.1607 | * |
149 0.0121 |* | 0.0118 |* |
150 0.00558 |* | 0.00539 |* |
151 0.000607 |* | 0.000592 |* |
152 0.00143 |* | 0.00139 |* |
153 0.0478 | * | 0.0470 | * |
The LOGISTIC Procedure
Regression Diagnostics
Delta Deviance Delta Chi-Square
Case (1 unit = 0.45) (1 unit = 1.82)
Number Value 0 2 4 6 8 12 16 Value 0 2 4 6 8 12 16
136 2.1319 | * | 1.8865 | * |
137 0.5825 | * | 0.3433 |* |
138 1.1005 | * | 0.7368 |* |
139 0.5825 | * | 0.3433 |* |
140 0.8206 | * | 0.5097 |* |
141 0.7180 | * | 0.4340 |* |
142 0.7389 | * | 0.4537 |* |
143 0.5528 | * | 0.3213 |* |
144 2.7217 | * | 2.8186 | * |
145 0.4589 | * | 0.2597 |* |
146 2.9156 | * | 3.1172 | * |
147 0.0891 |* | 0.0462 |* |
148 2.8