LOGISTIC REGRESSION PROBLEM USING SAS
For the model with the Intercept only: −2LL = 219.300
For the model with predictors: −2LL = 160.278
Logistic Regression Estimates 

Odds ratio 

Variable (coefficient) 
β(SE) 
Wald chisquare test 
p value 
Estimate 
95% CI 
Aptitude (β1) 
.138(.028) 
23.376 
.000 
1.148 
[1.085, 1.213] 
Award (β2) 
3.062(.573) 
28.583 
.000 
21.364 
[6.954, 65.639] 
Age (β3) 
1.307(.793) 
2.717 
.099 
3.694 
[.781, 17.471] 
Constant 
22.457(8.931) 
6.323 
.012 
.000 
Cases Having Standardized Residuals > 2 

Case 
Observed Outcome 
Predicted Probability 
Residual 
Pearson 
22 
0 
.951 
.951 
4.386 
33 
1 
.873 
.873 
2.623 
90 
1 
.128 
.872 
2.605 
105 
0 
.966 
.966 
5.306 
Classification Results (With Cut Value of .05) 

Predicted 

Observed 
Dropped out 
Completed 
Total 
Percent correct 
Dropped out 
50 
20 
70 
71.4 
Completed 
11 
79 
90 
87.8 
Total 
80.6 

Complete the following:
Principal Component and Factor Analysis Problem Using SAS
Unrotated Solution 
Varimax Solution 

Variables 
Comp 1 
Comp 2 
Comp 1 
Comp 2 
1 
.581 
.806 
.016 
.994 
2 
.767 
.545 
.941 
.009 
3 
.672 
.726 
.137 
.980 
4 
.932 
.104 
.825 
.447 
5 
.791 
.558 
.968 
.006 
Items Correlations for the ReactionstoTests Scales 

1 
2 
3 
4 
5 
6 
7 
8 
9 
10 
11 
12 

Ten1 
1.000 

Ten2 
.657 
1.000 

Ten3 
.652 
.660 
1.000 

Wor1 
.279 
.338 
.300 
1.000 

Wor2 
.290 
.330 
.350 
.644 
1.000 

Wor3 
.358 
.462 
.440 
.659 
.566 
1.000 

Tirt1 
.076 
.093 
.120 
.317 
.313 
.367 
1.000 

Tirt2 
.003 
.035 
.097 
.308 
.305 
.329 
.612 
1.000 

Tirt3 
.026 
.100 
.097 
.305 
.339 
.313 
.674 
.695 
1.000 

Body1 
.287 
.312 
.459 
.271 
.307 
.351 
.122 
.137 
.185 
1.000 

Body2 
.355 
.377 
.489 
.261 
.277 
.369 
.196 
.191 
.197 
.367 
1.000 

Body3 
.441 
.414 
.522 
.320 
.275 
.383 
.170 
.156 
.101 
.460 
.476 
1.000 
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