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Complex Samples Logistic Regression
What constitutes a “good” R
2
value varies between different areas of application. While these
statistics can be suggestive on their own, they are most useful when comparing competing models
for the same data. The model with the largest R
2
statistic is “best” according to this measure.
Classification
Figure 20-7
Classification table
The classification table shows the practical results of using the logistic regression model. For each
case, the predicted response is Yes if that case’s model-predicted logit is greater than 0. Cases are
weighted by finalweight, so that the classification table reports the expected model performance in
the population.
Cells on the diagonal are correct predictions.
Cells off the diagonal are incorrect predictions.
Based upon the cases used to create the model, you can expect to correctly classify 85.5% of the
nondefaulters in the population using this m odel. Likewise, you can expect to correctly classify
60.9% of the defaulters. Overall, you can expect to classify 76.5% of the cases correctly; however,
because this table was constructed with the cases used to create the model, these estimates are
likely to be overly optimistic.
Tests of Model Effects
Figure 20-8
Tests of between-subjects effects