Modeling and Multivariate Methods - SAS

200 Performing Logistic Regression on Nominal and Ordinal Responses Chapter 7
The Logistic Fit Report
The Logistic Fit Report
Initially, the Logistic platform produces the following reports:
• Iterations
• Whole Model Test
• Lack of Fit (only if applicable)
• Parameter Estimates
• Effect Likelihood Ratio Tests
You can also request Wald Tests.
All tests compare the fit of the specified model with subset or superset models, as illustrated in Figure 7.1. If
a test shows significance, then the higher order model is justified.
• Whole model tests: if the specified model is significantly better than a reduced model without any
effects except the intercepts.
• Lack of Fit tests: if a saturated model is significantly better than the specified model.
• Effect tests: if the specified model is significantly better than a model without a given effect.
Figure 7.1 Relationship of Statistical Tables
Tests are a comparison of model fits
reduced model
(with only intercepts)
Whole Model Test
Lack-of-Fit Test
(Goodness of Fit G 2 )
specified model
(optional)
Wald Effect Tests
Likelihood-ratio
Effect Tests
(default)
model without
i th effect
saturated model
(a parameter for each unique
combination of x values)
Logistic Plot
If your model contains a single continuous effect, then a logistic report similar to the one in Fit Y By X
appears. See Basic Analysis and Graphing for an interpretation of these plots.