Modeling and Multivariate Methods - SAS

204 Performing Logistic Regression on Nominal and Ordinal Responses Chapter 7
The Logistic Fit Report
Figure 7.4 Lack of Fit Test
The Saturated degrees of freedom is m–1, where m is the number of unique populations. The Fitted
degrees of freedom is the number of parameters not including the intercept. For the Ingots example, these
are 18 and 2 DF, respectively. The Lack of Fit DF is the difference between the Saturated and Fitted models,
in this case 18–2=16.
The Lack of Fit table lists the negative log-likelihood for error due to Lack of Fit, error in a Saturated model
(pure error), and the total error in the Fitted model. Chi-square statistics test for lack of fit.
In this example, the lack of fit Chi-square is not significant (Prob>ChiSq = 0.617) and supports the
conclusion that there is little to be gained by introducing additional variables, such as using polynomials or
crossed terms.
Parameter Estimates
The Parameter Estimates report gives the parameter estimates, standard errors, and associated hypothesis
test. The Covariance of Estimates report gives the variances and covariances of the parameter estimates.