Fitting Linear Models • Logistic Regression Models • The Logistic Fit Report • Fit Details
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Fit Details
The Fit Details report contains the following statistics:
Entropy RSquare
Equivalent to RSquare (U). See Whole Model Test.
Generalized RSquare
A measure that can be applied to general regression models. It is based on the likelihood function L and is scaled to have a maximum value of 1. The Generalized RSquare measure simplifies to the traditional RSquare for continuous normal responses in the standard least squares setting. Generalized RSquare is also known as the Nagelkerke or Craig and Uhler R2, which is a normalized version of Cox and Snell’s pseudo R2. See Nagelkerke (1991).
Mean -Log p
The average of -log(p), where p is the fitted probability associated with the event that occurred.
RMSE
The root mean square error, where the differences are between the response and p (the fitted probability for the event that actually occurred).
Mean Abs Dev
The average of the absolute values of the differences between the response and p (the fitted probability for the event that actually occurred).
Misclassification Rate
The rate for which the response category with the highest fitted probability is not the observed category.
For Entropy RSquare and Generalized RSquare, values closer to 1 indicate a better fit. For Mean -Log p, RMSE, Mean Abs Dev, and Misclassification Rate, smaller values indicate a better fit.
To test that the effects as a whole are significant (the Whole Model test), a chi-square statistic is computed by taking twice the difference in negative log-likelihoods between the fitted model and the reduced model that has only intercepts.

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Help created on 9/19/2017