In the Logistic Fit report, the Whole Model Test table shows tests that compare the whole-model fit to the model that omits all the regression parameters except the intercept parameters. The test is analogous to the Analysis of Variance table for continuous responses. The negative log-likelihood corresponds to the sums of squares, and the chi-square test corresponds to the F test.
The Whole Model Test table shows these quantities:
Model
The model labels.
Difference
The difference between the Full model and the Reduced model. This model is used to measure the significance of the regressors as a whole to the fit.
Full
The complete model that includes the intercepts and all effects.
Reduced
The model that includes only the intercept parameters.
–LogLikelihood
The negative log-likelihood for the respective models. See “Likelihood, AICc, and BIC”.
DF
The degrees of freedom (DF) for the Difference between the Full and Reduced model.
Chi-Square
The likelihood ratio chi-square test statistic for the hypothesis that all regression parameters are zero. The test statistic is computed by taking twice the difference in negative log-likelihoods between the fitted model and the reduced model that has only intercepts.
Prob>ChiSq
The probability of obtaining a greater chi-square value if the specified model fits no better than the model that includes only intercepts.
RSquare (U)
The proportion of the total uncertainty that is attributed to the model fit, defined as the ratio of the Difference to the Reduced negative log-likelihood values. RSquare ranges from zero for no improvement in fit to 1 for a perfect fit. An RSquare (U) value of 1 indicates that the predicted probabilities for events that occur are equal to one: There is no uncertainty in predicted probabilities. Because certainty in the predicted probabilities is rare for logistic models, RSquare (U) tends to be small.
RSquare (U) is sometimes referred to as U, the uncertainty coefficient, or as McFadden’s pseudo R2.
AICc
The corrected Akaike Information Criterion. See “Likelihood, AICc, and BIC”.
BIC
The Bayesian Information Criterion. See “Likelihood, AICc, and BIC”.
Observations (or Sum Weights)
Total number of observations in the sample. If a Freq or Weight column is specified in the Fit Model window, this value is the sum of the values of a column assigned to the Freq or Weight role.