Multinomial logistic regression: Statistics
The Statistics dialog provides options for specifying the following statistics for your Multinomial logistic regression.
- Overall model
- Statistics for the overall model.
- Pseudo R-square (Cox and Snell, Nagelkerke, McFadden)
- Prints the Cox and Snell, Nagelkerke, and McFadden R 2 statistics.
- Step summary
- This table summarizes the effects entered or removed at each step in a stepwise method. It is not produced unless a stepwise model is specified in the Model dialog.
- Model fitting information
- This table compares the fitted and intercept-only or null models.
- Information criteria
- This table prints Akaike’s information criterion (AIC) and Schwarz’s Bayesian information criterion (BIC).
- Cell probabilities
- Prints a table of the observed and expected frequencies (with residual) and proportions by covariate pattern and response category.
- Classification table
- Prints a table of the observed versus predicted responses.
- Goodness-of-fit
- Prints Pearson and likelihood-ratio chi-square statistics. Statistics are computed for the covariate patterns determined by all factors and covariates or by a user-defined subset of the factors and covariates.
- Monotinicity measures
- Displays a table with information on the number of concordant pairs, discordant pairs, and tied pairs. The Somers' D, Goodman and Kruskal's Gamma, Kendall's tau-a, and Concordance Index C are also displayed in this table.
- Model parameters
- Statistics related to the model parameters.
- Estimates
- Prints estimates of the model parameters, with a user-specified level of confidence.
- Likelihood ratio test
- Prints likelihood-ratio tests for the model partial effects. The test for the overall model is printed automatically.
- Asymptotic correlations
- Prints matrix of parameter estimate correlations.
- Asymptotic covariances
- Prints matrix of parameter estimate covariances.
- Subpopulation for goodness-of-fit statistics
- Allows you to select a subset of the factors and covariates in order to define the covariate patterns used by cell probabilities and the goodness-of-fit tests.
Selecting statistics for Multinomial logistic regression
This feature requires Custom Tables and Advanced Statistics.
- From the menus choose:
- Expand the Additional settings menu and click Statistics.
- Select the statistics you want to include in the output.