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Fitting Linear Models > Generalized Regression Models > Generalized Regression Report Window
Publication date: 11/10/2021

Image shown hereGeneralized Regression Report Window

When you click Run in the Fit Model launch window, the Generalized Regression report window that appears contains the following items:

A Model Comparison report that enables you to compare all of the models that have been fit in the report. Each time a new model is fit using the Model Launch control panel, it is added to the Model Comparison report. You can show or hide the reports for fitted models using the check boxes in the Show column. Other columns in the Model Comparison report contain information about each model that was fit, as well as fit statistics for each model. Click on the column headings to sort the models by any of the columns in the Model Comparison report. The first click sorts in ascending order; click the column heading a second time to sort in descending order.

Note: The Model Comparison report appears when one or more models have been fit.

A Model Launch control panel for fitting models. See Model Launch Control Panel. As you fit models, outlines are added with titles that describe the types of models that you have fit. See Model Fit Reports and Model Fit Options.

If there are linear dependencies among the model terms, the Model Launch control panel contains a Singularity Details report that shows the linear functions that the model terms satisfy. See Models with Linear Dependencies among Model Terms.

A Maximum Likelihood report that shows the results of a model that was fit using maximum likelihood estimation. See Maximum Likelihood. The Maximum Likelihood report appears only if the following conditions are met:

There are no linear dependencies among the predictors.

There are more observations than predictors.

There are no more than 250 predictors.

Note: When the specified Distribution is Normal, this report is labeled Standard Least Squares. When the specified Distribution is Binomial, this report is labeled Logistic Regression.

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