The Score Summaries report provides an overview of the discriminant scores. The table in Figure 4.12 shows Actual and Predicted classifications. If all observations are correctly classified, the off-diagonal counts are zero.
Figure 4.12 Score Summaries for Iris.jmp
If you used Stepwise Variable Selection to construct the model, the columns entered into the model are listed. See Figure 4.6.
Twice the negative log-likelihood of the observations in the training set, based on the model. Larger values indicate better fit. Provided for the training set only. For more details, see the Fitting Linear Models book.
Shows matrices of actual by predicted counts for each level of the categorical X. If you are using JMP Pro with validation, a matrix is given for each set of observations. If you are using JMP with excluded rows, the excluded rows are considered the validation set and a separate Validation matrix is given. For more information, see Validation in JMP and JMP Pro.
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