Discriminant analysis is a method of predicting some level of a one-way classification based on known values of the responses. The technique is based on how close the measurement variables are to the multivariate means of the levels being predicted. Discriminant analysis is more fully implemented using the Discriminant Platform (see Discriminant Analysis in the Multivariate Methods book).

In JMP, you specify the measurement variables as Y effects and the classification variable as a single X effect. The multivariate fitting platform gives estimates of the means and the covariance matrix for the data, assuming that the covariances are the same for each group. You obtain discriminant information with the Save Discrim option in the pop-up menu next to the MANOVA platform name. This command saves distances and probabilities as columns in the current data table using the initial E and H matrices.

For a classification variable with k levels, JMP adds k distance columns, k classification probability columns, the predicted classification column, and two columns of other computational information to the current data table.