Classification Trees Startup Panel - Quick Tab

From the first list, select the dependent variable for the analysis. The dependent variable must contain text or numeric codes identifying the class, or group, to which each case or object belongs.

From the second list, select any categorical predictors for the analysis. Like the dependent variable, categorical predictor variables must contain text or numeric codes identifying the group to which each case or object belongs.

From the third list, select any ordered predictors (measured on at least an ordinal-level scale) for the analysis. Note that at least one categorical or ordered predictor variable must be specified for the analysis.

From the fourth list, an optional sample identifier variable can be selected. A sample identifier variable is used when the data file contains both a learning sample, from which the classification tree for the dependent variable is computed, and a test sample, which is used to test the predictive accuracy of the classification tree computed from the learning sample. The sample identifier variable must contain codes identifying the sample (learning or test) to which each case or object belongs.

Note: missing data. Statistica will apply casewise deletion of missing data; that is, cases will be deleted from the analysis if there are missing data on that case for any of the variables specified for the analysis. Thus, be careful when there are missing data present in the selected variables; the results for those variables without missing data may not be based on all available information (namely, cases where some other variable had missing data, causing the entire case to be dropped from the analysis).