Statistics - Multivariate Exploratory Techniques - Discriminant Analysis

Ribbon bar. Select the Statistics tab. In the Advanced/Multivariate group, click Mult/Exploratory and on the menu, select Discriminant to display the Discriminant Function Analysis Startup Panel.

Classic menus. On the Statistics - Multivariate Exploratory Techniques submenu, select Discriminant Analysis to display the Discriminant Function Analysis Startup Panel.

Discriminant function analysis is used to determine which variables discriminate between two or more naturally occurring groups (it is used as either a hypothesis testing or exploratory method).

The Discriminant Analysis module is a full implementation of multiple stepwise discriminant function analysis. STATISTICA will perform forward or backward stepwise analyses, or enter user-specified blocks of variables into the model. In addition to the numerous graphics and diagnostics describing the discriminant functions, STATISTICA also provides a wide range of options and statistics for the classification of old or new cases (for validation of the model). The output includes the respective Wilks' Lambdas, partial Lambdas, F to enter (or remove), the p-values, the tolerance values, and the R-square. STATISTICA will perform a full canonical analysis and report the raw and cumulative eigenvalues for all roots, and their p-values, the raw and standardized discriminant (canonical) function coefficients, the structure coefficient matrix (of factor loadings), the means for the discriminant functions, and the discriminant scores for each case (which can also be automatically appended to the data file). Integrated graphs include histograms of the canonical scores within each group (and all groups combined), special scatterplots for pairs of canonical variables (where group membership of individual cases is visibly marked), a comprehensive selection of categorized (multiple) graphs allowing you to explore the distribution and relations between dependent variables across the groups (including multiple box-and-whisker plots, histograms, scatterplots, and probability plots), and many others. The Discriminant Analysis module will also compute the standard classification functions for each group. The classification of cases can be reviewed in terms of Mahalanobis distances, posterior probabilities, or actual classifications.

STATISTICA also includes the General Discriminant Analysis Models module for fitting ANOVA/ANCOVA-like designs to categorical dependent variables, and to perform various advanced types of analyses (e.g., best-subset selection of predictors, based on misclassification rates in an independent validation sample; profiling of posterior probabilities, etc.).