Introduction to Discriminant Procedures

Overview

The SAS procedures for discriminant analysis treat data with one classification variable and several quantitative variables. The purpose of discriminant analysis can be to find one or more of the following:

a mathematical rule, or discriminant function, for guessing to which class an observation belongs, based on knowledge of the quantitative variables only
a set of linear combinations of the quantitative variables that best reveals the differences among the classes
a subset of the quantitative variables that best reveals the differences among the classes

The SAS discriminant procedures are as follows:

DISCRIM: computes various discriminant functions for classifying observations. Linear or quadratic discriminant functions can be used for data with approximately multivariate normal within-class distributions. Nonparametric methods can be used without making any assumptions about these distributions.
CANDISC: performs a canonical analysis to find linear combinations of the quantitative variables that best summarize the differences among the classes.
STEPDISC: uses forward selection, backward elimination, or stepwise selection to try to find a subset of quantitative variables that best reveals differences among the classes.

Background

Example: Contrasting Univariate and Multivariate Analyses

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