The SAS/STAT procedures for discriminant analysis fit 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 for guessing to which class an observation belongs, a set of linear combinations of
the quantitative variables that best reveals the differences among the classes, or a subset of the
quantitative variables that best reveals the differences among the classes.
The SAS/STAT discriminant analysis procedures include the following:
 CANDISC Procedure — Performs a canonical discriminant analysis, computes squared Mahalanobis
distances between class means, and performs both univariate and multivariate oneway analyses of variance
 DISCRIM Procedure — Develops a discriminant criterion to classify each observation into groups
 STEPDISC Procedure — Given a classification variable and several quantitative variables,
the procedure performs a stepwise discriminant analysis to select a subset of the quantitative variables for use in
discriminating among the classes
CANDISC Procedure
The CANDISC procedure performs a canonical discriminant analysis, computes squared Mahalanobis distances between class means,
and performs both univariate and multivariate oneway analyses of variance.
The procedure enables you to do the following:
 display both standardized and unstandardized canonical coefficients
 display correlations between the canonical variables and the original variables as well as the class means for the canonical variables
 test the hypothesis that each canonical correlation and all smaller canonical correlations are zero in the population
 create a data set that contains the canonical coefficients

 create a data set that contains scored canonical variables
 create a data set that corresponds to any output table
 perform BY group processing, which enables you to obtain separate analyses on grouped observations
 perform weighted analysis

For further details, see
CANDISC Procedure
DISCRIM Procedure
Given a set of observations that contains one or more quantitative variables and a classification variable which
indexes groups of observations, the DISCRIM procedure develops a discriminant criterion to classify each observation
into one of the groups. The derived discriminant criterion from this data set can be applied to a second data set
during the same execution of PROC DISCRIM. The following are highlights of the DISCRIM procedure's features:
 when the distribution within each group is assumed to be multivariate normal,
the discriminant function is determined by a parametric method (a measure of generalized squared distance)
 when no assumptions can be made about the distribution within each group, or when the distribution
is assumed not to be multivariate normal, nonparametric methods are used to estimate the groupspecific densities
 nonparametric methods include the kernel and knearestneighbor methods
 uniform, normal, Epanechnikov, biweight, or triweight kernels are used for density estimation
 Mahalanobis or Euclidean distance can be used to determine proximity
 Mahalanobis distance can be based on either the full covariance matrix or the diagonal matrix of variances

 the pooled covariance matrix is used to calculate the Mahalanobis distances with a knearestneighbor method
 individual withingroup covariance matrices or the pooled covariance matrix can be used to calculate the Mahalanobis distances with a kernel method
 posterior probability estimates of group membership for each class can be evaluated
 the performance of a discriminant criterion is evaluated by estimating error rates (probabilities of misclassification) in the classification of future observations
 performs BY group processing, which enables you to obtain separate analyses on grouped observations
 performs weighted analysis
 creates a SAS data set that corresponds to any output table

For further details, see
DISCRIM Procedure
STEPDISC Procedure
Given a classification variable and several quantitative variables, the STEPDISC procedure performs a stepwise discriminant analysis
to select a subset of the quantitative variables for use in discriminating among the classes. The set of variables that make up each
class is assumed to be multivariate normal with a common covariance matrix. The following are highlights of the STEPDISC procedure's
features:
 selection methods include forward selection, backward elimination, and stepwise selection
 variables are chosen to enter or leave the model according to one of two criteria:
 the significance level of an F test from an analysis of covariance, where the variables already
chosen act as covariates and the variable under consideration is the dependent variable
 the squared partial correlation for predicting the variable under consideration from the
CLASS variable, controlling for the effects of the variables already selected for the model

 performs BY group processing, which enables you to obtain separate analyses on grouped observations
 perform weighted analysis
 creates a SAS data set that corresponds to any output table

For further details, see
STEPDISC Procedure