Discriminant function analysis

Discriminant function analysis is a statistical analysis to predict a categorical dependent variable by one or more continuous or binary independent variables. It is different from an ANOVA or MANOVA, which is used to predict one (ANOVA) or multiple (MANOVA) continuous dependent variables by one or more independent categorical variables instead. Discriminant function analysis is useful in determining whether a set of variables is effective in predicting category membership.

Moreover, it is a useful follow-up procedure to a MANOVA instead of doing a series of one-way ANOVAs, for ascertaining how the groups differ on the composite of dependent variables.

In simple terms, discriminant function analysis is classification - the act of distributing things into classes or categories of the same type.

See also

• Statistical classification
• Linear discriminant analysis
• Multiple discriminant analysis

External links

• Course notes, Discriminant function analysis by G. David Garson, NC State University
• Course notes, Discriminant function analysis by David W. Stockburger, Missouri State University

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