Discriminant function analysis: Difference between revisions

Revision as of 16:22, 1 May 2013

Revision as of 18:59, 19 August 2013

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The assumptions of discriminant analysis are the same as those for MANOVA. The analysis is quite sensitive to outliers and the size of the smallest group must be larger than the number of predictor variables.<ref name="buy"/>

*[[NormalityMultivariate normal distribution|Multivariate normality]]: Independent variables are normal for each level of the grouping variable.<ref name="green"/><ref name="buy"/>

*Homogeneity of variance/covariance ([[homoscedasticity]]): Variances among group variables are the same across levels of predictors. Can be tested with Box's M statistic.<ref name="green"/>{{page needed|date=April 2012}} It has been suggested, however, that [[linear discriminant analysis]] be used when covariances are equal, and that [[quadratic classifier#Quadratic discriminant analysis|quadratic discriminant analysis]] may be used when covariances are not equal.<ref name="buy"/>