Abstract
Chapters I, II, and III examine topics in multivariate analysis. Specifically, they discuss multivariate linear models, discriminant analysis, principal components, and factor analysis. The basic ideas behind these subjects are closely related to linear model theory. Multivariate linear models are simply linear models with more than one dependent variable. Discriminant analysis is closely related to both Mahalanobis’s distance (cf. Christensen, 1987, Section XIII.1) and multivariate one-way analysis of variance. Principal components are user-constructed variables which are best linear predictors (cf. Christensen, 1987, Section VI.3) of the original data. Factor analysis has ties to both multivariate linear models and principal components.KeywordsProfile AnalysisMultivariate Normal DistributionGrowth Curve ModelFull Column RankLikelihood Ratio Test StatisticThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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