Abstract
Chapters 1, 2, and 3 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 (see Christensen, 1996a, Section 13.1) and multivariate one-way analysis of variance. Principal components are user-constructed variables which are best linear predictors (see Christensen, 1996a, Section 6.3) of the original data. Factor analysis has ties to both multivariate linear models and principal components.KeywordsProfile AnalysisGrowth Curve ModelFull Column RankLikelihood Ratio Test StatisticHeart Rate DataThese 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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