Multivariate Linear Models

  • Ronald Christensen
Part of the Springer Texts in Statistics book series (STS)


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.


Profile Analysis Growth Curve Model Full Column Rank Likelihood Ratio Test Statistic Heart Rate Data 
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Copyright information

© Springer Science+Business Media New York 2001

Authors and Affiliations

  • Ronald Christensen
    • 1
  1. 1.Department of Mathematics and StatisticsUniversity of New MexicoAlbuquerqueUSA

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