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Multivariate General Linear Models
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Multivariate General Linear Models



November 2011 | 224 pages | SAGE Publications, Inc
This book provides an integrated introduction to multivariate multiple regression analysis (MMR) and multivariate analysis of variance (MANOVA). Beginning with an overview of the univariate general linear model, this volume defines the key steps in analyzing linear model data and introduces multivariate linear model analysis as a generalization of the univariate model. Richard F. Haase focuses on multivariate measures of association for four common multivariate test statistics, presents a flexible method for testing hypotheses on models, and emphasizes the multivariate procedures attributable to Wilks, Pillai, Hotelling, and Roy. The volume concludes with a discussion of canonical correlation analysis that is shown to subsume all the multivariate procedures discussed in previous chapters. The analyses are illustrated throughout the text with three running examples drawing from several disciples, including personnel psychology, anthropology, environmental epidemiology, and neuropsychology.
 
1. Introduction and Review of Univariate General Linear Models
 
2. Specifying the Structure of the Multivariate General Linear Model
 
3. Estimating the Parameters of the Multivariate General Linear Model
 
4. Partitioning the SSCP, Measures of Strength of Association, and Test Statistics in the Multivariate General Linear Model
 
5. Testing Hypotheses in the Multivariate General Linear Model
 
6. Coding the Design Matrix and the Multivariate Analysis of Variance
 
7. The Eigenvalue Solution to the Multivariate General Linear Model: Canonical Correlation and Multivariate Test Statistics
 
References
Key features

Key Features:

  • Provides an integrated introduction to multivariate techniques of MMR and MANOVA in a single framework, emphasizing that both regression and ANOVA models are part of the same analytic model
  • Adopts a four step process of analyzing linear models—specifying the model, estimating the parameters, developing measures of strength of association, and developing tests of hypotheses
  • Emphasizes the ability to generalize familiar concepts in the univariate domain to multivariate analyses, showing how the ideas are conceptually identical

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