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CASt online resources

Multivariate statistics
Applied Multivariate Statistical Analysis
    by R. A. Johnson & D. W. Wichern (5th ed, 2002).  Well-respected and comprehensive textbook at the undergraduate level.  Covers multivariate normal distributions, likelihood ratio method, k-sample comparisons, linear regression models, principal componients, factor analysis, structural equations, canonical correlation, discrimination, hierarchical and non-hierarchical clustering, multidimensional scaling and correspondance analysis.

Applied Linear Regression Models
    by Michael H. Kutner, John Neter & Christopher J. Nachtsheim  (4th ed, 2004).  Well-respected undergraduate textbook.  Covers bivariate linear regression, residual diagnostics, multiple regression, autocorrelation and correlation models, polynomial regression, nonlinear regression, model selection and validation.

An R and S-PLUS Companion to Multivariate Analysis
    by Brian Everitt (2005). Practical and informative cookbook for multivariate analysis with R including summary statistics, visualization, principal components analysis, factor analysis, multidimensional scaling and correspondence analysis, clustering, discriminant analysis and MANOVA, and multiple regression.

Multivariate Statistical Methods
    by Donald F. Morrison (4th ed, 2005). Text developed for business school students.  Includes multivariate normal populations, tests on means, MANOVA, classification by discriminant analysis, covariance matrices, principal components, and factor analysis.

Classical and Modern Regression with Applications
    by Raymond H. Myers (2nd ed., 1990). Undergraduate/ graduate-level text for statistics students.  Covers simple & multiple linear regression, best model criteria, residual & influence diagnostics, non-standard conditions (measurement errors & weighting, Poisson, GLM, outliers), multicollinearity, and nonlinear regression.

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