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