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This is one of the best books on Multivariate Statistics thta I have ever read. I strongly recomend it to any scientist interested in multivariate statistis.
Review: Multivariate Analysis: Methods and ApplicationsUser Review - Greg - Goodreads
This book strikes a really good balance between theory and applications. There is just enough theory for someone to implement an algorithm, when it isn't commonly available in software already ... Read full review
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algorithm approach associated assumptions canonical correlation analysis canonical variate causal Chapter cluster column common factors computed conditional probabilities coordinates correlation matrix corresponding covariance matrix criterion data matrix defined deletion denoted derived space diagonal dimension dimensional discriminant analysis discriminant function discussed distance effects eigenvalues eigenvectors elements endogenous variables equation Euclidean distance example F-value factor analysis Figure given independent variables indicated individual KSI 2 KSI LAMBDA latent class model maximum likelihood mean measures method multiple multiple discriminant analysis multivariate normal Note null hypothesis objects observed variables obtained orthogonal overidentified parameter estimates posterior probability predictor variables principal components analysis probability problem procedure regression analysis regression coefficients regression model relationship residuals restrictions rotation sample scores shown similarity solution squared standard statistically significant stimulus space structure sums-of-squares Table techniques test statistic variance variance-covariance matrix vector zero