Includes bibliographical references (pages 352-368).

The multivariate normal distribution -- Estimation of the mean vector and the covariance matrix -- The distributions and uses of sample correlation coefficients -- The generalized Tp2s-statistic -- Classification of observations -- The distribution of the sample covariance matrix and the sample generalized variance -- Testing the general linear hypotheses; analysis of variance -- Testing independence of sets of variates -- Testing hypotheses of equality of covariance matrices and equality of mean vectors and covariance matrices -- Principal components -- Canonical correlations and canonical variables -- The distribution of certain characteristic roots and vectors that do no depend on parameters -- A review of some other work in multivariate analysis -- Matrix theory. The multivariate normal distribution; Estimation of the mean vector and the covariance matrix; The distributions and uses of sample correlation coefficients; The generalized T2 statistic; Classification of observations; The distribution of the sample covariance matrix and the sample generalized variance; Testing the general linear hypothesis; analysis of variance; Testing independence of sets of variates; Testing hypotheses of equality of covariance matrices and equality of mean vectors and covariance matrices; Principal components; Canonical correlation and canonical variables; The distribution of certain characteristic roots and vectors that do not depend on parameters; A review of some other work in multivariate analysis.