About Multivariate Analysis
Introduction; Review of Matrix algebra;Practical examples of multivariate data; Preliminary data analysis; Examination of a data matrix, reduction of a data matrix; definition and calculation of sample summary statistics: means, variances, covariance’s, correlations; Examination and interpretation of sample correlation matrix; the multivariate normal distribution. Study of relationships (association); One-sample test of mean vector; simultaneous confidence intervals for detecting important components; test of structural relationsip; Extension to two-sample tests; principal components and factor analysis as a means of reducing dimensionality: Calculation and interpretation of principal components and common factors.