Multivariate data. Data Matrices and Measurement Scales. Multivariate Random Variables and Samples. The Multivariate Normal Distribution. Sampling from Multivariate Normal and Statistical Inference.
One-way MANOVA for independent and dependent samples and related controls. Generalization of linear regression and its application in the interpretation-prediction of more than one dependent variables.
Principal Components Analysis. Finding the main components resulting from the analysis of tables (covariances and correlations respectively). Sample core components and statistical inference using large data samples.
Correspondence Analysis. Study of analysis in matrix tables (dual input tables).
Discriminant Analysis and Classification. Study of group separation rules. Hierarchical and Nonhierarchical Clustering Methods.