This is an introductory multivariate statistical analysis course designed for senior undergraduate students or applied masters in Statistics and Data Science. The objective of the course is to introduce a variety of statistical methods used to analyze multivariate data, with emphasis on the implementation and interpretations of these methods. Topics covered include matrix computation of summary statistics, the multivariate normal distribution, multivariate tests, principal components analysis, multivariate regression analysis, factor analysis, canonical correlation analysis, classification/discrimination, as well as cluster analysis. We will use the R software packages for statistical computing and data analysis during the course.

Mathematical Statistics; Regression Modeling.