Course Contents

Multivariate Normal Distribution, Wishart distribution and their properties, Hotelling’s T^2 Distribution, Methods of Estimation; Maximum Likelihood and least squares, Multivariate Hypothesis testing, Likelihood ratio test, One sample and multi-sample hypothesis. Principal Component Analysis, Factor Analysis, Discriminant Analysis. Canonical Correlation, Cluster analysis, Path analysis, Multivariate Analysis of variance (MANOVA).

Course Synopsis

To impart the conceptual and advanced knowledge of multivariate data. To teach various advanced techniques to handle the challenges presented by these data. To develop sound knowledge of multivariate theories and its application in different fields.

Course Learning Outcomes

On completion of the course students should be able to: • Understand multivariate statistical analysis, both theory and methods. • Have an understanding of the link between multivariate techniques and corresponding univariate techniques. • Undertake multivariate hypothesis tests, and draw appropriate conclusions. • Recognition of the variety of advanced multivariate techniques and their proficient applications. • Development of the skill to summarize, analyze and interpret the multivariate data. • Use different softwares to analyze multivariate data.