Multivariate Statistical Methods

The aim of the course is concerned with statistical methods for describing and analyzing multivariate data, such as Multivariate descriptive statistics, multivariate normal (MVN) distribution, multivariate analysis of variance (MANOVA) and multivariate regression analysis. This course   provide students with the supporting knowledge necessary for making proper interpretations, selecting appropriate techniques of multivariate statistical methods 

Topics of the course:

Introduction to multivariate analysis. 

Matrix and Vector Algebra. Multivariate Descriptive Statistics. Mean Vectors , Covariance and Correlation Matrices

Multivariate Normal (MVN) distribution. Properties of (MVN). Sampling from (MVN) distribution. The Multivariate Normal Likelihood. The Sampling Distribution of mean sample and S. 

Inference about mean vector. Hotelling's T-square, Confidence Regions and Simultaneous Comparisons of Component Means

Comparison of  Several Multivariate Population Means, Paired Comparisons and a Repeated Measures Design

Testing for Equality of Covariance Matrices, Multivariate Analysis of Variance (MANOVA), One-Way and Two-Way MANOVA

Multivariate Linear Regression Model, Multivariate Multiple Regression

Principal Components analysis