# Applied Multivariate Analysis

Por: Swayam . en: ,

## Overview

The Applied Multivariate course caters to two objectives. On one hand it extends the usual univariate statistical methods to several dependent variables. On the other, it develops techniques which are peculiar to several variable studies only. In the former the discussions are on multivariate distributions, multivariate testing, multivariate linear models, including multivariate regression and multivariate ANOVA. The latter introduces grouping techniques like Cluster Analysis and Classification problems, dimension reduction methods like principal Component Analysis and Factor Analysis, and several such topics. The course is a mix of basic theory and their applications and is likely to be useful for both postgraduate students of Statistics and also students of other disciplines who uses statistical methods for their analyses.

## Syllabus

### COURSE LAYOUT

Week Module 1 1 Introduction to Multivariate Analysis 2 Multivariate Distributions 3 Multivariate Normal Distribution and Related Results 12 4 Multivariate Normal Distribution and Related Results 2 5 Multivariate Normal Distribution and Related Results 3 6 Multivariate Normal Distribution and Related Results 43 7 Classification of Individuals 8 Cluster Analysis 1 9 Cluster Analysis 24 10 Cluster Analysis 3 11 Cluster Analysis 4 12 Cluster Analysis 55 13 Discriminant Analysis and Classification 1 14 Discriminant Analysis and Classification 2 15 Discriminant Analysis and Classification 36 16 Discriminant Analysis and Classification 4 17 Principal Components Analysis 1 18 Principal Components Analysis 27 MID TERM ASSESSMENT8 19 Principal Components Analysis 3 20 Principal Components Analysis 4 21 Principal Components Analysis 59 22 Factor Analysis 1 23 Factor Analysis 2 24 Factor Analysis 310 25 Factor Analysis 4 26 Factor Analysis 5 27 Canonical Correlations 111 28 Canonical Correlations 2 29 Multidimensional Scaling 30 Correspondance Analysis12 31 Multivariate Linear Models 1 32 Multivariate Linear Models 2 33 Multivariate Linear Models 313 FINAL ASSESSMENT