Introduction: Meaning and Application of Multivariate Analysis.
Multivariate Normal Distribution: Meaning, Properties and uses of Normal Distribution.
Different Multivariate Sampling Distributions:
Multivariate Multiple Regression: Meaning, Functional form and Underlying Assumptions. Likelihood Ratio Test for Regression Parameters, Predicting Multivariate Multiple Regression.
Principal Components: Introduction to the Principal Components Analysis, Sampling Properties of the Sample Principal Components, Statistical Inference.
Factor Analysis: Definition and Purpose of Factor Analysis, the Mathematical Model for Factor Structure, ML Estimators for Random Orthogonal Factors, Testing the Goodness of Fit of the Factor Model. Factor Interpretation.
Cluster Analysis: Meaning and Objectives of Clustering, Different Similarity Measures, Hierarchical Clustering Method, Non-Hierarchical Method.
Discriminant Analysis: Meaning and Goals of Discriminations and Classification, Fisher's Linear Discriminant Function, Classification into One of Two and Into One of More than Two Multivariate Populations.