Multivariate analysis (MVA) is based on the statistical principle of multivariate statistics, which involves observation and analysis of more than one statistical outcome variable at a time.

- 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 hypotheses
- Principal Component Analysis
- Factor Analysis,
- Discriminant Analysis
- Cluster analysis
- Path analysis
- Multivariate Analysis of Variance (MANOVA)

- An Introduction to multivariate statistical analysis byCall Number: 519.535 ANDISBN: 9788126524488Publication Date: 2014Published by : John Wiley and Sons (New Delhi)

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- Applied Multivariate Statistical Analysisby Dean W. Wichern and Richard Johnson
- Methods for Statistical Data Analysis of Multivariate Observations, Second Editionby R. Gnanadesikan

ISBN : 9780471161196 - Methods of Multivariate Analysis, Third Editionby Alvin C. Rencher, William F. Christensen

ISBN : 9780470178966

Pages : 781 - Introduction to Multivariate Analysis: Linear and Nonlinear Modelingby Konishi, Sadanori

Publisher : CRC Press - An Introduction to Applied Multivariate Analysisby Tenko Raykov, George A. Marcoulides

ISBN : 0805863753

Pages : 496