Multivariate Statistical Analysis

Descrpition of aims and contents:

This module aims at (1) familiarizing students with the ideas and methodologies of some multivariate methods together with their applications in data analysis using the SPSS or other computing softwares like R or Excel (2) exploring some of the real-life situations occurring in the fields of agriculture, biology, environment, engineering, industrial experimentation, medicine, social sciences, etc, that can be investigated using multivariate techniques. 

Contents and literature:

             Contents:

1. Introduction to Multivariate Analysis
2. Multivariate distributions
3. Characterizing and displaying multivariate data
4. Multivariate normal distribution and statistical inference based on the multivariate normal distribution
5. Multivariate analysis of variance (MANOVA)
6. Discriminant analysis and Classification Analysis
7. Multivariate Regression and Canonical Correlation Analysis (MR, CCA)
8. Principal Component Analysis (PCA)
9. Factor Analysis and Cluster Analysis

          Literature: 

[1]  ALVIN C. RENCHER and WILLIAM F. CHRISTENSEN. Methods of Multivariate Analysis, Third Edition, John Wiley & Sons, Inc., New York, 2012.

[2]  ALVIN C. RENCHER. Methods of Multivariate Analysis, Second Edition, John Wiley & Sons, Inc., New York, 2002. https://www.ipen.br/biblioteca/slr/cel/0241

[3]  RICHARD A. JOHNSON and DEAN W. WICHERN. Applied Multivariate Statistical Analysis, Sixth Edition, Pearson Prentice Hall, New Jersey, 2007.