# Multivariate Data Analysis

## Objectives

This course covers techniques of multivariate statistical analysis. Students should understand underlying theory for the analysis of multivariate data developing ability to:

• Choose appropriate procedures for multivariate analysis
• Use R to carry out analyses
• Interpret the output of such analyses
They should also have knowledge of the advantages, limitations and conditions for the use of various data analysis methods presented by discipline.

## General characterization

400013

6.0

##### Responsible teacher

Jorge Morais Mendes

##### Hours

Weekly - Available soon

Total - Available soon

##### Teaching language

Portuguese. If there are Erasmus students, classes will be taught in English

### Prerequisites

Statistics and linear algebra (recomended)

### Bibliography

• Everitt, B. and Hothorn, T. (2011). An Introduction to Applied Multivariate Analysis with R, Springer
• Johnson, R.A and Winchern (2007), D. W., Applied Multivariate Statistical Analysis, 6th edition, Pearson Prentice Hall
• Sharma, S., (1996) Applied Multivariate Techniques, John Wiley & Sons
• Timm, N. H., (2002) Applied Multivariate Analysis, Springer
• Jr., W. C. Black, B. J., Hair, J. F. (2013). Multivariate Data Analysis-Pearson, 7th edition, Education Limited.

### Teaching method

The course is based on theoretical and practical classes. The classes are aimed at solving problems and exercises

### Evaluation method

• (60%) Final exam (1st or 2nd round dates)
• (40%) Project

Remarks:
1. A minimum grade of 9.5 points is required in final exam

## Subject matter

1.    Multivariate Data Analysis basics
2.    Fundamentals on data manipulation – introducing R software
3.    Graphical representation of multivariate data
4.    Multivariate normal distribution
5.    Principal components analysis
6.    (Exploratory) Factor Analysis
7.    Cluster analysis
9.    Correspondence analysis

## Programs

Programs where the course is taught: