Applied Multivariate Data Analysis


1. To understand multivariate data and its graphical display
2. To compute measures of central tendency, variance and a ssociation of multivariate data
3. To understand the meaning of linear combination of random variables
4. To understand the multivariate normal distribution and how it is used
5. To understand the properties of sample mean vectors and correlation in multivariate data contexts
6. To understand the role that partial correlation may play in multivariate contexts
7. To understand how data reduction techniques can be used to generate more meaningful interpretation
8. To learn how to perform principal component analysis
9. To learn how to perform factor analysis
10. To learn how to perform canonical correlation analysis
11. To learn how to perform cluster analysis
12. To learn how to perform discriminant analysis

General characterization





Responsible teacher

Docente a designar


Weekly - Available soon

Total - Available soon

Teaching language

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




- Johnson, R.A., and Wichern, D.W. (2007). Applied Multivariate Statistical Analysis. 6th ed. Prentice Hall, New York
- Everitt, B. and Hothorn, T. (2011). An Introduction to Applied Multivariate Analysis with R, Springer

Teaching method

The curricular unit is based on theoretical and practical lessons. A variety of instructional strategies will be applied, including lectures, slide show demonstrations, step-by-step applications (with and without software), questions and answers. The sessions include presentation of concepts and methodologies, solving examples, discussion and interpretation of results. The practical component is geared towards solving problems and exercises, including discussion and interpretation of results. A set of exercises to be completed independently in extra-classroom context is also proposed.

Evaluation method

1st call: project (40%), first round exam (60%)
2nd call: final exam (100%)

Subject matter

1. Basics on multivariate analysis
2. Measures of Central Tendency, Dispersion and Association
3. Linear Combinations of Random Variables
4. Graphical Display of Multivariate Data
5. Multivariate Normal Distribution
6. Sample Mean Vector and Sample Correlation and Related Inference Problems
7. Discriminant Analysis
8. Principal Components Analysis
9. Factor Analysis and Extended Factor Analysis
10. Canonical Correlation Analysis
11. Cluster Analysis (distance-based methods)