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  3. Post-Graduation in Statistical Systems
  4. Postgraduate Program in Statistical Systems with a specialization in Central Banks
  5. Multivariate Data Analysis

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

Code

400013

Credits

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.    Introduction to Multivariate Statistics Data Analysis
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
8.    Discriminant analysis
9.    Multidimensional scaling
10.    Repeated measures analysis