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
General characterization
Code
400013
Credits
6.0
Responsible teacher
José António de Almeida Pinheiro
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
Programs
Programs where the course is taught: