Multivariate Statistics
Information
Teachers: Adelaide Freitas
Duration: One semester
Work hours: 162
Contact hours: 45
ECTS: 6
Scientific area: Mathematics
Objectives
- Present the main results of statistical inference in Normal Multivariate populations.
- Introduce multivariate data analysis techniques, focusing on methodologies, application conditions and interpretation of results.
- Train the use of statistical programs for the treatment of multivariate data, exploring the potential of the R statistical program and encouraging its use with the critical analysis of the results.
Learning Outcomes
- Ability to discuss multivariate data analysis methodologies, reduce information and interpret results.
- Recognize properties of the multivariate normal distribution
Requirements
- Initial training courses in Probability and Statistics.
- Basic knowledge of vector calculus and matrix algebra.
Grading
- 40.00% Pratical assignments
- 60.00% Written exams
Methodology
The program is developed in blocks according to the chapters of the subject. The blocks are made up of sessions to present the methodology, followed by practical classes in a computer laboratory and the preparation of assignments/exercises by the students.
Contents
- Introduction to Multivariate Analysis
- Principal Component Analysis
- Factor Analysis
- Cluster Analysis
- Multivariate Normal Distribution
- Discriminant Analysis and Classification
Main book
-
Applied multivariate statistical analysis, 4th editionJohnson, R.A. e Wichern, D.W.1998
Recommended reading
- Applied multivariate statistical analysis, 4th edition. Johnson, R.A. e Wichern, D.W.1998
- Anderson, T. W. (2003) An introduction to multivariate statistical analysis, John Wiley & Sons.
- Everitt, B. and Hothorn, T. (2010). A handbook of statistical analyses using R. CRC Press.
- Johnson, R.A. e Wichern, D.W. (2007). Applied multivariate statistical analysis. 6th edition. Prentice-Hall.
- Rencher, A., Christensen, W. (2012) Methods of multivariate analysis, John Wiley.
- Sharma. (1995). Applied multivariate Technique. Wiley.