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Multivariate methods
Last Updated: 2026-02-05 15:06:30
Abstract
The course teaches multivariate statistical methods such as linear regression, logistic and probit regression, analysis of variance, cluster analysis, and factor analysis.
Objective
Upon completion of this course, the student should have acquired: (1) Knowledge on the foundations of several methods of multivariate data analysis, along with the conditions under which their use is appropriate (2) Skill in the estimation, specification and diagnostics of the various models (3) Hands-on experience with those methods through the use of appropriate software and actual data sets in the PC lab
Content
The course will begin with an introduction to multiple linear regression, in which a metric dependent variable is "explained" by two or more independent variables. The course will continue with the discussion of procedures for the analysis of relationships involving dichotomous or polytomous dependent variables (the choice of a mode of transportation, for example). These procedures include logistic regression and probit analysis. Multivariate methods such as analysis of variance, cluster analysis, and factor analysis will also be covered.
Resources
Literature
Will be announced at the beginning of the course.
General Information
- Language
- English
- Levels
- DS , MSC
- Frequency
- Yearly recurring
Examination
- Type
- session examination
- Mode
- written 60 minutes
- Aids
- None
Course Components
| Type | Title | Time & Place | Hours |
|---|---|---|---|
| lecture | Multivariate methods |
|
2 h weekly |
| exercise | Multivariate methods |
|
1 h weekly |