Course: Multivariate Analysis
Course type: compulsory
Lecturer: Aleš Žiberna, Ph.D., Associate Professor
| Study programme and level | Study field | Academic year | Semester |
|---|---|---|---|
| Applied statistics, second level | All modules | 1st | 2nd |
For the timeline see Curriculum.
Prerequisites:
- Enrolment into the first year of the programme.
Content (Syllabus outline):
- Graphical representation of multivariate data
- Cluster analysis
- Principal components analysis
- Factor analysis
- Canonical correlation analysis
- Discriminant analysis
- Structural equation modeling
Objectives and competences:
The goal of the course is to introduce modern methods of multivariate analysis, their application on real-life data, and proper interpretation of the obtained results. In the process, students also learn how to use the latest software tools for multivariate analysis.
Intended learning outcomes:
- Understanding basic multivariate approaches, understanding theory based on the applications.
- Applications of multivariate analysis can be found in most of the natural and social sciences. The knowledge obtained in the course is necessary in most of the other courses in the program.
- The ability of abstract thinking.
- Skills to use the literature.
