Multivariate analysis

Subject description

  • Graphical representation of multivariate data
  • Cluster analysis
  • Principal components analysis
  • Factor analysis
  • Structural equation modeling
  • Other multivariate methods

The subject is taught in programs

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.

Teaching and learning methods

Lectures, tutorial, seminar work, consultations

Part of the pedagogical process will be carried out with the help of ICT technologies and the opportunities they offer.

Expected study results

Knowledge and understanding:

  • Understanding basic multivariate approaches, also graphical representation of multivariate data and explorative multivariate analysis,  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

Basic sources and literature

  1. Tabachnick B.G. in Fidell L.S.: Using Multivariate Statistics. Pearson/Allyn & Bacon., Boston.  2007 (Peta izdaja)
  2. Kaplan D.: Structural Equation Modeling, Foundations and Extensions. Sage, Thousand Oaks, London, New Delhi, 2000.
  3. Johnson R.A. in Wichern D.W.: Applied Multivariate Statistical Analysis, 6th international edition. Pearson Education International, Upper Saddle Rive, 2007.
  4. Härdle W., Simar L.: Applied multivariate statistical analysis, 2nd ed. Springer, Berlin,   Heidelberg, New York, 2007.
  5. Ferligoj A.: Razvrščanje v skupine. Metodološki zvezki, 4, FSPN, Ljubljana, 1989.
  6. Omladič V.: Uporaba linearne algebre v statistiki. Metodološki zvezki, 13, FDV, Ljubljana, 1997.

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