Multivariate Analysis

Subject description

  • Graphical representations of multivariate data
  • Multiple regression
  • Cluster analysis
  • Principal component analysis
  • Factor analysis
  • Structural equation modeling
  • Other methods based on available time:
    • Canonical correlation analysis
    • Discriminant analysis
    • Multidimensional scaling
    • Corespondence analysis
  • Overview of some other multivariate methods

The subject is taught in programs

Objectives and competences

In the framework of this course students familiarise themselves with the methods of multivariate analysis, their application in the analysis of the real-life data, and the interpretation of the obtained results. The students also learn to use the latest software tools applicable in multivariate analysis.

Teaching and learning methods

Lectures and seminars.

Expected study results

Knowledge and understanding: Knowledge of methods of multivariate analysis, the ability of their use and evaluation of their results. Application: Use in advanced statistical data analysis. Reflection: Learning and understanding the connection between theory and empirical approach in solving specific research problems raised in various scientific disciplines.

Basic sources and literature

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

Stay up to date

University of Ljubljana, Faculty of Electrical Engineering Tržaška cesta 25, 1000 Ljubljana

E: T:  01 4768 411