Topic outline

  • General

    This course is an introduction to multivariate statistical analysis. The goal is to learn basics of common multivariate data analysis techniques and to use the methods in practice. Software R is used in the exercises of this course. The topics of the course are multivariate location and scatter, principal component analysis, bivariate correspondence analysis, multiple correspondence analysis, canonical correlation analysis, discriminant analysis, classification, and clustering.  

    About the exam: In the exam you may have a basic calculator (no graphical calculators), your pens and pencils, a ruler and an eraser. On top of that you may have one A4 of notes. The rules for the note are: size A4, text on one side only, it must be hand-written, your name has to be on the top right corner of the note. Other materials, such as formulae books,  are not allowed in the exam. Please take an id-card with you to the exam. The exam organizers do not know you.

    Lecture 1: Introduction, practical things, Multivariate location and scatter

    Lecture 2: Principal component analysis

    Lecture 3: Principal component analysis continues

    Lecture 4: Measures of robustness, Robust principal component analysis

    Lecture 5: Correspondence analysis

    Lecture 6: Bivariate correspondence analysis continues

    Lecture 7: Multiple correspondence analysis

    Lecture 8: Canonical correlation analysis

    Lecture 9: Discriminant analysis and classification

    Lecture 10: Clustering

    Lecture 11: Summary

    Lecture 12: New winds