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