Applied Multivariate Statistics

(Master, 2nd Semester, SS)

Which questions does the multivariate statistics answer?

• Which transport type will be chosen on the way homework?
• Who belongs to the target group for traveling on a cruise ship? Which advertising are most attractive for the target group?
• How can geographic distances between cities be used to create entire maps?
• Which factors do influence stock prices?
• How do you differentiate between consumer groups of different restaurants?
• What is the probability that a newfound book was written by Shakespear?

The course enables you to answer this kind of question. Many methods from multivariate statistics, such as principal component analysis, factor, discriminant, and cluster analysis will be discussed during one semester. Furthermore, the methods will be applied to real data using the statistical software R, which is a very helpful tool in the statistical analysis of large datasets.

Semester progress

• The course contains one lecture and one tutorial on PCs per week, self-study. 
• To get the credit points the participants have to make a project (present it at the end of the semester) and successfully attend the written exam (90 minutes). 
• To make a  project means to handle a dataset and then present it at the end of the semester. For details go to section " projects ".   
• All participants are recommended to use their own laptop with installed   R software (open source) (please note the  GNU GENERAL PUBLIC LICENSE ). 
• The course language is English.

OPAL

• The lecture script, lecture videos, exercises are available in OPAL Kurs.
• Please register for the course in OPAL!

Schedule

Topics

(may change during the semester.)

• Refreshment matrix algebra

• Decomposition of data matrices by factors

• Principal component analysis

• Factor analysis

• Correspondence analysis

• Canonical correlation analysis

• Multidimensional scaling

• Discriminant analysis

• Logistic regression

• Regression

• Principal component regression

Literature

• Backhaus, K., Erichson, B., Plinke, W., Weiber, R. (2008), Multivariate Analysemethoden: Eine anwendungsorientierte Einführung (12. Auflage), Springer Verlag.
• Härdle, W., Simar, L. (2015), Applied Multivariate Statistical Analysis (4nd edititon), Springer Lehrbuch.
• Härdle, W., Hlavka, Z. (2007), Multivariate Statistics: Exercises and Solutions, Springer Lehrbuch.