Multivariate Statistics

Module Code
BI2009
Module Coordinators
Franz Cemic
Teachers
Franz Cemic
Short Description
Algorithms and application of multivariate statistics to data analysis.
Learning Objectives

Students know the basics of multivariate statistics. They know algorithms and tools and are able to apply them. In addition, they are able to challenge the analytical results critically and check for consistency. Graduates are able to work independently and reflect on their learning process.

Contents
  • Factor analysis
  • Cluster analysis
  • Principal components analysis
  • Regression analysis
  • Analysis of variance
Duration in Semester
1
Instruction Language
German
Total Effort
3 CrP; an estimated 90 hours, of which approximately 30 are spent in class.
Weekly School Hours
2
Method of Instruction

Lecture 1 SWS, Exercises 1 SWS

Requirements for the awarding of Credit Points

Examination prerequisite: Attendance at integrated lab

Examination: Oral exam

Evaluation Standard
according to examination regulations (§ 9)
Availability
Yearly
References
  • K. Backhaus,B. Erichson, R. Plinke: Multivariate Analysemethoden. Eine anwendungsorientierte Einführung, Springer Berlin