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Multivariate Statistical Analysis

Learning objectives

• knowledge of methods for the analysis of multivariate statistical data, ability to describe and apply these methods
• ability to apply the learned methods by means of statistical software
• knowledge of advantages and disadvantages of the learned methods
• ability to recognize and to justify which method is to apply in which situation, ability to critically question the applicability and to autonomously perform small adaptations

Contents

• the multivariate normal distribution
• analysis of variance
• factor analysis
• cluster analysis
• linear discriminant analysis
• overview of further methods of multivariate analysis

Prerequisites

There are no formal prerequisites for participation in the course. However, knowledge of statistics and mathematics at bachelor level is desirable.

Lecture and exercise documents

Script, exercises, sample solutions as well as a collection of formulas are available for download in Stud.IP in the respective semester. Examinations with sample solutions from previous semesters can be found at the end of this page.

Scope and evaluation

• lecture with 2 WCH and exercise with 1 WCH
• written exam
• 5 credit points

Dates

Lecture and exercise dates are announced via Stud.IP. The course takes places every second summer semester. Examination dates can be found on the website of the examination office.

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