Multivariate Analysis


Lecturer: Markus Pauly and Sarah Friedrich

Exercises: Burim Ramosaj and Thilo Welz

General Information

Lectures4 h


2 h

Time and Venue


Monday, 12 - 2 p.m., He 18, 120

Thursday, 8 - 10 a.m., He 18, 120


Tuesday, 4 - 6 p.m., He 22, E.04


Final Exam: in Heho 18, Room 120 on Monday, July 16, 2018 from 11.30am - 02.00pm

Retake Exam: tba.

General Informations:

Prerequisites:Analysis I-II; Linear Algebra I-II; Stochastics I; Elementary Probability and Statistics

In order to be admitted to the exam, students must have achieved at least 40% of all exercise points.


Multivariate analysis is in principle a collection of methods designed to elicit information from multivariate data and to answer different statistical questions of interest.
In particular, students will

  • get to know different (parametric and nonparametric) statistical models which are most popular for describing multivariate data in practice and will
  • be familiar with the corresponding inference procedures as hypothesis tests (e.g. Wilk's Lambda) and confidence ellipsoids,
  • learn about specific classification and grouping methods and their properties and
  • be able to apply their knowledge to real data.
  • Finally, if there is enough time left, we will also treat modern statistical learning techniques for (multivariate) classification and prediction problems.


  • Data visualization. How to present multivariate data?
  • Hypothesis construction and testing; e.g. likelihood-ratio-tests and nonparametric tests
  • Confidence ellipsoids
  • Dimension reduction or structural simplification and their limitations
  • Investigation of dependence among variables
  • Bootstrap for multivariate data
  • Classification and Prediction

Exercise Sheets:

The exercise sheets are on Moodle.

Literature: click here

Semesterapparatclick here


Important Information:

Final Exam on Monday, July 16, 2018 in Heho 18, Room 120 starting at 11.30am !!!