Multivariate Analysis (bzw. Multivariate Methoden)

Lecturer: Markus Pauly
Exercises taught by:
Dennis Dobler


General Information

LanguageEnglish
Lectures4 h

Exercises

2 h


Time and Venue

Lectures

Mondays, 8:30 a.m. - 10 a.m. in Helmholtz-Straße 18, room 120 (starting April 20th)

Tuesdays, 4 p.m. - 6 p.m. in Helmholtz-Straße 18, room 220

Exercise

Tutorium

Wednesdays, 2 p.m. - 4 p.m. in Helmholtz-Straße 18, room E20 

Wednesdays, 10 a.m. - 12 p.m. in Helmholtz-Straße 18, room 120 (except the Tutorium (June 10) which will start around 10:30 a.m.)


Exam:

Exam: Wed. July 15, 2015 in H3, 14:00 - 16:00.

Retake exam (in written form): Mon. October 5, 2015 in H11, 10:00 - 12:00.

Points to grade correspondence (retake exam):
at least 19.5 points: 4.0
at least 21.0 points: 3.7
at least 23.5 points: 3.3
at least 26.5 points: 3.0
at least 28.5 points: 2.7
at least 30.0 points: 2.3
at least 33.0 points: 2.0
at least 35.0 points: 1.7
at least 37.0 points: 1.3
at least 39.0 points: 1.0

On October 6 at 11 a.m. students may view their exam papers. (At Institute of Statistics)


General Informations:

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

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


Aims: 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.

Contents:

  • 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 the SLC. 


Literature: Click here!

Semesterapparat: Click here!


Note

Lecture Notes

The retake exam will be in written form.