Universität Wien

040690 UK Generalized Linear Model (2019S)

4.00 ECTS (2.00 SWS), SPL 4 - Wirtschaftswissenschaften
Continuous assessment of course work
Moodle

Registration/Deregistration

Note: The time of your registration within the registration period has no effect on the allocation of places (no first come, first served).

Details

max. 40 participants
Language: German

Lecturers

Classes (iCal) - next class is marked with N

  • Tuesday 05.03. 09:45 - 11:15 Seminarraum 14 Oskar-Morgenstern-Platz 1 2.Stock
  • Tuesday 19.03. 09:45 - 11:15 Seminarraum 14 Oskar-Morgenstern-Platz 1 2.Stock
  • Tuesday 26.03. 09:45 - 11:15 Seminarraum 14 Oskar-Morgenstern-Platz 1 2.Stock
  • Tuesday 02.04. 09:45 - 11:15 Seminarraum 14 Oskar-Morgenstern-Platz 1 2.Stock
  • Tuesday 09.04. 09:45 - 11:15 Seminarraum 14 Oskar-Morgenstern-Platz 1 2.Stock
  • Tuesday 30.04. 09:45 - 11:15 Seminarraum 14 Oskar-Morgenstern-Platz 1 2.Stock
  • Tuesday 07.05. 09:45 - 11:15 Seminarraum 14 Oskar-Morgenstern-Platz 1 2.Stock
  • Tuesday 14.05. 09:45 - 11:15 Seminarraum 14 Oskar-Morgenstern-Platz 1 2.Stock
  • Tuesday 21.05. 09:45 - 11:15 Seminarraum 14 Oskar-Morgenstern-Platz 1 2.Stock
  • Tuesday 28.05. 09:45 - 11:15 Seminarraum 14 Oskar-Morgenstern-Platz 1 2.Stock
  • Tuesday 04.06. 09:45 - 11:15 Seminarraum 14 Oskar-Morgenstern-Platz 1 2.Stock
  • Tuesday 18.06. 09:45 - 11:15 Seminarraum 14 Oskar-Morgenstern-Platz 1 2.Stock
  • Tuesday 25.06. 09:45 - 11:15 Seminarraum 14 Oskar-Morgenstern-Platz 1 2.Stock

Information

Aims, contents and method of the course

Goals:
Students learn advanced statistical methods, know how to use this methods in statistical modelling and apply this methods in statistical practice.

Contents:
Block I: Piecewise Regression und Regression Trees
Block II: Logistic Regression
Block III: Analysis of Variance und Experimental Design,
Block IV Random effect models
Block V: Loglineare models and introduction to generalized linearer models

Methods:
Lecture combined with practical exercises

Assessment and permitted materials

Attendance of the lectures (at most 3 missing)
In each block 2 exercises have to be solved and presented in accordance with the time schedule.
There are two tests: one for block I and II and one for blocks III - V

Minimum requirements and assessment criteria

Weights of the different assignments:
15% Exercises Block I
15% Exercises Block II
8% Exercises Block III
7% Exercises Block IV
15% Exercises Block V
20% First test
20% Second test

Examination topics

Topics presented in the lectures

Reading list

Agresti,A. (2002). Categorical Data Analysis. John Wiley & Sons.
Dobson, A.J. (2001). An Introduction to Generalized Linear Models, Second Edition. Chapman and Hall.
Fahrmeir, L., Kneib, T. und Lang, S. (2007). Regression: Modelle, Methoden und Anwendungen, Springer.
Faraway, J.J. (2005). Linear models with R, Chapman & Hall.
Faraway, J.J. (2005). Extending the Linear Model with R: Generalized Linear, Mixed Effects and Nonparametric Regression Models, Chapman & Hall.
Fox, J.(2008). Applied Regression Analysis and Generalized Linear Models, Sage.
Hosmer, D.W. & S. Lemeshow (2000). Applied Logistic Regression, Second Edition. John Wiley & Sons.
Kleinbaum, D. G. (1994): Logistic Regression. A Self-Learning Text. Springer.
Nelder J.A. & P. McCullagh (1989). Generalized Linear Models, Second Edition. Chapman& Hall.

Association in the course directory

Last modified: Mo 07.09.2020 15:29