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040690 UK Generalized Linear Model (2012S)

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

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. 50 participants
Language: German

Lecturers

Classes (iCal) - next class is marked with N

Tuesday 06.03. 09:00 - 12:00 (ehem. Hörsaal DAC Universitätsstraße 5 Hochparterre)
Tuesday 13.03. 09:00 - 12:00 (ehem. Hörsaal DAC Universitätsstraße 5 Hochparterre)
Tuesday 20.03. 09:00 - 12:00 (ehem. Hörsaal DAC Universitätsstraße 5 Hochparterre)
Tuesday 27.03. 09:00 - 12:00 (ehem. Hörsaal DAC Universitätsstraße 5 Hochparterre)
Tuesday 17.04. 09:00 - 12:00 (ehem. Hörsaal DAC Universitätsstraße 5 Hochparterre)
Tuesday 24.04. 09:00 - 12:00 (ehem. Hörsaal DAC Universitätsstraße 5 Hochparterre)
Tuesday 08.05. 09:00 - 12:00 (ehem. Hörsaal DAC Universitätsstraße 5 Hochparterre)
Tuesday 15.05. 09:00 - 12:00 (ehem. Hörsaal DAC Universitätsstraße 5 Hochparterre)
Tuesday 22.05. 09:00 - 12:00 (ehem. Hörsaal DAC Universitätsstraße 5 Hochparterre)
Tuesday 05.06. 09:00 - 12:00 (ehem. Hörsaal DAC Universitätsstraße 5 Hochparterre)
Tuesday 12.06. 09:00 - 12:00 (ehem. Hörsaal DAC Universitätsstraße 5 Hochparterre)
Tuesday 19.06. 09:00 - 12:00 (ehem. Hörsaal DAC Universitätsstraße 5 Hochparterre)
Tuesday 26.06. 09:00 - 12:00 (ehem. Hörsaal DAC Universitätsstraße 5 Hochparterre)

Information

Aims, contents and method of the course

1. Piecewise Regression und Regression Trees /mh/
2. Logistic Regression /mh/
3. Log-Lineare Modelle /wg/
4. ANOVA and Mixed Models /wg/
Introduction into generalized linear models

Assessment and permitted materials

Attendance of course is obligatory (max 3 lectures absence)
In each block two exercises must be be uploaded to the platform according to the schedule
Final test at the end of semester
Gradues: 15% for exercises in each block, 40% final test

Minimum requirements and assessment criteria

Application oriented introduction into advanced statistical methods
Understanding statistical modeling in applications
Usíng R in advanced statistical modeling

Examination topics

Lecture combined with exercises
Handouts are available

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.

Association in the course directory

Last modified: Mo 07.09.2020 15:29