040690 UK Generalized Linear Model (2012S)
Continuous assessment of course work
Labels
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).
- Registration is open from Th 09.02.2012 09:00 to Mo 20.02.2012 17:00
- Registration is open from Mo 27.02.2012 09:00 to Tu 27.03.2012 09:30
- Deregistration possible until Mo 30.04.2012 23:59
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
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
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
Understanding statistical modeling in applications
Usíng R in advanced statistical modeling
Examination topics
Lecture combined with exercises
Handouts are available
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
2. Logistic Regression /mh/
3. Log-Lineare Modelle /wg/
4. ANOVA and Mixed Models /wg/
Introduction into generalized linear models