Generalized Linear Models (Summer 2017)

This course covers the basic theory, methodology, and application of generalized linear models (GLMs).
GLMs are a class of models for the analysis of quantitative and qualitative data.
This class subsumes linear models for quantitative responses (multiple regression, analysis of variance and analysis of covariance),
binomial models for binary response (logistic regression and probit models), loglinear models for categorical data, Poisson models for count data and survival models for failure time data as special cases.
In the lecture on Wednesday the mathematical aspects of the different models are explained, while in the tutorial on Thursday the application of these models is treated using the R software.
Case studies drawn from social, economic, engineering, and behavioral sciences are used to illustrate the estimation, assessment and interpretation of GLMs. Prerequisites Good knowledge of basic mathematical and statistical concepts (e.g. definition of probability and random variables). Strong mathematical soft skills (e.g. the ability to understand and work with mathematical definitions and theorems, elements of linear algebra) 
Schedule
Lecture (V. Amati)  Wed 08:1509:45 in G 306  
Tutorial  Thu 17:0018:30 in Z 613  
Exams (written)  02.08.2017, 10.00, P602;  11.10.2017, 10.00, P602 
Exercises
Most documents are only locally accessible  see possibilities for remote access. >no.  online  due  tutorial  download  data  solution 

0  26 April 2017  03 May 2017  04 May 2017  No assignment     
1  03 May 2017  10 May 2017  11 May 2017  Sheet 1    Solution 1 
2  10 May 2017  17 May 2017  18 May 2017  Sheet 2  rent  Solution 2 
3  17 May 2017  24 May 2017  01 June 2017  Sheet 3  car  Solution 3 
4  24 May 2017  31 May 2017  01 June 2017  Sheet 4  bmi  Solution 4 
5  31 May 2017  07 June 2017  08 June 2017  Sheet 5  pima  Solution 5 
6  07 June 2017  14 June 2017  22 June 2017  Sheet 6  pima  Solution 6 
7  14 June 2017  21 June 2017  22 June 2017  Sheet 7  rent_munich , advertising  Solution 7 
8  21 June 2017  28 June 2017  29 June 2017  Sheet 8  usedCars  Solution 8 
9  28 June 2017  05 July 2017  06 July 2017  Sheet 9  pima  Solution 9 
10  05 July 2017    13 July 2017  Sheet 10    Solution 10 
11  12 July 2017  19 July 2017  20 July 2017  Sheet 11  pima  Solution 11 
12  19 July 2017  26 July 2017  27 July 2017  Sheet 12    Solution 12 
Material
Most documents are only locally accessible  see possibilities for remote access.Slides and lecture notes
 Introduction (26 April 2017).
 Lecture notes last updated: 24 July 2017 (final version).
 Probability and random variables (Tutorial: 27 April 2017).
 Script and Data (Tutorial: 4 May 2017).
 Script and Data (Tutorial: 11 May 2017).
 Script and Data (Tutorial: 0108 June 2017).
 Script (Tutorial: 29 June 2017).
 Script and Data (Tutorial: 13 July 2017).
Software
Literature
The course content is covered in lecture notes that are made available. Further reading:General text books for GLM
 Dobson, A. J., Barnett, A. (2008). An introduction to generalized linear models. CRC press.
 Faraway, J. J. (2016). Extending the linear model with R: generalized linear, mixed effects and nonparametric regression models. CRC press.
 Fox, J. (2015). Applied regression analysis and generalized linear models. Sage Publications.
 McCullagh, P., Nelder, J. A. (1989). Generalized Linear ModelsChapman & Hall.
 Weisberg, S. (2005). Applied linear regression (Vol. 528). John Wiley & Sons.
 Lemeshow, S., Hosmer Jr, D.W. (2000). Logistic Regression. In Encyclopedia of Epidemiologic Methods. Wiley. (local copy)
 Hosmer Jr, D.W., Lemeshow, S. (2005). Applied logistic regression. John Wiley and Sons.
 Hosmer Jr, D.W., Lemeshow, S. (2005). Applied logistic regression. John Wiley & Sons. (local copy  Chapter 8)
 Train, K. (2009). Discrete Choice Methods with Simulation. Cambridge Universuty Press. (download)
 Ross, S. M. (2014). Introduction to probability models. Academic press. (Chapters 12).
 Devore, J. L. (2015). Probability and Statistics for Engineering and the Sciences. Cengage Learning. (Chapters 14)