Applied Statistical Regression

Beginning of lectures: 26/09/2011

Attendance certificate conditions: None.

Doctoral students:There are no conditions for obtaining the attendance certificate. This also holds for PhD students that are after ETH credit points for their doctorate. All doctoral students, as well as other attendants who are after ECTS credit points need to attend and pass the exam for getting the credits awarded.

Content

Here is a link to the exercises. 

Abstract

This course offers a practically oriented introduction into regression modeling methods. The basic concepts and some mathematical background are included, with the emphasis lying in learning "good practice" that can be applied in every student's own projects and daily work life.

Content

The course starts with the basics of linear regression models, and then proceeds to parameter estimation, tests and confidence intervals, residual analysis, model choice, and prediction. More rarely touched but practically relevant topics that will be covered include variable transformations, categorical and erroneous input variables.

The last third of the course is dedicated to an introduction into generalized linear regression models: Logistic regression for binary response variables, Poisson regression for count data, cumulative logit models for ordered, and multinomial regression for categorical response variables.

Objetive

The students acquire advanced practical skills in linear regression analysis and are also familiar with its extensions to generalized linear modeling.

Notice

The exercises, but also the classes will be based on procedures from the freely available, open-source statistical software package R, for which an introduction will be held. See the exercise section for more information.

Documents

Old exams

• So11
  

• Wi11
  

Organisation

Script (last update 03.10.2011)

Slides

• Week 1

• Week 2 no lecture/slides

• Week 3

• Week 4

• Week 5
  

• Week 6
  

• Week 7
  

• Week 8 no slides
  

• Week 9
  

• Week 10
  

• Week 11
  

• Week 12 no slides

• Week 13
  

Datasets from the lecture

Literature

There are many books that cover the topics of our course. Here are 3 recommendations:

1. Linear Models with R, Julian J. Faraway, Chapman & Hall/CRC (2005). ISBN-10: 1584884258. 229 pages, ca. 70$.
   There is a freely available version on CRAN, entitled Practical Regression and Anova using R: http://cran.r-project.org/doc/contrib/Faraway-PRA.pdf. This free version is not identical to the book, but it is still a very good reference. For the later chapters of the course, the second volume of Faraway’s regression literature is required:
2. Extending the Linear Model with R, Julian J. Faraway, Chapman & Hall/CRC (2006). ISBN-10: 158488424X. 312 pages, ca. 75$.
3. Applied Regression Analysis, N. Draper and H. Smith, Wiley Interscience, 3rd Edition (1998). ISBN-10: 0471170828. 736 pages, ca. 100$
4. Introduction to Linear Regression Analysis, D. Montgomery, E. Peck, G. Vining, Wiley-Interscience, 4th Edition (2006). ISBN-10: 0471754951. 640 pages, ca. 85$.
5. Applied Regression Analysis and Generalized Linear Models, J. Fox, Sage Publications, 2nd Edition (2008). ISBN-10: 0761930426. 688 pages, ca. 82$.