Applied Regression

Lecturer (assistant)
Duration2 SWS
TermWintersemester 2019/20
Language of instructionEnglish
Position within curriculaSee TUMonline
DatesSee TUMonline


Admission information


At the end of this course, students are able to reproduce all definitions encompassed in linear univariate regression, including model specification, estimation, hypothesis testing, goodness-of-fit assessment, and model selection. Students will become adept at using the R statistical package for graphing data, fitting and assessing regression models. They are able to name and apply the necessary commands to produce plots, carry out parameter estimation and for the assessment of the regression correctly. The students are able to apply univariate linear regression on real and simulated data and assess the results.


Simple linear and multiple regression comprising model specification and assumptions, minimum least squares and maximum likelihood estimation, R2 goodness of fit, hypothesis testing by F- and t-tests, individual confidence and prediction intervals, residual analyses, influence diagnostics, transformations, multi-collinearity, model selection criterion (Mallows Cp, AIC, crossvalidation); brief introductions to logistic, poisson, survival and linear mixed model regression. The statistical package R will be used.


MA1401 Introduction to Probability, MA2402 Basic Statistics, working knowledge of the statistical package R

Teaching and learning methods

The module is offered as lectures with accompanying practice sessions. In the lectures, the contents will be presented in a talk with demonstrative examples, as well as through discussion with the students. The lectures should animate the students to carry out their own analysis of the themes presented and to independently study the relevant literature. Corresponding to each lecture, practice sessions will be offered, in which exercise sheets and solutions will be available. In this way, students can deepen their understanding of the methods and concepts taught in the lectures and independently check their progress. At the beginning of the module, the practice sessions will be offered under guidance, but during the term the sessions will become more independent, and intensify learning individually as well as in small groups.

Recommended literature

[1] Myers, R.H. (1990): Classical and Modern Regression with Applications, Duxbury Press, Belmont, CA, USA. [2] Abraham, B. and Ledolter, J. (2006): Introduction to Regression Modeling, Thomson/Brooks Cole, USA. [3] Christensen, R. (2002): Plane answers to complex questions: the theory of linear models. 3rd Edition, Springer, NY. [4] Faraway, J.J. (2004): Linear Models with R, Chapman & Hall/CRC, UK. [5] Fitzmaurice, G.M., Laird, N.M., and Ware, J.H. (2004). Applied Longitudinal Analysis, Wiley. [6] Fox, J. (1997) Applied Regression Analysis, Linear Models, and Related Methods. Sage Publications, London, UK. [7] Fox, J. (2002) An R and S-Plus Companion to Applied Regression. Sage Publications, London, UK.


This class is supported by DataCamp.