Chair of Mathematical Statistics (SMAT)

Linear Models - MATH 341

Instructor: Prof. Victor Panaretos

Assistants: Pavol Guričan, Valentina Masarotto



Regression modelling is a basic tool of statistics, because it describes how one variable may depend on another. The aim of this course is to familiarise students with the basics of regression modelling, and some related topics. 

Topics covered include:

Required prior knowledge

The second-year course in statistics; first-year course in linear algebra

Useful Documents

Recommended Texts

Draper, N.R. & Smith, H.S. (1998). Applied regression analysis. Wiley
Hocking, R.R. (1996). Methods and applications of linear models : regression and the analysis of variance. Wiley.
Davison, A.C. (2009).Statistical models. Cambridge.

Exam Information

There will be a mock midterm exam (test blanc) and a written final exam.
No notes, books or any other material will be allowed in the exams.


The midterm will take place on Wednesday November 23rd, 16:15-18:00 in CM 3.


Each week one or two exercises will be graded to provide feedback. Deadline to hand these in is either at the beginning of the exercise session (following the week when they are set), or until 16:00 on the same Monday in the box outside of office MA B1 493.


Practicals aim at developing the practical skills required when applying regression methodology. Practicals are assigned on certain weeks and are due three weeks later.

Winter 2016 Schedule

Lectures: CM3 Wednesdays, 16:15-18:00
Exercises: CH B3 31 Mondays, 16:15-18:00