Advanced Regression Methods for Linguistics



Language and Computation Courses

Introductory Course

Advanced Regression Methods for Linguistics (in R), Martijn Wieling (University of Groningen, The Netherlands)

Week 1, 9:00 – 10:30, Room 272, Floor 3

This course will introduce students to advanced regression methods in R. While many people have learned about multiple regression, interpreting the output of a regression model, especially with interactions present is something which is often found difficult. The course will therefore start with one lecture explaining multiple regression. Subsequently, two lectures of the course will cover (Gaussian and logistic) mixed-effects regression in order to enable students to take into account structural variability present in the data. For example, experiments in linguistics frequently involve participants responding to multiple items. This structure needs to be brought into the model in order to prevent overconfident (i.e. too low) p-values. The final two lectures of this course provide a thorough introduction to generalized additive modeling, which is a powerful method to analyze non-linear patterns in data. This approach is especially useful when time-series data (such as EEG, eye-tracking, or articulatory data) need to be analyzed.

Lecture slides

Monday Aug. 6: Correlation and regression(pdf)
Tuesday Aug. 7: Mixed-effects regression (pdf)
Wednesday Aug. 8: Logistic mixed-effects regression (pdf)
Thursday Aug. 9: Introduction to generalized additive modeling (pdf)
Friday Aug. 10: Two-dimensional interactions with generalized additive modeling (pdf)

After opening one of the links to the (html) presentation in either Google Chrome or Firefox, you can use the arrow keys to move from slide to slide. You can press the [o] key to get an overview of all slides, and the [f] key to view the presentation in full screen mode. If you’d like to save the slides as pdf, this can best be done by printing to pdf from within the browser. Note that for each lecture, there are associated lab sessions (see recap slide, e.g., which you can use to practice.

Course requirements

Everybody attending the course should have a good grasp of R. For example, the material covered in this presentation:
should be familiar.

Preparatory reading

Supplementary reading

Here are some suggestions for further reading (books), if you want to learn more about (mixed-effects) regression and generalized additive modeling: