The slides of graduate-level statistics courses which I teach at the University of Groningen (Methodology & Statistics for Linguistics Research, BCN Statistics Course, and BCN Advanced (non)linear regression techniques in R) and by invitation at (e.g.) Cambridge and McGill, can be found at the links below. While the examples in my presentations mainly concern linguistics, the methods can be applied in other fields as well. After opening an (html) presentation, 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 an up-to-date pdf file is available, this is indicated. Note that the slides have been generated in R using the Slidify R package.

- Basics of statistics (
*pdf*)

Contents:*p*-values, sample vs. population, hypothesis testing - Introduction to R and RStudio (
*pdf*)

Contents: descriptive statistics and visualization - Basic statistical tests (
*pdf*)

Contents:*t*-test, Χ^{2}-test, ANOVA, non-parametric alternatives - Regression (
*pdf*)

Contents: correlation and multiple regression - Regression: extended version (
*pdf*)

Contents: correlation, multiple regression and logistic regression - Introduction to mixed-effects regression (
*pdf*)

Contents: analyzing reaction time data using mixed-effects regression - Logistic mixed-effects regression (
*pdf*)

Contents: analyzing eye tracking data using logistic mixed-effects regression - Introduction to generalized additive modeling (
*pdf*)

Contents: analyzing articulatory data using generalized additive modeling - Two-dimensional interactions with GAMs (
*pdf*)

Contents: analyzing EEG (ERP) data using generalized additive modeling - Multi-dimensional interactions with GAMs (
*pdf*)

Contents: analyzing geolinguistic variation using generalized additive modeling

For a slower pace discussing the material of the first three lectures above, the lecture slides of my undergraduate statistics course *Statistiek I* might be useful: Intro R, Descriptives, Sampling, *t*-tests, Non-parametric tests, Relating same-type variables, Summary & practice exam.