Introductory statistics are typically taught as a sequence of disconnected tests and protocols (e.g. t-test, ANOVA, ANCOVA, regression). Most of these tests, however, can be seen as special cases of the generalized linear regression model (GLM). In this course, we will introduce GLMs as a unified, coherent, and easily extendable framework for the analysis of many types of data, including Normal (Gaussian), binary, and discrete (count) response variables with both categorical (factors) and continuous predictors.

The course is aimed at graduate students and researchers with basic statistical knowledge that want to learn how to analyze experimental and observation data with generalized linear regression models in R. Basic knowledge means that we assume knowledge about foundational statistical concepts (e.g. standard error, p-value, hypothesis testing) that are usually covered in a first introductory statistics class. Participants should also be familiar with Rstudio and have some experience in programming R code, including being able to import, manipulate (e.g. modify variables) and visualize data. If you have never used R before, it will be more useful for you to take an introductory R course first.

1. Being able to specify and fit generalized linear regression models in R, choosing the appropriate distribution and link function according for your data.

2. Interpret the parameter estimates of the fitted models, including the correct interpretation of categorical predictors (e.g. contrasts, ANOVA, post-hoc testing), and calculate predictions from your model.

3. Understand the principles of model selection to choose the correct model / regression formula for your question.

4. Visualize the fitted models to check assumptions, communicate results, and increase understanding.

5. Acquire the foundations and some first ideas to move on to more complex regression models (e.g. Generalized Linear Mixed Models, Generalized Additive Models, Bayesian modeling) in the future.

Sessions from 14:00 to 20:00 (Monday to Thursday), 14:00 to 18:00 on Friday (Berlin time). Sessions will consist of a mix of lectures, in-class discussion, and practical exercises / case studies over Slack and Zoom.

Monday– Classes from 2-8 PM Berlin time

Brief reminder of R programming: importing, manipulating, and visualizing data.

Reminder of foundational statistical concepts: Hypothesis tests, p-values, MLE, confidence intervals

Introduction to linear models: mathematical and graphical interpretation. Parameter estimates. Residual variation and residual checks. Maximum likelihood estimation.


Tuesday– Classes from 2-8 PM Berlin time

Simple and multiple regression for normal (Gaussian) response variables with continuous and categorical predictors: fitting, understanding, visualizing, validation and predictions.


Wednesday– Classes from 2-8 PM Berlin time

ANOVA and variation partitioning.

Model selection

The general structure of generalized linear models (GLM) - link function and distribution

Generalized linear models for binary responses (Logistic Regression) - fitting, understanding, visualizing and checking


Thursday– Classes from 2-8 PM Berlin time

Basics of beta regression and Negative binomial regression.

Generalized linear models for discrete (count) data (Poisson Regression) - fitting, understanding, visualizing and model checks.


Friday– Classes from 2-6 PM Berlin time

Case studies

What lies ahead: generalized additive models, multilevel or mixed-effects models to account for hierarchical and correlated data (time, space, phylogeny).

Should you have any further questions, please send an email to info@physalia-courses.org