Many of us are interested in questions like "which factors influence a certain biological phenomenon?", but are unsure which statistical test to apply. The purpose of the course is to understand which test is appropriate for your data. I’ll cover the standard statistical tests and explain in easy-to-understand terms how to use the R software to analyse your data. We cover linear and non-linear regression, t-tests, anova, ancova, multiple regression and other model-fitting techniques. This course provides a short introduction into the R environment, model fitting and then tackles in more depth the problem of model selection (the task of selecting “good” models from a set of candidate models).

The open source software R (http://www.r-project.org) has revolutionized the statistical data analysis for most bioscience disciplines. The R environment is completely free and runs on all common operating systems.

### Day1

- a brief introduction to R
- t-test, linear regression, anova, ancova
- multiple regression, multifactorial anova
- exercises

### Day2

- generalized linear models
- mixed effect models
- exercises

### Day3

- model selection
- exercises

- Teacher: Sven Bacher