Upon successful conclusion, the students are expected to be able to perform and to comment on statistical analyses using basic and intermediate statistical methods. In particular, they should be able to use statistical modelling to describe and analyse data sets including interactions between variables and models ranging from simple linear models to generalised linear (logistic) and survival analysis models. The students should be able to comment on the appropriateness of an experiment and its statistical analyses from a technical point of view and about the relevance of the statistical results for the question of interest, i.e., they should become critical users of statistics.

(Subject to change)
' Exploratory data analysis
Numerical and graphical data summaries
p-values: Significance and Hypothesis Testing
' Basic techniques, from a modelling point of view:
Linear Models: correlation and regression, analysis of variance
Multivariate analysis
Non Parametric Tests
- Extending the general linear model
The generalised linear model
Maximum Likelihood Methods
Survival analysis
Classification

Course taught in English.

This course presents a unified view of common biostatistical methods through the use of statistical modelling. It takes a practical an applied approach to statistical data analysis and provides a refresher and an overview of common methods used to analyse biological data. Besides the statistical theory, the course is also an introduction to the statistical computing environment R, with an emphasis on the exploratory data analysis and the reproducible research paradigms.