Exam

Introduction and motivation of basic statistical methods using practical examples from neuroscience, perception research and image processing. The focus lies on the practical application of statistical methods and their implementation in Python. The following topics are covered: Discrete and continuous probability distributions, descriptive statistics (e.g., measures of location, dispersion, and correlation), inductive statistics (e.g., regression, generalized linear model (GLM)), and exploratory statistics are covered. Furthermore, the lecture will cover the introduction and application of probability distributions. Finally, a short introduction into Python and the use of notebooks will given in order to facilitate the use of the required statistical packages.

Students learn basic statistical methods, apply them and implement them in software. They are able to plan and evaluate experiments themselves and to avoid typical errors in experimental design. Furthermore, they can critically evaluated results presented in the literature.

Fahrmeir, Künstler, Pigeot, Tutz: Statistik; Springer-Verlag. /
Stahel: Statistische Datenanalyse; Vieweg & Sohn. /
Zusatzliteratur wird in der Vorlesung bekannt gegeben.