We discuss modern statistical methods for data analysis, including methods for data exploration, prediction and inference. We pay attention to algorithmic aspects, theoretical properties and practical considerations. The class is hands-on and methods are applied using the statistical programming language R.

Multivariate Statistics deals with joint distributions of several random variables. The course introduces the basic concepts and provides an overview over the diverse methods of multivariate statistics with emphasis on applications.

Regression studies the dependence of a random response variable on other variables. We consider the theory and application of linear regression with one or more covariates. Various extensions such as nonlinear models, generalized linear models, nonparametric models, robust methods, and model selection are discussed.

"Statistics Lab" is an Applied Statistics Workshop in Data Analysis. It provides a learning environment in a realistic setting.Students lead a regular consulting session at the Seminar für Statistik (SfS). After the session, the statistical data analysis is carried out and a written report and results are presented to the client. The project is also presented in the course's seminar.

The following concepts are covered: probabilities, random variables, probability distributions, joint and conditional probabilities and distributions, law of large numbers, central limit theorem, descriptive statistics, statistical inference, parameter estimation, confidence intervals, statistical tests, two-sample tests, linear regression.