Applied Statistics in R

At the end of the course, students are able to - solve practical problems by selecting adequate, advanced statistical methods, - applying statistical methods in a suitable analysis software, and - present the obtained results well using advanced visualization methods for statistical data (e.g. of spatial data)

The diversity types of data in real life scenarios leads to a manifold of statistical methods one has to master.In this course, the students will learn about advanced statistical techniques (e.g. nonlinear and logistic regression, penalized regression, and time series analysis) for different research questions. The course consists of a practical part including an introduction to the statistical programming language R, visualization techniques, and applying a selection of statistical methods to real life data, and a complementary lecture about the statistical backgrounds of the selected methods. The main focus is on the practical aspects and the application in R.

Seminar session will be partly structured as interactive lectures, introducing and discussing new tools in R. Further, in short presentations, students recapitulate and reflect on the most important learning outcomes of the previous session. In the following exercises, the methods learned are applied to new problems and questions.