Applied Statistical Modelling

Content
The lecture consists of two independent, equally weighted parts.

Part 1: Generalized Linear Models
Generalized Linear Models are an extension of classical linear models and cover many of the most common statistical models such as logistic regression for binary data or Poisson regression for count data.
This part of the lecture will cover the transfer form the classical regression model to the Generalized Linear Model, the introduction of the exponential family and link functions, an outline of parameter estimation, model selection, and model testing, as well as the practical application of Generalized Linear Models using the software R.

Part 2: Nonparametric Methods
Nonparametric methodes avoid restrictive parametric assumptions (e.g. the assumption of normally distributed observations) and are therefore very flexible especially suitable for explorative data analysis.
This part of the lecture will cover an introduction to nonparametric density estimation and nonparametric regression as well as the practical application of the presented methods using the software R.