Statistical inference is intended to aid in answering scientific questions about a population, based on a sample from this population, i.e. on data that are subject to variability. The data generating mechanism is described as a probability model that is completely specified except for a limited number of unknown parameters. The questions that can be answered are a) are the data consistent with the model? and b) assuming that a) is fulfilled, what can be concluded about values of the unknown parameters? In this course, the basic principles of statistical inference are presented, with an emphasis on likelihood methods. Methods are illustrated by the classical linear model.

All elements have to be awarded with at least a 5.5 in order to pass the final Assessment.