Learning Outcomes
Regression is a key tool for empirical academic research for explanation, i.e. estimating, causal relationships between variables. Recently, it has taken center stage as a key tool for prediction, i.e. supervised statistical learning with a continuous target variable Y. This will be the perspective of this class. The empirical example, used throughout this course, will be the predicition of house prices (Y) given its characteristics (X).
In the lectures of this course, students will gain knowledge on the theoretical concepts of forecasting in the context of regression analysis. In the tutorials, this knowledge will be used to actually implement and estimate empirical models using the programming language R. This will enable students to autonomously answer empirical research questions. This encompasses the specification of statistical models as well as their estimation. Theoretical knowledge from the lectures endows students to contextualize the obtained results. This includes the recognition of falsely specified models as well as the correct interpretation of model results. Furthermore, students will gain an expertise in the processing of empirical research projects typically part of a scientific thesis.
