After successful completion of the course, students are able to master the theoretical background of linear regression models and to apply these methods for concrete data sets, as well as interpreting the corresponding software output and test statistical hypothesis.
Simple and multiple linear regression model, least-squares estimation, geometric interpretation, Frisch-Waugh, coefficient of determination, classical linear regression model, Gauss-Markov theorem, variance estimation, testing linear hypotheses, t-test, F-test, generalized linear regression model, Aitken estimator, asymptotic analysis.
