Lecturer: Jan Beyersmann
Exercises: Regina Stegherr
|English, unless all students have sufficient knowledge of German|
|Lectures Mondays 10:00 a.m. - 12:00 p.m. (H14)|
Exercise Tuesdays 4:00 p.m. -5:00 p.m. (H12)
Reading course There will also be a complementary reading course. Details will be announced in the lecture. (written Exam: 12.02.2019, 16:15 in H12)
1st Exam 13.02.2019, 12:00-14:00 in H2
2nd Exam 28.03.2019
Elementary Probability Calculus, Stochastik I and Measure Theory. The level of the course is that of a first year's master course in Mathematics, but 3-year BSc students will also be able to follow the course. Some basic programming knowledge in R would be helpful.
The lecture "Stochastics 3" is a fundamental part in statistical education, covering, in particular, estimation and testing in linear models. Linear models are a key discipline in applied statistics, including the modern fields of analytics/prediction and causality. Topics covered include:
- multivariate normal distribution
- random quadratic forms
- Least-Squares- and BLUE-estimators
- Analysis of Variance (ANOVA)
- Regression analysis
- Prediction and Causality
Lecture and exercises will combine a thorough mathematical study of linear models theory with more applied aspects, the latter also using R.
Exam In order to participate in the final exam, it is necessary to earn 50% of the points on the exercise sheets.
- Agresti, A., Foundations of linear and generalized linear models. Wiley Series in Probability and Statistics, 2015.
- Christensen, R., Plane answers to complex questions: the theory of linear models. Springer Science and Business Media, 2011.
- Farawy, J.J., Linear Models with R. CRC Press, 2015.
- Toutenburg, H., Lineare Modelle: Schätzung, Vorhersage, Modellwahl, Mean-Square-Error-Superiorität, Zusatzinformation, fehlende Werte, Datenanalyse, kategorielle Regression, Matrixtheorie. Physica-Verl., 1992.
Because of the "Semestereröffnung" on Monday, Oct 15, the first lecture will be on Mon, Oct 22, 10h15. The second lecture will be on Tue, Oct, 23, 16h15 in room N24, H12. The first exercise will be on Tue, Oct 30.
Moodle enrollment keywort will be announced in the first lecture.