The Chair of Statistics offers a wide range of courses from the area of statistics and econometrics. Besides basic methods of descriptive and inductive statistics the main focus lies on multivariate testing and estimation procedures – especially in the context of regression analysis.
The relevant core competence for research and practice consists in a combination of methodological understanding and the skill to implement the respective statistical techniques in statistical programming. Students will learn how to implement and interpret statistical methods empirically as well as to evaluate statistical findings properly.
Bachelor's degree studies
Information about current ECTS for the courses "Mathematics" and "Statistics" for students of Business/Economic Sciences can be found in this overview .
- Mathematics for students of Business/Economic Sciences (winter semester): This course teaches students the mathematical basics needed for a Business/Economic degree programme. By solving exercises and practical examples on their own students will learn how to apply mathematical techniques on relevant business/economic problems..
- Statistics 1 and Statistics 2 for students of Business/Economic Sciences : descriptive statistik and exploration of data; basics of probability calculation; random variables; discrete and continuous distribution; random samples; point and interval estimation; distributed and non-distributed hypothesis testing; linear regression analysis; use of statistical (standard) software. Note that Statistics 1 and 2 are now taking two semesters instead of one and that the credit points received have changed from 8 to 10. Erklärung zur Umstellung von Statistik 1 und 2 auf zwei Semester und von 8 auf 10 ECTS.
- Introduction to Econometrics (summer semester): This course focuses on regression analysis. Relevant topics are databased quantification of economic correlation, testing of corresponding hypothesis and estimation of the uncertainty of the results.
- Introduction to Time Series Analysis (winter semester): The course is covering the basic topics of time series analysis like level analysis, season analysis, cycle analysis and analysis of trends. The first part of the course focuses on intuitive, semi- and nonparametric methods, for example the simple component model and diverse methods of smoothing data sets. The second part of the course centres theory, selection, estimation and diagnostics of ARIMA-Models which still play an essential role in the context of time series analysis.
- Computerised Statistics: Central subject is an introduction to the statistical software R. Besides teaching basic techniques of the software R (objects, functions, loops, etc.) the course offers an introduction to applied statistical data analysis(providing helpful tables and graphics, descriptive analysis, model estimation).
Master's degree studies
- Methods of Econometrics (winter semester): The course is intended to be the basis for the Master's degree and covers the basic topics of regression analysis and test methods. At the beginning of the course there will be a smooth overview on topics of the course „Introduction to Econometrics“. The second part of the course covers the method of least squares in-depht and its asymptotic properties. The third part consists of exact and asymptotic test theory and diverse test methods, for example there will be an introduction to the Theory of Maximum Likelihood. The fourth and fifth part of the course centres methods of generalised least squares and an outlook on advanced regression analysis.
- Panel Data Analysis (summer semester): The course focuses on the estimation of regression models for panel data. The range of topics covered in the course spans essential methods of estimation, fixed effects and random effects estimation. Furthermore, methods of forecasting and testing within the context of panel data analysis will be presented.
- Seminar "Applied Statistics" (summer term): Die computergestützte Anwendung statistischer Verfahren und die Interpretation der erzielten empirischen Ergebnisse sind Kernkompetenzen in diversen Berufsfeldern. Die Erlangung dieser Kompetenzen ist Ziel dieses Seminars mit wechselnden Themenschwerpunkten, die den Bereichen Zeitreihenprognose, Mikroökonometrie (z.B. Marketing- und Kapitalmarktforschung) und robusten Methoden zugeordnet werden können.
- Computational Statistics - Regression in R: The course focuses on the estimation of regression models, model validation and diagnostics for regression. Besides graphical and classic validation methods and tests simulation based approaches will be discussed. Moreover, modelling of diverse scales of measurement and transformations of variables are a further topic. The course deals with cross-sectional data, time series data and panel data.
- Computational Statistics - Statistical Learning in R: tba
- The Chair of Statitics does not offer any kind of "Scheinklausuren" (certificates for participation in classes that are not part of your curriculum with grade and ECTS credits). Students of "International Cultural and Business Studies" should address Professor Heinrich if there are any questions concerning the education in statistics (this also comprises learning agreements for classes visited at other institutions and international universities). You are not supposed to replace the statistics class in the curriculum with the statistics class offered by the faculty of economics and business administration.
- The Courses Computergestützte Statistik 1 & 2 (and more of the Computer-aided statistics program) are offered to Bachelor's degree and Master's degree students of all faculties. You will receive a confirmation certifying that you passed the exam, listing the specific topics treated, however without a grade.