# WiSe 21/22: Module offerings

## Statistics

0191d_m30
• ### Mathematik für Wirtschaftswissenschaftler (Mathematics for Economists)

0171dA1.5

learning objectives:
Students learn the essential mathematical methods they need in order to understand the formalized economic relationships that they will encounter during their studies. The methods will also enable them to develop solutions to problems related to these economic relationships. The methods include a fundamental understanding of linear algebra and analysis as well as how to apply that to economic problems. In addition, the module treats the students’ individual and cultural diversity as a positive contribution that supports student and teacher success.

course content:
Vectors, matrices, determinants, linear systems of equations, functions of one or more variables, ordinary and partial derivatives, extreme values of functions with and without constraints, integral calculus.

language of instruction:
German / English

180 hours (6 ECTS)

duration / frequency

one semester / every winter semester

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• 10120301 Lecture
Mathematics for Economists (V) (Dieter Nautz)
Schedule: Mo 10:00-12:00 (Class starts on: 2021-10-25)
Location: Hs 101 Hörsaal (Garystr. 21)
• 10120302 Practice seminar
Mathematics for Economists (Ü) (Dieter Nautz)
Schedule: Di 10:00-12:00 (Class starts on: 2021-10-26)
Location: Hs 101 Hörsaal (Garystr. 21)
• 10120305 Tutorial
Mathematics for Economists (T) (N.N.)
Schedule: Di 14:00-16:00, Di 16:00-18:00, Mi 08:00-10:00, Mi 10:00-12:00, Mi 12:00-14:00, Mi 14:00-16:00, Mi 16:00-18:00, Do 08:00-10:00, Do 10:00-12:00, Do 12:00-14:00, Do 14:00-16:00, Do 16:00-18:00 (Class starts on: 2021-10-26)
Location: 328 Hörsaal (Boltzmannstr. 16-20)
• ### Schließende Statistik (Inferential Statistics)

0171dA1.7

learning objectives:
Students learn to make decisions based on statistical data and assess the quality of these decisions. They learn how to solve three central types of statistical problems: Estimating unknown parameters of a distribution (point estimation), determining the confidence interval for an unknown parameter (confidence interval), making statements about the equality or inequality of distributions (tests). Students learn to apply these techniques themselves using empirical data and with the help of computer technology. The diversity of perspectives, experiences, and skills that the students bring with them to the group is treated as a positive contribution to the quality of research they produce and entails great benefits.

course content:
Continuous distribution models, sampling functions, parameter estimation, confidence intervals, hypothesis testing, regression analysis.

language of instruction:
German / English

180 hours (6 ECTS)

duration / frequency

one semester / every winter semester

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• 10120501 Lecture
Inferential Statistics (V) (Jan Pablo Burgard)
Schedule: Do 10:00-12:00 (Class starts on: 2021-10-21)
Location: HFB/C Hörsaal (Garystr. 35-37)
• 10120502 Practice seminar
Inferential Statistics (T) (Jan Pablo Burgard)
Schedule: Do 12:00-14:00 (Class starts on: 2021-10-21)
Location: Online - zeitABhängig
• 10120505 Tutorial
Inferential Statistics (T) (N.N.)
Schedule: Di 08:00-10:00, Do 16:00-18:00 (Class starts on: 2021-10-26)
Location: Di Online - zeitABhängig, Do Hs 107 Hörsaal (Garystr. 21), Do Online - zeitABhängig
• ### Statistische Modellierung (Statistical Modeling)

0171dB2.2

learning objectives:
Students are advised to take “Introduction to Econometrics” before starting this module. Students learn how to analyze data for which the regression model is not appropriate. Students deal with the most important models used in the analysis of nominal, ordinal, and integer characteristics so that they can describe the models and also apply them empirically. Students will continue to discuss the analysis of general dependency patterns. They also learn the relevant methods and how to interpret the results obtained through use of these methods. In the tutorial section, students learn how to use the appropriate software and interpret the results based on examples. Everyone is given an equal opportunity to contribute their ideas and concepts.

course content:
Logit and probit models, threshold models, cumulative probit models, count data models, generalized linear model, log-linear model, models for longitudinal data.

language of instruction:
German / English

180 hours (6 ECTS)

duration / frequency

one semester / irregular

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• 10121301 Lecture
Statistical Modeling (V) (Jan Pablo Burgard)
Schedule: Mi 08:00-10:00 (Class starts on: 2021-10-20)
Location: Mi Online - zeitABhängig
• 10121326 Methods Tutorial
Statistical Modeling (Ü) (Christopher Caratiola)
Schedule: Mi 10:00-12:00 (Class starts on: 2021-10-20)
Location: Mi Online - zeitABhängig
• ### Stichprobenverfahren (Sampling Procedure)

0171dB2.5

learning objectives:
Students gain an introduction into the field of survey statistics and learn the basic methods of sampling theory. They also learn about the most important sampling techniques and how to use them. In addition, they use example cases to learn how to deal with nonreponse and how to use calibration methods. In the tutorial section, students learn how statistics software can be used to draw samples, for example, from the Campus-Files from the Federal Statistical Office. They also learn the relevant methods and are thus able to assess critically the practical implementations of sampling procedures. Moreover, they learn to explain and evaluate survey data generated through polling. The module takes into consideration gender and diversity to establish conditions that make it possible for all students to participate.

course content:
Population and sampling probabilities, Simple random sampling, Stratified sampling, Cluster sampling, Two-stage (multi-stage) sampling, Selection schemes with unequal probabilities, Regression estimation

language of instruction:
German / English

180 hours (6 ECTS)

duration / frequency

one semester / irregular

close
• 10122001 Lecture
Sampling Procedure (V) (Jan Pablo Burgard)
Schedule: Di 14:00-16:00 (Class starts on: 2021-10-26)
Location: Online - zeitABhängig
• 10122026 Methods Tutorial
Sampling Procedure (Ü) (Jan Pablo Burgard)
Schedule: Di 16:00-18:00, zusätzliche Termine siehe LV-Details (Class starts on: 2021-10-26)
Location: Online - zeitABhängig
• Einführung in die Statistik (Introduction to Statistics) 0171dA1.6
• Einführung in die Ökonometrie (Introduction to Econometrics) 0171dB2.1