Course catalog

SoSe 26  
 School of Busin...  
 Statistics  
 Course

SoSe 26: Module offerings

Statistics

0191d_m30

  • Einführung in die Statistik (Introduction to Statistics)

    0171dA1.6

    learning objectives:
     Students gain basic knowledge of information reduction when dealing with one-dimensional and multidimensional data on different measurement levels (“descriptive statistics”). Students also learn how to use the tools of probability theory to deal with the randomness of statistical information. Based on the concept of probability, students learn to derive the concept of random variables. In addition to learning about basic concepts and definitions, students also learn how to describe important distribution models. Students also use software to display central statistical concepts, such as the dispersion of results within a distribution model. They learn to process simple statistical analyses themselves using a computer. The module integrates intercultural and international diversity as a cross-sectional topic that students should be aware of when it comes to research methods and evaluations.

    course content:
     One-dimensional and multidimensional empirical distribution, principles of probability theory, random variables, ratios and indices, discrete distribution models.

    language of instruction:
     German/English

    workload
     180 hours (6 ECTS)

    duration / frequency
    one semester / every summer semester

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  • Einführung in die Ökonometrie (Introduction to Econometrics)

    0171dB2.1

    learning objectives:
     Students learn to quantify and verify economic behavioral equations using statistical methods and observational data. They learn to describe and apply the basic methods of regression analysis including testing parameters. Through a trained understanding of econometric models, they can also identify the effects of deviating or violating models on estimates and regression parameter tests and develop appropriate strategic solutions. By including a practical computational exercise, students learn to perform regression analyses by themselves and interpret the results appropriately. Another important objective is to use student diversity as a resource and make a conscious effort to include it in the students’ daily life.

    course content:
     Fundamental methods of econometrics, for example: The classical linear regression model, parameter estimation with the least squares method, confidence ranges and parameter tests, modeling structural breaks and season, heteroscedasticity, and autocorrelation of residuals.

    language of instruction:
     German / English

    workload
     180 hours (6 ECTS)

    duration / frequency
    one semester / irregular

    close
    • 10121201 Lecture
      Introduction to Econometrics (V) (Dieter Nautz)
      Schedule: Mo 10:00-12:00 (Class starts on: 2026-04-13)
      Location: Hs 105 Hörsaal (Garystr. 21)
    • 10121226 Methods Tutorial
      Introduction to Econometrics (Ü1) (Michael Tran Xuan)
      Schedule: Do 10:00-12:00 (Class starts on: 2026-04-16)
      Location: Hs 105 Hörsaal (Garystr. 21)
    • 10121202 Practice seminar
      Introduction to Econometrics (Ü2) (N.N.)
      Schedule: Do 10:00-12:00 (Class starts on: 2026-04-16)
      Location: Do K 006a PC Pool 1 (Garystr. 21), Do K 006b PC-Pool 2 (Garystr. 21)

  • Sampling Methods

    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

    workload
     180 hours (6 ECTS)

    duration / frequency
    one semester / irregular

    close
    • 10122001 Lecture
      Machine Learning (Philipp Bach)
      Schedule: Mi 14:00-17:00 (Class starts on: 2026-04-22)
      Location: Hs 103 Hörsaal (Garystr. 21)
    • 10122026 Methods Tutorial
      Machine Learning (Philipp Bach)
      Schedule: Mi 17:00-18:00 (Class starts on: 2026-04-22)
      Location: Hs 103 Hörsaal (Garystr. 21)
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