WiSe 20/21: Module offerings

• Mathematik für Wirtschaftswissenschaftler (Mathematics for Economists)

  0171cA1.4
  learning objectives: Students learn the essential mathematical methods they need in order to understand the formalized economic ... read more

  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.
  
  

  types of course units / workload per unit / obligatory or optional participation
  Vorlesung / 3 SWS / Teilnahme wird empfohlen
  Übung / 1 SWS / Teilnahme wird empfohlen
  Studentisches Tutorium / - SWS / Teilnahme wird empfohlen

  test
  Klausur (ca. 120 Minuten)

  language of instruction:
  German
  
  workload
  180 hours (6 ECTS)
  
  duration / frequency
  one semester / every winter semester

  close
  • 10120301 Lecture
    Mathematics for Economists (V) (Dieter Nautz)
    Schedule: -
    Location: Online - zeitUNabhängig
    
  • 10120302 Practice seminar
    Mathematics for Economists (Ü) (Dieter Nautz)
    Schedule: -
    Location: Online - zeitABhängig
    
  • 10120305 Tutorial
    Mathematics for Economists (T) (Lea Sieckmann)
    Schedule: -
    Location: Online - zeitABhängig, Termine siehe Blackboard
    
  • 10120305a Tutorial Cancelled
    Mathematik (T) (Lea Sieckmann)
    Schedule: Mi 10:00-12:00 (Class starts on: 2020-11-04)
    Location: Hs 102 Hörsaal (Garystr. 21)
    
  • 10120305b Tutorial Cancelled
    Mathematik (T) (Lea Sieckmann)
    Schedule: Mi 10:00-12:00 (Class starts on: 2020-11-04)
    Location: Hs 103 Hörsaal (Garystr. 21)
    
  • 10120305c Tutorial Cancelled
    Mathematik (T) (Lea Sieckmann)
    Schedule: Mi 10:00-12:00 (Class starts on: 2020-11-04)
    Location: Hs 104 Hörsaal (Garystr. 21)
    
  • 10120305d Tutorial Cancelled
    Mathematik (T) (Lea Sieckmann)
    Schedule: Mi 10:00-12:00 (Class starts on: 2020-11-04)
    Location: Hs 106 Hörsaal (Garystr. 21)
    
  • 10120305e Tutorial Cancelled
    Mathematik (T) (Lea Sieckmann)
    Schedule: Mi 10:00-12:00 (Class starts on: 2020-11-04)
    Location: Hs 107 Hörsaal (Garystr. 21)
    
  • 10120305f Tutorial Cancelled
    Mathematik (T) (Lea Sieckmann)
    Schedule: Mi 12:00-14:00 (Class starts on: 2020-11-04)
    Location: Hs 102 Hörsaal (Garystr. 21)
    
  • 10120305g Tutorial Cancelled
    Mathematik (T) (Lea Sieckmann)
    Schedule: Mi 12:00-14:00 (Class starts on: 2020-11-04)
    Location: Hs 103 Hörsaal (Garystr. 21)
    
  • 10120305h Tutorial Cancelled
    Mathematik (T) (Lea Sieckmann)
    Schedule: Mi 12:00-14:00 (Class starts on: 2020-11-04)
    Location: Hs 104 Hörsaal (Garystr. 21)
    
  • 10120305i Tutorial Cancelled
    Mathematik (T) (Lea Sieckmann)
    Schedule: Mi 12:00-14:00 (Class starts on: 2020-11-04)
    Location: Hs 106 Hörsaal (Garystr. 21)
    
  • 10120305j Tutorial Cancelled
    Mathematik (T) (Lea Sieckmann)
    Schedule: Mi 12:00-14:00 (Class starts on: 2020-11-04)
    Location: Hs 107 Hörsaal (Garystr. 21)
    
  • 10120305k Tutorial Cancelled
    Mathematik (T) (Lea Sieckmann)
    Schedule: Mi 14:00-16:00 (Class starts on: 2020-11-04)
    Location: Hs 102 Hörsaal (Garystr. 21)
    
  • 10120305l Tutorial Cancelled
    Mathematik (T) (Lea Sieckmann)
    Schedule: Mi 14:00-16:00 (Class starts on: 2020-11-04)
    Location: Hs 103 Hörsaal (Garystr. 21)
    
  • 10120305m Tutorial Cancelled
    Mathematik (T) (Lea Sieckmann)
    Schedule: Mi 14:00-16:00 (Class starts on: 2020-11-04)
    Location: Hs 104 Hörsaal (Garystr. 21)
    
  • 10120305n Tutorial Cancelled
    Mathematik (T) (Lea Sieckmann)
    Schedule: Mi 14:00-16:00 (Class starts on: 2020-11-04)
    Location: Hs 106 Hörsaal (Garystr. 21)
    
  • 10120305o Tutorial Cancelled
    Mathematik (T) (Lea Sieckmann)
    Schedule: Mi 14:00-16:00 (Class starts on: 2020-11-04)
    Location: Hs 107 Hörsaal (Garystr. 21)
    
  • 10120305p Tutorial Cancelled
    Mathematik (T) (Lea Sieckmann)
    Schedule: Mi 16:00-18:00 (Class starts on: 2020-11-04)
    Location: HFB/B Hörsaal (Garystr. 35-37)
    
  • 10120305q Tutorial Cancelled
    Mathematik (T) (Lea Sieckmann)
    Schedule: Mi 16:00-18:00 (Class starts on: 2020-11-04)
    Location: HFB/D Hörsaal (Garystr. 35-37)
    
  • 10120305r Tutorial Cancelled
    Mathematik (T) (Lea Sieckmann)
    Schedule: Mi 16:00-18:00 (Class starts on: 2020-11-04)
    Location: Hs 102 Hörsaal (Garystr. 21)
    
  • 10120305s Tutorial Cancelled
    Mathematik (T) (Lea Sieckmann)
    Schedule: Mi 16:00-18:00 (Class starts on: 2020-11-04)
    Location: Hs 103 Hörsaal (Garystr. 21)
    
  • 10120305t Tutorial Cancelled
    Mathematik (T) (Lea Sieckmann)
    Schedule: Mi 16:00-18:00 (Class starts on: 2020-11-04)
    Location: Hs 104 Hörsaal (Garystr. 21)
    

• Schließende Statistik (Inferential Statistics)

  0171cA1.6
  learning objectives: Students learn to make decisions based on statistical data and assess the quality of these decisions. They learn how ... read more

  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.
  
  

  types of course units / workload per unit / obligatory or optional participation
  Vorlesung / 2 SWS / Teilnahme wird empfohlen
  Übung / 1 SWS / Teilnahme wird empfohlen
  Studentisches Tutorium / - SWS / Teilnahme wird empfohlen

  test
  Klausur (ca. 120 min.)

  language of instruction:
  German / English
  
  workload
  180 hours (6 ECTS)
  
  duration / frequency
  one semester / every winter semester

  close
  • 10120501 Lecture
    Inferential Statistics (V) (Timo Schmid)
    Schedule: Mi 12:00-14:00 (Class starts on: 2020-11-04)
    Location: Mi Online - zeitABhängig, Mi Online - zeitUNabhängig
    
  • 10120502 Practice seminar
    Inferential Statistics (T) (Sylvia Harmening)
    Schedule: Di 10:00-12:00 (Class starts on: 2020-11-03)
    Location: Di Online - zeitABhängig, Di Online - zeitUNabhängig
    
  • 10120505 Tutorial
    Inferential Statistics (T) (Sylvia Harmening)
    Schedule: Di 08:00-10:00, Mi 14:00-16:00, Do 16:00-18:00 (Class starts on: 2020-11-03)
    Location: Online - zeitABhängig; Online - zeitUNabhängig
    

• Stichprobenverfahren (Sampling Procedure)

  0171cB2.5
  learning objectives: Students gain an introduction into the field of survey statistics and learn the basic methods of sampling theory. ... read more

  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
    Sampling Procedure (V) (Timo Schmid)
    Schedule: Di 18:00-20:00 (Class starts on: 2020-11-03)
    Location: Online - zeitABhängig
    
  • 10122026 Methods Tutorial
    Sampling Procedure (Ü) (Sylvia Harmening)
    Schedule: Di 16:00-18:00 (Class starts on: 2020-11-10)
    Location: Online - zeitABhängig
    

• Modules w/o courses in this semester

  3 Modules
  • Einführung in die Statistik (Introduction to Statistics) 0171cA1.5
  • Einführung in die Ökonometrie (Introduction to Econometrics) 0171cB2.1
  • Statistische Modellierung (Statistical Modeling) 0171cB2.2