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Mathematical Economics 2 and Statistics 1

Module name (EN): Mathematical Economics 2 and Statistics 1
Degree programme: Business Administration, Bachelor, ASPO 01.10.2020
Module code: BBWL-2020-240
Hours per semester week / Teaching method: 6V (6 hours per week)
ECTS credits: 5
Semester: 2
Mandatory course: yes
Language of instruction:
German
Assessment:
Written exam (90 min. / Can be repeated semesterly)

[updated 02.01.2019]
Applicability / Curricular relevance:
BBWL-240 Business Administration, Bachelor, ASPO 01.10.2012, semester 2, mandatory course
BBWL-240 Business Administration, Bachelor, ASPO 01.10.2016, semester 2, mandatory course
BBWL-2020-240 Business Administration, Bachelor, ASPO 01.10.2020, semester 2, mandatory course
Workload:
90 class hours (= 67.5 clock hours) over a 15-week period.
The total student study time is 150 hours (equivalent to 5 ECTS credits).
There are therefore 82.5 hours available for class preparation and follow-up work and exam preparation.
Recommended prerequisites (modules):
BBWL-2020-140 Mathematical Economics 1


[updated 17.12.2019]
Recommended as prerequisite for:
BBWL-2020-310 Investment and Financing
BBWL-2020-450 Statistics 2
BBWL-2020-633
BBWL-2020-664 Specialization (SP) Module 4: International Finance


[updated 22.02.2020]
Module coordinator:
Prof. Dr. Teresa Melo
Lecturer: Prof. Dr. Teresa Melo

[updated 01.10.2016]
Learning outcomes:
Submodule: Mathematical Economics 2
After successfully completing this module, students will:
 
- be able to develop linear optimization models from informally described practical planning problems,
- be able to apply the simplex algorithm to solve linear programs independently,
- be able to describe basic concepts of duality theory,
- be able to interpret the results obtained by means of optimization methods,
- be able to describe the link between primal and dual optimization problems,
- be ale to perform a sensitivity analysis for an optimization problem,
- use Excel Solver for modeling and solving economic optimization problems,
- In addition, students will have developed their analytical skills by independently solving practical tasks.
  
 
 
Submodule: Statistics 1
After successfully completing this module, students will:
 
- be able to describe basic economic concepts of descriptive statistics for univariate and bivariate data analysis,
- select suitable methods for statistical data analysis and apply them independently to specific subjects of study,
- apply concepts for the graphical presentation of empirical data,
- interpret the results obtained from a data evaluation,
- analyze and interpret correlations between characteristics,
- understand possible applications in other fields of business studies and their practice,

[updated 07.04.2021]
Module content:
Submodule: Mathematical Economics 2
 
- Introduction to operations research
- Modeling economic planning problems with the help of linear optimization models
- Graphical solution of linear optimization problems
- Properties of linear optimization problems
- Primal simplex algorithm for solving linear programs
- Economic interpretation of results of optimization calculations
- Conducting a sensitivity analysis (using shadow prices and reduced costs).
- Duality and dualization rules
- Dual simplex algorithm
- Connection between primal and dual problem (theorem of complementary slackness)
- Solving a linear optimization problem using Excel Solver
- Economic applications of linear optimization (e.g. in production, logistics, marketing, investment)
 
  
Submodule: Statistics 1
 
- Research methodology and basic concepts
- Classification of statistical features
- Frequency distributions for grouped and ungrouped data
- Graphical representation of univariate data sets
- Description of univariate datasets using measures of location and dispersion
- Bivariate data analysis: Graphical representation of data sets and investigation of the correlation of statistical characteristics (contingency, correlation, rank correlation)
- Regression analysis


[updated 07.04.2021]
Teaching methods/Media:
Lecture and discussion in a large group using transparencies (projector) and the blackboard (theory and example calculations).
 
Both submodules (Mathematical Economics 2 / Statistics 1) will be supplemented by exercises and tutorials. A large number of exercise sheets covering the wide range topics in this module will be provided. Afterwards, the solutions will be discussed with the students.
 
Both the lecture notes and the exercise sheets will be available to students in electronic form.


[updated 07.04.2021]
Recommended or required reading:
Submodule: Mathematical Economics 2
 
- Domschke, Drexl: Einführung in Operations Research, 9. über. und verb. Auflage, Springer Gabler, Berlin, Heidelberg, 2015
- Domschke, Drexl, Klein, Scholl, Voß: Übungen und Fallbeispiele zum Operations Research, 8. akt. u. verb. Auflage, Springer Gabler, Berlin, Heidelberg, 2015
- Gohout, Operations Research: Einige ausgewählte Gebiete der linearen und nichtlinearen Optimierung, 4. wesentlich erw. Auflage, Oldenbourg, München, 2009
- Koop, Moock: Lineare Optimierung - Eine anwendungsorientierte Einführung in Operations Research, 2. Auflage, Springer Spektrum, Berlin, Heidelberg, 2018
- Werners: Grundlagen des Operations Research mit Aufgaben und Lösungen, 3. überarb. Auflage, Springer Gabler, Berlin, Heidelberg, 2013
 
 
  
Submodule: Statistics 1
 
- Arrenberg: Wirtschaftsstatistik für Bachelor. Mit Aufgaben und Lösungen, 3. überarb. u. erw. Auflage, UVK-Verlag, München, 2019
- Caputo, Fahrmeir, Künstler, Lang, Pigeot-Kübler, Tutz: Arbeitsbuch Statistik, 5. Auflage, Springer, Berlin, 2009
- Cramer, Kamps: Grundlagen der Wahrscheinlichkeitsrechnung und Statistik, 4. korr. u. erw. Auflage, Springer Spektrum, Berlin, Heidelberg, 2017
- Eckstein: Klausurtraining Statistik: Deskriptive Statistik - Stochastik - Induktive Statistik. Mit kompletten Lösungen, 7. vollständig überarb. Auflage, Springer, Berlin, Heidelberg, 2018
- Fahrmeir, Künstler, Pigeot, Tutz: Statistik: Der Weg zur Datenanalyse, 8. Auflage, Springer Spektrum, Berlin, Heidelberg, 2016
- Schira: Statistische Methoden der VWL und BWL: Theorie und Praxis, 5. akt. Auflage, Pearson Studium, 2016
- Steland: Basiswissen Statistik: Kompaktkurs für Anwender aus Wirtschaft, Informatik und Technik, 4. Auflage, Springer Spektrum, Berlin, Heidelberg, 2016
 


[updated 01.07.2021]
[Mon Jan 24 23:57:38 CET 2022, CKEY=bw2us1, BKEY=bbw3, CID=BBWL-2020-240, LANGUAGE=en, DATE=24.01.2022]