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Business Statistics

Module name (EN):
Name of module in study programme. It should be precise and clear.
Business Statistics
Degree programme:
Study Programme with validity of corresponding study regulations containing this module.
Business Administration, Bachelor, ASPO 01.10.2013
Module code: BBABW-230
SAP-Submodule-No.:
The exam administration creates a SAP-Submodule-No for every exam type in every module. The SAP-Submodule-No is equal for the same module in different study programs.
P420-0388
Hours per semester week / Teaching method:
The count of hours per week is a combination of lecture (V for German Vorlesung), exercise (U for Übung), practice (P) oder project (PA). For example a course of the form 2V+2U has 2 hours of lecture and 2 hours of exercise per week.
4V (4 hours per week)
ECTS credits:
European Credit Transfer System. Points for successful completion of a course. Each ECTS point represents a workload of 30 hours.
5
Semester: 2
Mandatory course: yes
Language of instruction:
German
Assessment:
Written exam (90 min. / Repeated semesterly)

[updated 30.06.2025]
Workload:
Workload of student for successfully completing the course. Each ECTS credit represents 30 working hours. These are the combined effort of face-to-face time, post-processing the subject of the lecture, exercises and preparation for the exam.

The total workload is distributed on the semester (01.04.-30.09. during the summer term, 01.10.-31.03. during the winter term).
60 class hours (= 45 clock hours) over a 15-week period.
The total student study time is 150 hours (equivalent to 5 ECTS credits).
There are therefore 105 hours available for class preparation and follow-up work and exam preparation.
Recommended prerequisites (modules):
BBABW-130 Business Mathematics


[updated 07.04.2014]
Recommended as prerequisite for:
Module coordinator:
Prof. Dr. Teresa Melo
Lecturer:
Prof. Dr. Teresa Melo
Dominique-Carsten Kellner
Michael Ohligschläger


[updated 07.04.2014]
Learning outcomes:
Submodule: Mathematical Economics 2
After successfully completing this module, students will be able to:
 
• model economic linear optimization problems,
• apply the most important methods of operations research to solve linear optimization problems,
• describe concepts of duality theory,
• economically interpret the solutions obtained using quantitative methods and perform a sensitivity analysis,
• use standard software for modeling and solving economic optimization problems,
• In addition, students will have developed 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,
• characterize empirical data with the help of statistical software,
• establish links to other areas of business studies and their practical application.


[updated 30.06.2025]
Module content:
Submodule Mathematical Economics 2:
• Introduction to linear optimization
• Creating models for business problems (e. g. production,
 logistics, marketing, investment)
• Graphical solution method for solving linear optimization problems
• Simplex method, economic interpretation of optimal solutions and conducting a sensitivity analysis
• Duality theory and its economic interpretation
  
Submodule Statistics 1:
• Classification of features
• Frequency tables for classified and non-classified data
• Graphical representation of univariate data sets
• Description of univariate datasets using measures of central tendency, dispersion and
 concentration
• Bivariate data analysis: Graphical representation of data sets and investigation of the  
 relationship between statistical characteristics (contingency, correlation, rank correlation)
• Linear regression
• Statistic software (e.g. SPSS)


[updated 30.06.2025]
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 30.06.2025]
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 30.06.2025]
 
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