Information on individual educational components (ECTS-Course descriptions) per semester

Degree programme: Master Business Administration: International Marketing & Sales
Type of degree: FH Master´s Degree Programme
Summer Semester 2020

Course unit title Statistics
Course unit code 080321021031
Language of instruction English
Type of course unit (compulsory, optional) Compulsory
Semester when the course unit is delivered Summer Semester 2020
Teaching hours per week 2
Year of study 2020
Number of ECTS credits allocated Second Cycle (Master)
Number of ECTS credits allocated 4
Name of lecturer(s) Gunther ROTHFUSS

Prerequisites and co-requisites


Course content
  1. What is statistics? Application examples from different industries and business sectors with the aim of making the breadth of statistics as a science visible
  2. Central concepts of statistics
  3. The set-up of a statistical study
  4. Possibilities and limitations of statistics software
  5. Combinatorics (Core concepts, Combinatorics as an interdisciplinary subject, the combinatorial explosion)
  6. A feeling for large amounts of data (Histograms, Measure of location, Measure of scale, an important example of the analysis of correlations: regression)
  7. Statistics as part of stochastics (Elementary concepts of probability theory in particular, events, random variables and density functions; Things that should be known from probability theory
  8. Probability distributions
  9. Dependent probabilities and the Bayesian Sets
  10. Statistics as a metacognitive problem

Learning outcomes

As preparation for the Masters thesis, this course provides the necessary methodological basis for applying statistical methods in scientific work - from fields of application and major concepts to practical application in concrete everyday problem situations.

At the end of the course, students will

  • be able to name important fields of application and major concepts of statistics and probability theory as well as describe their most important functions and characteristics,
  • understand how a statistical study is conducted,
  • know the relationship between statistics and probability theory from a user perspective,
  • specify a solution strategy for simple statistical problems and design a study plan, identify appropriate methods or tests and apply them correctly to the problem,
  • participate in discussions of statistical problems on a professional level,
  • be able to assess the suitability of statistical methods for specific problems.

Planned learning activities and teaching methods
  • Lectures
  • Coaching exercises
  • Discussions and open educational talks

Assessment methods and criteria

Open-book exam with multiple choice questions, open comprehension questions and calculating tasks



Recommended or required reading
  • Balakrishnan, N.; Nevzorov, V. B. (2003): A Primer on Statistical Distributions. New. Hoboken, N.J: John Wiley & Sons.
  • Field, Andy (2016): An Adventure in Statistics: The Reality Enigma. Thousand Oaks, CA: Sage Publications Ltd.
  • Hastie, Trevor; Tibshirani, Robert; Friedman, Jerome (2017): The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition. 2nd ed. 2009, Corr. 9th printing 2017. New York, NY: Springer.
  • Montgomery, Douglas C. ; Runger, George C. (2013): Applied Statistics and Probability for Engineers. 6. Aufl. Hoboken, NJ: John Wiley & Sons Inc.

in German:

  • Schira, Josef (2016): Statistische Methoden der VWL und BWL: Theorie und Praxis. 5. Aufl. Hallbergmoos: Pearson Studium.
  • Bleymüller, Josef  u. a. (2015): Statistik für Wirtschaftswissenschaftler. 17. Aufl. München: Vahlen.

Mode of delivery (face-to-face, distance learning)

Face-to-face instruction with mandatory attendance.