Faculty of Business Management and Social Sciences


Version 18.0 of 01/30/2023

Code of Module


Modulename (german)


Study Programmes
  • Betriebswirtschaft und Management - WiSo (B.A.)
  • Internationale Betriebswirtschaft und Management (B.A.)
  • International Management (B.A.)
  • Betriebliches Informationsmanagement (B.Sc.)
  • Volkswirtschaftslehre (B.A.)
Level of Module


  1. Principles
    1.1 Data classification
    1.2 Data collection
  2. One-dimensional features
    2.1 Distributions and their graphic representation
    2.2 Key figures
    2.3 Economic applications
  3. Two-dimensional features & regression analysis
    3.1 Contingency tables
    3.2 Association dimensions
    3.3 Regression analysis
    3.4 Economic applications
  4. Measurements and index values
    4.1 Measurements
    4.2 Index values
    4.3 Economic applications
  5. Elementary time series analysis
    5.1 Trend determination
    5.2 Estimation of components
    5.3 Economic applications
  6. Random variables and distributions
  7. Estimation and testing procedures
    7.1 Point and interval estimations
    7.2 Testing procedures
    7.3 Economic applications
  8. Analysis of economic data using statistics software
    8.1 Introduction to statistics software
    8.2 Computer-aided graphic representation of data
    8.3 Computer-aided statistical computation
Learning Outcomes

Knowledge Broadening
The students know the different methods to prepare and to present static data. They are able to understand and to interpret diagrams, tables, frequency distributions, statistical measures und indexes. The students know the differences between one-dimensional and two-dimensional features.
Knowledge Deepening
The students are able to carry out independently a statistical study in a company. They can prepare the results graphically and in table form and interpret it comprehensively. Finally they can analyse the basic material and can transform the results into understandable reports. They can verify hypotheses.
Instrumental Skills and Competences
The students:
- carry out data collections
- can differentiate characteristics by the scale
- know how the absolute and the relative frequencies are defined and can
draw up frequency tables
- can calculate statistical measures and indexes
- can carry out a simple regression analysis
- can calculate key figures
- can recognise a time serie and calculate the most important parameters
- can verify hypothesis with statistical methods of testing
- can estimate parameters
- can calculate simple key figures by means of statistic software
Communicative Skills and Competences
The students learn how to use data. They can evaluate data and they can interpret and communicate the results. They are able to verify hypotheses and to estimate parameters.
Systemic Skills and Competences
The students are able to justify their decisions by means of statistical methods and analysis.

Mode of Delivery

Lectures, exercises, case studies, self-study, e-Learning

Expected Knowledge and/or Competences


Responsible of the Module

Markovic-Bredthauer, Danijela

  • Faatz, Andreas
  • Neumann, Ludger
  • Hübner, Ursula Hertha
  • Markovic-Bredthauer, Danijela


Concept of Study and Teaching
Workload Dozentengebunden
Std. WorkloadLehrtyp
Workload Dozentenungebunden
Std. WorkloadLehrtyp
Recommended Reading

Chapman, C. N. (2015). R for Marketing Research and Analytics (2015th ed.). New York, NY: Springer.

Field, A. (2013). Discovering Statistics Using SPSS (4th Edition.). Los Angeles: Sage Publications Ltd.

Field, A., & Miles, J. (2012). Discovering Statistics Using R. London ; Thousand Oaks, Calif: Sage Publications Ltd.

McClave, J. T., Benson, P. G., & Sincich, T. L. (2013). Statistics for Business and Economics: Pearson New International Edition (12th ed.). Pearson.

Graded Exam

Two-Hour Written Examination

Assessment Methods Remark



1 Term

Module Frequency

Winter and Summer Term

Language of Instruction