Statistics and Data Analysis

  • Data entry, solution methodology, solution interpretation
  • Descriptive statistics: subdomains in statistics
  • Concept of sample, population
  • Scientific scales (scale levels, types of scales)
  • Absolute, relative frequencies
  • Conditional and joint frequency distribution
  • Frequency distributions and histograms
  • Position measures (median, mode, mean)
  • Measures of dispersion (variance and standard deviation)
  • Two-dimensional frequencies and empirical distributions
  • Two-dimensional measures (empirical correlation and covariance)
  • Lorenz curve and Gini coefficient
  • Empirical regression lines
  • Information systems for data processing
  • Navigation and structure of common information systems
  • Functions in information systems
  • Linking data in information systems

Mode of delivery

face to face



Recommended or required reading and other learning resources/tools


Planned learning activities and teaching methods

Lecture, blended learning, presentation of group work, self-study

Assessment methods and criteria

  • Written final exam (70%, open questions, calculations)
  • Continuous assessment (30%): individual and group work, presentations, quizzes, active contribution in class
  • Content criteria: degree of problem identification and problem characterisation, complexity of solutions in terms of subject and methodological competence.
  • Formal criteria: completeness of answers, linguistic differentiation, and independence of the presentation of results.

Prerequisites and co-requisites



Degree programme

Logistics & Transport Management (Bachelor)



ECTS Credits


Language of instruction




Academic year



2 SS



Learning outcome

After successful completion of the course part statistics students can

  • name and explain basic terms and concepts of statistics and data analysis (1,2)
  • name, explain and apply important descriptive parameters of statistics (1,2,3)
  • name and explain basic forms of statistical distributions and their consequences on the methods to be applied (1,2)
  • name and explain the basic classes of statistical data/variables (scales) (1,2)
  • name and explain central statistical methods (1,2)
  • select and apply appropriate statistical methods according to situational requirements (2,3)
  • to explain and analyse the results of statistical evaluations in a basic way (2,4)
After successful completion of the course part data analysis students can
  • name common information systems for data processing (1)
  • explain and apply basic functions of information systems for data analysis (2,3)
  • link data (3)
  • process and evaluate data according to specifications with common information systems (3,4)

Course code