Statistics and Data Analysis

Brief description

• 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

Type

compulsory

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

Infos

Degree programme

Logistics & Transport Management (Bachelor)

Cycle

Bachelor

ECTS Credits

3.00

Language of instruction

German

Curriculum

Full-Time

Academic year

2026

Semester

2 SS

Incoming

Yes

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

0391-21-01-VZ-DE-22

Statistics and Data Analysis