## 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

face to face

compulsory

N.N.

### 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.

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### Infos

#### Degree programme

Logistics & Transport Management (Bachelor)

Bachelor

3.00

German

Full-Time

2024

2 SS

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)