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| Detailed information |
| Original study plan |
Master's programme Mechanical Engineering 2025W |
| Learning Outcomes |
Competences |
| Students can perform basic probability calculations and apply simple inferential statistical methods such as one- and two-sample tests, linear regression, or analysis of variance to practical problems. Students are familiar with the principles of statistical experimental design, can create simple experimental designs, and appropriately analyze experimental results.
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| Skills |
Knowledge |
- Ability to solve basic probabilistic problems, particularly applications related to geometric, binomial, and normal distributions as well as Poisson processes (k3)
- Create appropriate descriptive summaries of data sets in the form of suitable distribution indicators, tables, and graphs and interpret the results (k3, k4)
- Create confidence intervals and conduct hypothesis tests for one- and two-sample problems, and select the appropriate method (k3, k4)
- Fit simple linear regression models, interpret the results, and assess the model quality (k3, k4, k5)
- Create simple factorial and fractional factorial experimental designs and understand their areas of application and limitations (k2, k3)
- Conduct statistical analyses of experimentally obtained data using single-factor or multi-factor analysis of variance (k3, k4, k5)
- Use Minitab software to solve the statistical problems discussed in class (k3)
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- Fundamental axioms and rules of probability theory
- Probability mass functions, moments, and areas of application of basic discrete probability distributions such as binomial, geometric, and Poisson distributions
- Probability density functions, moments, and areas of application of basic continuous probability distributions such as normal and exponential distributions
- Important location and dispersion indicators for summarizing descriptive data
- Important graphical data representations such as histograms, box plots, scatterplots, and probability plots
- Confidence intervals and one-sample t-tests for means
- Two-sample t-tests for the difference between means
- Model fitting, analysis, and assessment of model fit for simple linear regression
- Model fitting, analysis, and assessment of model fit for analysis of variance with one or more factors
- Construction of full and fractional factorial designs, analysis of the effects based on data obtained from these designs, and knowledge of the effects’ aliasing structure for fractional factorial experiments
- Important Minitab functions for data analysis and statistics
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| Criteria for evaluation |
Written exam, number of submitted homework examples
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| Methods |
Lecture (using slides and the blackboard), discussion of homework examples
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| Language |
German |
| Study material |
- Montgomery, D. C. & Runger, G. C. (2018): Applied Statistics and Probability for Engineers, 7th Edition. Wiley, Hoboken
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| Changing subject? |
No |
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