Data Science & Intelligent Analytics PT
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Quantitative Process & Quality Management (Six Sigma) (elective)

level of course unit

Master's course

Learning outcomes of course unit

- know the basics of descriptive and conclusive statistics.
- know how to examine measuring arrangements for repeatability and reproducibility.
- know how to calculate sample sizes.
- know how to monitor the stability of process results using statistical monitoring methods.
- know how to evaluate the ability of processes to meet customer requirements.
- know methods to search for the deviation causes in results using test procedures.
- know basic functionalities of the "Minitab" statistics software.
- know how to use "Minitab" in the context of process analysis.

prerequisites and co-requisites

not applicable

course contents

The following content is discussed in the course:

- Basics of descriptive statistics
- Measurement system analysis
- Sample determination
- Statistical process monitoring
- Process monitoring charts
- Process capability analysis
- Components of Variants Analysis (COV)
- Repetition Basics of inferential statistics
- Failure cause determination via hypothesis testing (T-test, Chi-Sq, ANOVA)
- Multiple regression analysis

recommended or required reading

Töpferer, A.; Six Sigma Konzeption und Erfolgsbeispiele für praktizierende Null-Fehler-Qualität; Berlin/Heidelberg/New York 2007; 4th edition
George M.; Rowlands D.; Price M.; Maxey J.; The Lean Six Sigma Pocket Toolbook; New York; 2005
Lunau St. (publisher); Six Sigma + Lean Toolset; 5th edition; Heidelberg; 2014

assessment methods and criteria

Written exam or seminar thesis

language of instruction


number of ECTS credits allocated


eLearning quota in percent


course-hours-per-week (chw)


planned learning activities and teaching methods

Lecture, individual work with software, group work, presentation and discussion of tasks

semester/trimester when the course unit is delivered


name of lecturer(s)

Prof. (FH) Dr. Martin Adam

year of study

2.year of studies

recommended optional program components

not applicable

course unit code


type of course unit

integrated lecture

mode of delivery


work placement(s)