|Module name (EN): Experiment Design and Quality Control|
|Degree programme: Automotive Engineering, Master, ASPO 01.04.2021|
|Module code: FTM-VUQ|
|Hours per semester week / Teaching method: 2V (2 hours per week)|
|ECTS credits: 3|
|Semester: according to optional course list|
|Mandatory course: no|
|Language of instruction:
|Applicability / Curricular relevance:
FTM-VUQ Automotive Engineering, Master, ASPO 01.04.2021, optional course, technical
FTM-VUQ Automotive Engineering, Master, ASPO 01.04.2023, optional course, technical
MAM.220.127.116.11 (P241-0367) Engineering and Management, Master, ASPO 01.10.2019, optional course, technical
30 class hours (= 22.5 clock hours) over a 15-week period.
The total student study time is 90 hours (equivalent to 3 ECTS credits).
There are therefore 67.5 hours available for class preparation and follow-up work and exam preparation.
|Recommended prerequisites (modules):
|Recommended as prerequisite for:
Prof. Dr. Gerald Kroisandt
|Lecturer: Prof. Dr. Gerald Kroisandt
After successfully completing this module and based on the statistical knowledge they acquired in MAM_19_A_1.01.MTS, students will be able to determine confidence intervals for a wide range of mean values and variances. They will also understand how process control charts work.
Students will understand tests, and in particular how to proceed when choosing a hypothesis and an alternative.
As with confidence intervals, they will be able to design appropriate tests for a wide range of situations.
If something depends on several factors, e.g. the load capacity of a component, students will be familiar with common methods for designing experiments and will be able to apply them.
The question as to which factor(s) produce differences in quality is examined by analysis of variance, which students will also be able to apply.
- Point estimator (ML estimator) and mean-squared error for quality assessment
- Confidence intervals for diverse situtaions
- Basics of process control charts
- Hypothesis testing for different situations
- Designing experiments
- Analysis of variance
|Recommended or required reading:
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