Each module contains 3 ECTS. You choose a total of 10 modules/30 ECTS in the following module categories:
- 12-15 ECTS in technical scientific modules (TSM)
TSM modules teach profile-specific specialist skills and supplement the decentralised specialisation modules.
- 9-12 ECTS in fundamental theoretical principles modules (FTP)
FTP modules deal with theoretical fundamentals such as higher mathematics, physics, information theory, chemistry, etc. They will teach more detailed, abstract scientific knowledge and help you to bridge the gap between abstraction and application that is so important for innovation.
- 6-9 ECTS in context modules (CM)
CM modules will impart additional skills in areas such as technology management, business administration, communication, project management, patent law, contract law, etc.
In the module description (download pdf) you find the entire language information per module divided into the following categories:
Students are introduced to statistical tools used in the industrial sector, and particularly in process and quality control. In this module, students learn to plan and conduct statistical evaluations independently.
Please note: An MSE cursus may not contain both similar statistics modules FTP_AppStat and FTP_PredMod. Students can only choose one of these modules.
Basic knowledge of the calculation of probabilities and statistics: models; parameter estimation; knowledge of how statistical tests are compiled and what confidence intervals are; user knowledge of a statistical program (Excel, R, S-PLUS, SPSS or MATLAB); fundamental laboratory experience (measuring technology)
To be in a position to plan and evaluate experiments in an industrial environment; understand how processes are statistically controlled and improved; be capable of analyzing and interpreting data by means of regression analysis; be able to implement the methods covered with a statistical package.
Contents of Module
Statistical process and quality control (SPC): the "Magnificent Seven“, control charts, operating characteristic curve, acceptance sampling (weighting 1/3)
Introduction to multiple regression analysis: model prerequisites, confidence and prediction intervals, graphic checking of model assumptions (weighting 1/3)
Overview of Design of Experiment – DoE (planning and evaluating experiments): basic principles for the planning of experimental studies, one-way and multi-way analysis of variance, factorial experiment designs and their analysis, block designs (weighting 1/3)
The contents listed are illustrated with case studies from the industrial and scientific environment. In doing so, use is made of graphical methods and statistical bases, including classic and robust estimation methods and Monte Carlo simulations.
Teaching and Learning Methods
Lectures, practical work on the computer with a statistics program
Lecturers' scripts with references to current literature