Learn about advanced experimental designs to account for constraints: time, resources, material heterogeneity, randomisation restrictions as well as repeated measures. The construction of advanced designs and their analysis is covered.
Once influential factors are identified, the next goal consists of optimising their settings. This module covers the construction of experimental designs for optimisation. Data modelling is carried out with response surface methodology.
In preliminary research phases, the number of potentially influential factors to investigate is usually large. Screening designs are essential to identify the most influential factors with a reasonable number of runs in preliminary research phases.
Variation is present in every experiment. Learn about DOE techniques to control variation, and to maximise data quality. Commonly used experimental designs are discussed as well as the statistical data analysis tools.
In industrial applications, reliability is crucial and testing is expensive. Collected data must be exploited in the best way possible. Reliability data possess specific features that call for dedicated statistical methods. Learn about statistical tools for reliability analysis.
Learn key concepts in statistics. Classical and more recent exploratory data analysis techniques to efficiently summarise data and to detect outliers are covered. Statistical testing and decision-making in the presence of variation are also discussed.
Building a regression model with stats packages has become straightforward. However, interpreting the software output and building a good are no simple tasks. Learn the essentials of model-building, goodness-of-fit tools & common pitfalls.
Linear regression is inappropriate to model binary responses such as pass/fail, alive/dead. Learn the principle of logistic regression, its similarities with linear regression and its specific tools. Good practices for model-building are presented.