This module offers an easy introduction to key concepts in statistics. Laboratory staff will learn to to efficiently visualize data, detect trends and outliers. They will also learn how to best summarize data. Statistical testing will be demystified: risks (alpha and beta), p-values, statistical significance, confidence and power are defined and illustrated. 

Shelf-life data possess specific features so that their design and analysis require adapted statistical tools. Key elements in the study design are presented and survival techniques used to analyse life data are covered and illustrated.

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.

An easy intro to key statistical concepts. Learn about data visualisation & outlier detection. Demystify statistical significance testing – p-values, significance, power, confidence, etc. An excellent introductory module and a solid basis for all other courses.

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.

Learn key biostatistical concepts to review & interpret findings published in the medical literature. Selected scientific publications are reviewed, discussed and criticised in terms of bias, uncertainty & scope.

Learn about key biostatistical concepts and efficient tools for summarising & visualising data. Demystify the statistical testing approach used to make decision in the presence of uncertainty.

Research question often require the use of a combination of multivariate data analysis techniques. This course covers advanced multivariate analysis applications for mapping purposes, the selection of representative items in groups, segmentation and prediction, and many more.