A training session specially designed to be offered online.
An easy and applied introduction 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.
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.
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 corresponding statistical data analysis tools.
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.
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.
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 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.
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.
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.
Learn about Principal Component Analysis, a data reduction technique, to identify, quantify & visualise the structure of a set of measurements. PCA provides insightful data visualisation tools. Learn about innovative applications.
Learn how to take data (consumers, genes, ...) and organise them into homogeneous groups for use in many applications, such as market analysis and biomedical data analysis, or as a pre-processing step for many data mining tasks. Learn about this very active field of research in statistics and data mining, and discover new techniques.
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.