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R & D

R & D

Learn about Design of Experiments (DOE) and tools for statistical analysis. This course is aimed at researchers, laboratory staff and more broadly staff involved in any discovery process.

An easy intro to key statistical concepts. Learn about data visualization and outlier detection. Demystify statistical significance testing. An excellent introductory module and a solid basis for all other modules.

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.

Efficient experiments must be large enough to detect meaningful scientific differences and maximize the use of available resources. Learn about sample size and power calculations.

Learn about advanced experimental designs to account for constraints: time, resources, material heterogeneity, randomisation restrictions as well as repeated measures. The construction of avanced 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 modeling is carried out with response surface methodology.

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 analyze life data are covered and illustrated.

Several experiments are conducted to determine whether differences exists between procedures, methods, treatments. Learn about the design and the analysis of simple comparative experiments and more complex situations.

Classical statistical methods such as ANOVA, linear regression rely on certain data distribution assumptions. Whenever they are not met, alternative methods such as nonparametric may be used. Learn about their principle, advantages and limitations.