A training session specially designed to be offered online.
This workshop offers an introduction to the fundamental principles and concepts in statistics.
The first part covers classical and more recent exploratory data analysis (EDA) techniques to describe data with numerical and graphical tools. The various uses of these methods such as outlier detection is discussed.
The second part addresses, with the help of real-life examples, the principles underlying statistical testing and decision-making in the presence of uncertainty. It covers risks involved, effect size, p-values as well as statistical significance and practical relevance. The use and interpretation of confidence intervals is also discussed.
An excellent introductory module and a solid basis for all other courses.
sciences de la vie
Become more confident when discussing clinical results with leading healthcare providers. This course covers key biostatistical concepts required to review and interpret findings published in the biomedical literature. The exact course curriculum is tailored to the therapy area of interest. Selected scientific publications are reviewed, discussed and criticised.
DoE
Variation is present in every experiment. Learn about DoE techniques to control variation, and to maximise data quality.
This workshop presents classical techniques to design efficient experiments as well as the tools to analyze their results. The principles of sample size calculations, strategies to remove undesirable sources of variability like the use of blocks and controls, as well as the most commonly used experimental designs are discussed. The statistical analysis of designed experiments is progressively introduced, starting with the t-test method used to compare two groups. Then, the analysis of variance technique (ANOVA) is extensively covered from simple one-factor experiments to more advanced multi-factor situations where the interaction between factors needs to be considered. Multiple comparisons techniques used to locate differences are also presented.
plans d'expériences
Learn about advanced experimental designs to account for experimental various types of constraints such as time, available resources, material heterogeneity, randomization restrictions when certain factors are more difficult or costly to change than others, different sizes of experimental units as well as repeated measures. In this course, the construction of advanced designs and their statistical analysis is covered with the help of real case studies.
criblage
In preliminary research phases, the number of potentially influential factors to investigate is usually large. Screening designs are experimental designs used to identify the most influential factors that influence a response or outcome in a process or system with a reasonable number of runs. These designs are typically used in the early stages of experimentation, when you want to quickly assess a large number of variables to determine which ones have the greatest effect on the response variable. The goal is to eliminate unimportant factors and focus resources on the most influential ones. Learn about the construction of fractional factorial designs, aliasing and de-aliasing strategie. A working knowledge of multiple linear regression is needed to make the most out of this workshop.
