Training Sessions in Statistics

Engineering

In this category you will find training courses to help engineers in the industry design efficient experiments with "Design of Experiment" methods (DOE) as well as sessions which cover the analysis of the data collected in such experiments: reliability, failure time estimation, etc.

Session Name+	Summary	Length (days)
Introduction to the Design of Experiments (DOE)	This 2-day hands-on 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.	2.0
Advanced Experimental Designs	This training course covers advanced experimental designs to account for different constraints that experimenters have to deal with, such as: time, resources, material heterogeneity, repeated measures on subjects and randomization constraints. It also deals with how to analyze data and interpret results based on the design selected.	2.0
Screening Designs	This 1-day training reviews how to construct advanced experimental designs to screen for the most influential factors to study in future experiments or studies. These designs require very few runs, thus minimizing the time and cost needed at this preliminary phase.	1.0
Optimization Designs and Response Surface Models	This 2-day course covers the construction of advanced experimental designs specially designed to optimize a process or formulation according to the study objectives and using a minimum number of runs.	2.0
Repeatability and Reproducibility Studies (Gage R&R)	This session discusses how to set up repeatability and reproducibility studies (R&R) and how to analyze the results with the appropriate statistical methods. The main goal of R&R studies is to determine which sources of variability contribute the most to the overall process variation and how to decide if the measurement system is acceptable or not.	1.0
Fundamental Tools in Statistics	This hands-on workshop offers an introduction to the fundamental principles & 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 like outlier detection is presented. 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 (alpha and beta), p-values and statistical significance. The use and interpretation of confidence intervals is also discussed. This course can serve as an introductory class or a refresher and provides a solid basis for all other courses.	1.0
Regression on Principal Components and PLS Regression	This workshop covers the problem of correlated explanatory variables (multicollinearity) in regression models, its impact on the model performance and discusses PLS regression, the most popular technique to deal with this problem. It first introduces the underlying concepts in PLS regression through its link with ultivariate methods, namely Principal Component Analysis (PCA). Focus is also put on strategies used to measure the predictive ability of models and their usefulness to optimise the PLS model-building phase. Case studies are used so that participants gain confidence in the interpretation of software output and to make them aware of the conditions of use and common pitfalls.	1.0
Efficient Design & Analysis of Shelf-Life & Stability Studies	This 1-day workshop uses a variety of case studies to present the most important aspects to consider for a sound determination of product shelf life. Starting from the assessment of the differences between shelf-life and stability studies, participants learn for each type of study how to design efficient experiments to determine the failure time of products accurately. The issues discussed include the timepoint selection, how to handle destructive testing, the experiment size and the choice of samples. The workshop also emphasizes the appropriate ways to analyze life data and to adequately interpret and communicate the results obtained. The principle of accelerated shelf-life testing (ASLT) along with the conditions for a successful use are discussed. Throughout the session, FDA and other existing guidelines will be discussed.	1.0

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Efficient Design & Analysis of Shelf-Life & Stability Studies

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