A training session specially designed to be offered online.
optimisation
Optimization designs refer to experimental design strategies that are specifically structured to optimize a process, product, or system. The goal of these designs is to identify the combination of factors (inputs) that lead to the best possible outcome (response) according to a defined objective, such as maximizing performance, minimizing cost, or finding the most efficient operating conditions. Learn more about experimental designs when influential factors have been identified and the goal is to optimize their levels. Principle underlying the construction of composite and Box-Behnken design are covered. Principle, model-building, and response surface methodology are reviewed.
Linear
The linear regression is a method used to model the relationship between a dependent variable and one or more independent variables by fitting a linear equation to observed data. Building a regression model with stats packages has become straightforward. However, interpreting the software output and building a good model are no simple tasks.
Learn about statistical modeling with a focus on linear models. What is a model? Estimating and interpreting model coefficients. Dealing with continuous and categorical predictors and interactions. Evaluating model performance: explanatory vs. predictive. Common pitfalls and best practices. Introduction to nonlinear regression.
Linear regression is inappropriate to model binary responses such as pass/fail, survived/died.
Learn the principle of logistic regression part of the Generalized Linear Models along with its similarities with linear regression and its specific tools. Good practices for model-building and for assessing model goodness-of-fit are presented.
Shelf-life data possess specific features so that their design and analysis require adapted statistical tools.
This 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.
Lifetime data possess specific features so that their design and analysis require adapted statistical tools.This workshop uses a variety of case studies to present the most important aspects to consider for a sound determination of survival curves.
Participants learn for each type of study how to design efficient experiments to determine the survival time of patients. The issues discussed include the timepoint selection, how to handle censoring (incomplete observations), competing risks, and the experiment size.
The workshop also emphasizes the appropriate ways to analyze life data, how to compare curves, how to account for time-varying covariates and to adequately interpret and communicate the results obtained.
