Stat-Ease is proud to offer free webinars to those interested in design of experiments (DOE) and Design-Expert® software. Presented on a regular basis, topics range from beginner to advanced levels. Material may be new or drawn from our ever-popular DOE workshops.
If there is a particular subject you are interested in and don't see below, send us an email and let us know. If there is enough interest, we may present your topic in a future webinar.
"Thank you very much for the continuing education opportunities through Stat-Ease's webinar offerings. The instructors do an excellent job of explaining DOE. Thank you, Stat-Ease!"
—Jeff Reimer, Principal Scientist, Sigma-Aldrich Corporation
Richard Williams, Stat-Ease Consultant, will detail what design of experiments (DOE) can do for you when catalyzed with DX12’s world-class statistical tools. Learn about factorial design, the core tool for DOE, followed by a peek at response surface methods (RSM) for process optimization and last, but not least, a look into mixture design for optimal formulation. Whether you want to do more with Design-Expert as a current user, or need a better tool for doing DOE, or enlightenment as to what these powerful multifactor-testing methods can do for you, this free webinar will be of great benefit.
Date: April 17, 2020
Time: 1:00pm ET/12:00pm CT/11:00am MT/10:00am PT
Pat Whitcomb details the cost-saving mixture-process models developed by Scott Kowalski, John Cornell and Geoff Vining (KCV). Design-Expert® software, version 12, drops this modeling tool right into the user's hands. See how it reduces the number of model terms and thereby reduces the number of runs required to estimate the complex relationship between mixture and process variables. Estimated length: 45 min.
Details what design of experiments (DOE) can do for you when catalyzed with DX12’s world-class statistical tools. Learn about factorial design, followed by a peek at response surface methods (RSM) for process optimization and a look into mixture design for optimal formulation.
Mark Anderson, Stat-Ease Consultant and lead author of the DOE/RSM/Formulation Simplified trilogy, will detail what design of experiments (DOE) can do for you when catalyzed with DX12’s world-class statistical tools.
Martin Bezener, Stat-Ease Consultant, introduces Design-Expert v12’s new tools for logistic regression.
Discover how to optimize your process while avoiding impossible factor combinations.
Learn about factorial design, the core tool for DOE, followed by a peek at response surface methods (RSM).
This case-study driven webinar is a must for all who experiment on APIs. Learn how to apply statistically valid, multifactor and multicomponent testing strategies that catalyze your development work.
Use mixture design tools for multi-component product development and optimization, perfect for formulators. Learn why factorial designs won't work.
Use graphical tools (half-normal & Pareto plots) to select effects quickly and accurately.
How to use automatic model selection tools to build on appropriate models. Pros and cons of the methods are discussed.
Learn how multivariate analysis (MVA) methods can be used in combination with design of experiments (DOE) tools. Presenters demonstrate how to build an optimal design from principal components and use the DOE results to find an optimal compound.
Learn how to use power and precision to properly size factorial and RSM designs.
Topics include foldovers, semifoldovers, building a CCD from a one-factor-at-a-time (OFAT) study, and optimal augmentation for RSM designs.
We have an answer for your question: "How many runs do I really need?"
A briefing on QbD, along with state-of-the-art response surface methods (RSMs) for developing a robust design space.
Discover how propagation of error and tolerance analysis can account for variation in your system.
Topics include using optimal designs for constrained design spaces, adding categoric factors, higher-order models, and design augmentation.
Review central composite designs and multiple response optimization.
Learn why, when, and how to use algorithmic (optimal) designs for your experiments.
A dual response approach to response surface methods (RSM) provides a statistically sound way to make your process more robust.
Learn the differences between replicates, duplicates, and repeats, as well as the reasons for using each. Cost-based decision selection of one versus another are discussed.