Stat-Ease is proud to offer free webinars to those interested in Design of Experiments (DOE). 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 e-mail 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. Brooks did a fine job yesterday presenting RSM Part 2. I've also listened to past webinars presented by other instructors and they also do an excellent job of explaining DOE. Thank you, Stat-Ease!"
—Jeff Reimer, Principal Scientist, Sigma-Aldrich Corporation
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.