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.
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.