This webinar provides valuable insights on Stat-Ease® 360 software’s special modeling tools for binary data, counts, and deterministic results (such as those collected from computer simulations). The focus will be on the practical aspects, with minimal emphasis on theory and technical details.
Before embarking on expensive experiments, it often pays to mine existing data. It may be gold, or it may be garbage, but why not try? This webinar demonstrates how easily Stat-Ease software imports results so you can then apply its powerful tools for evaluation, analysis and optimization.
Learn how Python has been integrated into Stat-Ease 360. This tutorial walks through connecting Python, extracting data from SE360, and some other more complex examples.
This talk features four examples making use of Design-Expert’s comprehensive design-building facilities to build the desired design while not revealing everything to DX.
Pat Whitcomb, Stat-Ease founder, illustrates how to take best advantage of designs geared for hard-to-change process settings. While running through a number of case studies with Design-Expert® software, he provides statistical details and practical advice on the pluses and minuses created by the split-plot factor layout.
Via a series of case studies illustrating Design-Expert® software’s new Poisson regression tool, Engineering Consultant Mark Anderson provides practical aspects for modeling counts; e.g., manufacturing defects. He will contrast and compare Poisson regression with ordinary least square regression (with and without a transformation).
Optimize your products and processes with accurate prediction models. Learn how to get the most out of your RSM design by following a few key analysis steps. See how automated model-reduction tools build simpler models that predict more precisely. Then discover how diagnostics confirm your model’s validity. Finally, learn how key statistics like lack of fit and various R-squared measures characterize the polynomial model.
This talk deals with thorny issues that confront every experimenter: How to handle results that fit badly with your chosen model. Design-Expert software provides graphical tools that make it easy to diagnose what is wrong—damaging outliers and/or a need for transformation. A variety of case studies will demonstrate the value of these diagnostics.
How to use automatic model selection tools to build on appropriate models. Pros and cons of the methods are discussed.
Topics include foldovers, semifoldovers, building a CCD from a one-factor-at-a-time (OFAT) study, and optimal augmentation for RSM designs.
A briefing on QbD, along with state-of-the-art response surface methods (RSMs) for developing a robust design space.