Webinars (Advanced)


Presented by: Martin Bezener on Aug. 8, 2022
Category: Advanced

Building up from the Mixture DOE Crash Course, this webinar explains how formulators can create experiment designs that combine mixture components with process factors, include categorical factors, and deal with hard-to-change variables.

Presented by: Mark Anderson on June 10, 2022
Category: Advanced

In this talk, Mark Anderson details cost-saving mixture-process methods invented by statisticians Kowalski, Cornell and Vining (KCV) and implemented by Stat-Ease. The KCV tools streamline combined designs by focusing on the interactions—the hidden gold remaining buried by traditional experimentation. Via a real-world example, Mark will present experiment-design and modeling methods that make combined mixture-process studies practical for chemists.

Presented by: Martin Bezener on Nov. 10, 2021
Category: Advanced

In many cases, experimental data is the result of a deterministic simulation rather than a lab experiment. These may be referred to as computer experiments. Such situations need special experimental designs and data analysis tools. See how Stat-Ease 360 fills this need with via space-filling designs and Gaussian process models.

Presented by: Patrick Whitcomb on Dec. 15, 2020
Category: Advanced

In this advanced-level webinar, Stat-Ease Consultant Pat Whitcomb discusses robust design, propagation of error, and tolerance analysis. Propagation of error (POE) accounts for variation transmitted from deviations in factor levels. It finds the flats—high plateaus or broad valleys of response, whichever direction one wants to go—maximum or minimum; respectively. Tolerance analysis drills down to the variation of individual units, thus facilitating improvement of process capability.

Presented by: Patrick Whitcomb on Sept. 16, 2020
Category: Advanced

Discover the secrets to customizing your experiments using optimal (custom) designs. Learn the importance of adding lack of fit points and replicates. All these issues are considered at a practical level – keeping the actual experimenters in mind.

Presented by: Patrick Whitcomb on Jan. 22, 2019
Category: Advanced

Discover how to optimize your process while avoiding impossible factor combinations.