Case Studies and White Papers
Computer-Aided Design of Experiments for Formulations
See how to apply statistically based design of experiments (DOE) for mixtures - a proven method for making breakthrough improvements in cost and performance. Ultimately you may discover a sweet spot where all your customer specifications can be satisfied. To illustrate the method, this article lays out a case study on the formulation of rheology modifiers. (A somewhat different version of this article appeared in Modern Paint and Coatings.)
Optimize Your Process-Optimization Efforts
What would you do it confronted with an "opportunity" to make a major change, involving many factors, but you need to do it quickly? The traditional approach to experimentation requires you to change only one factor at a time (OFAT). However, the OFAT approach doesn’t provide data on interactions of factors, a likely occurrence with chemical processes. An alternative approach called “two-level factorial design” can uncover critical interactions. This statistically based method involves simultaneous adjustment of experimental factors at only two levels, offering a parallel testing scheme that’s much more efficient than the serial approach of OFAT.
Talk by Pat Whitcomb and Mark Anderson that was presented at the 50th Annual Quality Congress.
A Balancing Act: Optimizing a Product's Properties
G.C. Derringer provides an easy-to-read explanation of the commonly used optimization function called desirability. When used as the final step in DOE, this function allows simultaneous optimization of multiple responses, resulting in the discovery of a group of optimal factor settings.
Applying DOE to Microwave Popcorn
Design of experiments identifies which factors matter and which ones don't when microwaving popcorn, as well as helping find optimal settings.
Analyzing Two-Level Factorials Having Missing Data
This presentation details and demonstrates a procedure that, despite missing data, allows the use of user-friendly, normal-probability plots for two-level-factorial effect selection.
A Simple Comparative Experiment with Paper Clips
Details and demonstrates a fun experiment to do at home or in class to build understanding of variation and how it can be handled with simple comparative designs. For teaching purposes it works best if each student breaks two brands of clips, thus providing data for a paired t-test, which blocks out variability due to the tester.
An updated version of paper-clip experiment is provided in the June 2009 Stat-Teaser posted at https://cdnm.statease.com/news/news0906.pdf.
A look at augmenting the usual probability plot effects with points representing pure error.