No-confounding designs with 24 runs for 7-12 factors Online publication date: Sat, 21-Oct-2017
by Brian B. Stone; Douglas C. Montgomery; Rachel T. Silvestrini; Bradley Jones
International Journal of Experimental Design and Process Optimisation (IJEDPO), Vol. 5, No. 3, 2017
Abstract: When experimenting with more than six independent variables, researchers under significant resource or time constraints often require alternatives to large Resolution V 2k-p fractional factorial designs. Many researchers also desire to avoid the expense of a foldover experiment required to de-alias completely confounded two-factor interactions (2FIs) when using resolution IV 2k-p designs. No-confounding designs are an excellent solution to this problem, as they have orthogonal main effects (ME) and no 2FI is completely confounded with another ME or 2FI. This paper introduces 24-run no-confounding designs for 7-12 factors. It presents a Monte Carlo simulation methodology used to evaluate algorithmically constructed designs and those in the existing literature. The results report the best-performing designs and metrics related to their types I and II error rate from the variable-selection process during repeated simulations of regression analyses.
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Experimental Design and Process Optimisation (IJEDPO):
Login with your Inderscience username and password:
Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.
If you still need assistance, please email subs@inderscience.com
Our Blog 
Follow us on Twitter 
Visit us on Facebook