Topics in the body text of this DOE FAQ Alert are headlined below (the "Expert" ones, if any, delve into statistical details):

P.S. Quote for the month: The payoff for doing an experiment.

(Page down to the end of this e-zine to enjoy the actual quote.)

1: FAQ: Can I disregard the transformation recommended by the Box-Cox plot?

Original question from a Manufacturing Support Scientist:“For some analyses, the transformation recommended by Box-Cox results in a weaker model (higher p-value). Should the suggestion be disregarded in this case, that is, analyze the data as-is? Also, sometimes the Box-Cox recommends a certain transformation, and after doing it, it then recommends another type of transformation. Would you recommend to always do whichever transformation it settles on?”

Answer from Stat-Ease Consultant Shari Kraber:“I recommend using the model that produces the highest predicted R-squared. When Box-Cox changes its recommendation after doing the transformation, go with the final one. In any case, you must be the judge of which model seems most sensible based on all the statistics and your subject-matter knowledge.”

(Learn more about transformations by attending the three-day computer-intensive workshop on Modern DOE for Process Optimization. Click on the title for a description of this class and link from this page to the course outline and schedule. Then, if you like, enroll online.)

2: FAQ: Must I accept ranges adjusted by Design-Expert® software for mixture designs?

Original question from a Materials Technology Researcher:“I attended your recent webinar on mixture design: “Formulation Simplified”.* I found it very useful and clear. Thank you for the effective presentation. You showed how Design-Expert automatically corrects variable ranges when their sum exceeds 100%. Sometimes the new ranges differ considerably from the original ones. Is it mandatory to accept them?”

* Slides and a recording are posted here.

Answer: Thank you for your kind words. You are very welcome. You are correct: when ranges are too wide on some components to accommodate others within the fixed total, then Design-Expert must make arbitrary adjustments that may not be what the user wants. In such a case, the user should over-ride the suggested changes with their own ones that will make the constraints feasible.

The detergent case that I showed in my webinar is instructive in this regard. After specifying a total of 9%, the chemist went for 3-8% of ingredient A, and 2-4% each for ingredients B and C. That cannot be accommodated because, with 8% of A and 2% each of the other ingredients, the total comes to 12%. Therefore, the program suggests decreasing the upper end of A to 5%. But let’s say the chemist really does want A to go as high as 8%. Then this experiment would need to reduce the low limit of B and C to 0.5% each, thus making it possible to keep within the 9% total.

(Learn more about dealing with ingredient constraints by attending the computer-intensive three-day workshop Mixture and Combined Designs for Optimal Formulations. Click on the title for a complete description of this class. Link from this page to the course outline and schedule. Then, if you like, enroll online.)

3: Info alert: DOE speeds the development of a specialty chemical

The August issue of the Asia-Pacific Coatings Journal features two mixture designs (“Experimental Developments”, page 45) accomplished via Design-Expert software. Via this multicomponent methodology, Vectra, a specialty chemical manufacturer, quickly matched a competitor’s formulation, thus securing second-supplier status.  This case study epitomizes the value of statistical methods for product development.

4: Events alert: Looking for a speaker on the topic of DOE for a 2018 meeting?

Do you need a speaker on DOE for a learning session within your company or technical society at regional, national, or even international levels? If so, contact me. It may not cost you anything if Stat-Ease has a consultant close by, or if a web conference will be suitable. However, for presentations involving travel, we appreciate reimbursement for travel expenses. In any case, it never hurts to ask Stat-Ease for a speaker on this topic.

5: Workshop alert: Resolve to sharpen your DOE skills—sign up now for a 2018 class

You can do no better for quickly advancing your DOE skills than attending a Stat-Ease workshop. In these computer-intensive classes, our expert instructors provide you with a lively and extremely informative series of lectures interspersed by valuable hands-on exercises with one-on-one coaching. Enroll at least 6 weeks prior to the date so your place can be assured—plus get a 10% “early-bird” discount.

• Experiment Design Made Easy (EDME)
• Apr 24–25, Minneapolis, Minnesota (MN)
• Modern DOE for Process Optimization (MDOE)
• February 21-23, Orlando, FL
• Apr 24–26, Minneapolis, Minnesota (MN)
• Mixture and Combined Designs for Optimal Formulations (MIXC)
  • Mar 14–16 Cleveland, OH
  • Jun 26–28, Minneapolis, Minnesota (MN)
• Designed Experiments for Pharma (DEPH)
  • Apr 9–10 Philadelphia, PA

See this web page for complete schedule and site information on all Stat-Ease workshops open to the public. To enroll, scroll down to the workshop of your choice and click on it, or call our Client Specialist Rachel Pollack, at 612-746-2030. If spots remain available, bring along several colleagues and take advantage of quantity discounts in tuition. Or, consider bringing in an expert from Stat-Ease to teach a private class at your site.*

*Once you achieve a critical mass of about 6 students, it becomes very economical to sponsor a private workshop, which is most convenient and effective for your staff. For a quote, e-mail [email protected].

I hope you learned something from this issue. Address your general questions and comments to me at: [email protected].

Please do not send me requests to subscribe or unsubscribe—follow the instructions at the end of this message.

Sincerely,

Mark

Mark J. Anderson, PE, CQE
Principal, Stat-Ease, Inc.
2021 East Hennepin Avenue, Suite 480
Minneapolis, Minnesota 55413 USA

P.S. Quote for the month: How not to ‘urn’ your keep when teaching statistics.

"Every time man makes a new experiment he always learns more. He cannot learn less.”

—R. Buckminster Fuller

Trademarks: Stat-Ease, Design-Ease, Design-Expert and Statistics Made Easy are registered trademarks of Stat-Ease, Inc.

Acknowledgements to contributors:
—Students of Stat-Ease training and users of Stat-Ease software
—Stat-Ease consultants Pat Whitcomb, Martin Bezener, and Shari Kraber
—Stat-Ease programmers Hank Anderson, Neal Vaughn, Joe Carriere and Jon Kraber
—Heidi Hansel Wolfe, Stat-Ease sales and marketing director, and all the remaining staff that provide such supreme support!

For breaking news from Stat-Ease go to our Twitter feed.

DOE FAQ Alert ©2017 Stat-Ease, Inc.
Circulation: Over 6000 worldwide
All rights reserved.