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: Overlooked words of wisdom from George Box on the usefulness of empirical models. (Page down to the end of this e-zine to enjoy the actual quote.)

1: Stat-Ease alert: Version 10 of Design-Expert software released and updated!

We are very happy to announce the release of Design-Expert version 10 (DX10), which offers an amazing array of interface enhancements and new statistical tools. Click here and scroll down to the “Features” tab for more details on what’s new in DX10. Then, assuming you like what you see, scroll back up to the link for the free 30-day trial or buy it directly, taking advantage of upgrade pricing if eligible. You will do well by gaining the leading edge on DOE capability!

P.S. If you became an early adopter of our new Design-Expert release before it achieved its current revision of 10.0.2, then download the update from within the program. View the Read Me file for details on the changes—mainly maintenance items. If you want to receive notice when an update becomes available, go to Edit on the main menu of your program, select Preferences and, within the default General tab, turn on (if not already on by default) the “Check for updates on program start” option. 

2: Newsletter alert: New issue of the Stat-Teaser features Design-Expert version 10

Check out the latest issue of our Stat-Teaser newsletter via this link. Get the scoop on v10 of Design-Expert and how to purchase it most economically, plus other bulletins of vital interest to fans of Stat-Ease.
Thank you for reading our newsletter. We appreciate you passing along the link to the posting of the Stat-Teaser to your colleagues.

3: FAQ: Tips and tricks on using the Pareto plot to select factorial effectsOriginal question from a Biotechnologist:
“We use Pareto plots from Design-Expert to show which factors are selected in models because it's the preferred visual for most our customers. In the case of robustness studies, we can get many responses with basically no factors selected which is a good thing.

The Pareto is a good visual for showing all factor effects are well below the limit. However, customers (internal and external) almost invariably ask why the bars are not labeled. I usually answer that due to the design, the bars' size and place on the chart changes with factor selection, making labeling impossible. I sometimes add a label for the most important effect to show what it 'would have been' (provided it still is the top contributor after selection).

Answer from Stat-Ease Consultant Wayne Adams:“Our Pareto chart is a model selection tool, therefore we plot the significance of the effect rather than the size. Terms that are not selected get thrown into the pooled estimate of noise: They are not labeled. To enumerate these unselected terms sorted by t-value high-to-low, hold the Ctrl key and right-click the graph. This also copies the table so you can paste it to Excel or other applications.”

P.S. See a revealing screen shot that Wayne provided the last time he answered this FAQ by going to #1 at the Nov/Dec ’13 DOE FAQ Alert posted here.
—Mark

4: Info alert: Newly published articles explain “How to Properly Size Response Surface Method Experiment (RSM) Designs for System Optimization” and “How Design of Experiments Can Improve Formulation Development”

The March issue of ITEA Journal, published by the International Test and Evaluation Association, features a detailing on “How to Properly Size Response Surface Method Experiment (RSM) Designs for System Optimization” by Stat-Ease Consultants Wayne Adams, Pat Whitcomb and myself. Contact me if you are not a member of ITEA and thus cannot gain access to this publication.

5: Events alert: The 6th European Design of Experiments (DOE) User Meeting and Workshops

(Last notice!) Sign up now to attend the 6th European Design of Experiments (DOE) User Meeting and Workshops in lovely Leuven, Belgium on May 18 through 20. This biannual get-together is co-sponsored by Stat-Ease and the on-site host CQ Consultancy. Get all the details on this fun and informative conference by clicking this link.

Also see Stat-Ease Consultants appearing at two technical conferences coming up in the USA:

• Spring Research Conference, Chicago, May 25-27. There Stat-Ease Consultant Martin Bezener will explain how to deal with “Restricted-Randomization Optimal Design of Experiments Combining Mixture and Non-Mixture Factors”.
• Quality and Productivity Research Conference, Phoenix, June 13-17.

Click here for these and other upcoming appearances by Stat-Ease professionals.

P.S. 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.





6: Workshop alert: Classes coming to California!

You can do no better for quickly advancing your DOE skills than attending a Stat-Ease workshop. In these 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. Also, take advantage of a $400 discount when you take two complementary workshops that are offered on consecutive days.

• Experiment Design Made Easy (EDME)
• June 20-21, California*
  
• Response Surface Methods for Process Optimization (RSM)
• June 22-23, California*

* Take both EDME and RSM to earn $400 off the combined tuition!

• Mixture and Combined Designs for Optimal Formulations (MIXC)
• July 19-21, Minneapolis

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 contact the Client Specialist, Rachel, at workshops@statease.com or 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 workshops@statease.com.

I hope you learned something from this issue. Address your general questions and comments to me at: mark@statease.com.

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—Overlooked words of wisdom from George Box on the usefulness of empirical models:

"All models are wrong; some models are useful.  This aphorism is particularly true for empirical functions such as polynomials that make no claim to do more than locally graduate the true function.”

  —George E. P. Box, “Choice of Response Surface Design and Alphabetic Optimality.” Utilitas Mathematica, 21 (1982): 11–55. The quote appears on p34. A bit later he says that “one conclusion I reached from many such [chemical kinetic modeling] studies was that approximations would not need to be very good for response surface methods to work” and, no doubt referring to use of optimality criterion (the topic of this treatise), that “any theory of experimental design which depended on the exactness of such approximations should be regarded with skepticism.”

As reported in Dr. Fisher’s Casebook column in the February 2016 issue of Significance (p 44) “Play it again, Sam”, a variation on this comes from 1987 text by Box and Draper on Empirical Model-Building and Response Surfaces: “Essentially, all models are wrong, but some are useful. However, the approximate nature of the model must always be borne in mind.”

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, Shari Kraber, Wayne Adams, Brooks Henderson and Martin Bezener
—Statistical advisor to Stat-Ease: Dr. Gary Oehlert
—Stat-Ease programmers led by Neal Vaughn
—Heidi Hansel Wolfe, Stat-Ease sales and marketing director, and all the remaining staff that provide such supreme support!

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