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Vol: 14 | No: 5 | Sep/Oct '14
The DOE FAQ Alert

Stat-Ease Statistical Group

Dear Experimenter,
Here’s another set of frequently asked questions (FAQs) about doing design of experiments (DOE), plus alerts to timely information and free software updates. If you missed the previous DOE FAQ Alert click here.

To open yet another avenue of communications with fellow DOE and Stat-Ease fans, sign up for The Stat-Ease Professional Network on Linked in. A recent thread features “Favorite Stats and DOE Blogs.”

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

1:  Newsletter alert: New issue of the Stat-Teaser demonstrates Design-Expert® software, version 9 (DX9) being put to good use on a split-plot design for cupcake baking, and a DOE for fine-tuning hearing aids
2:  FAQ: How to deal with a mean model >: (
3:  Info alert: “Practical Aspects for Designing Statistically Optimal Experiments”
4:  Webinar alert: “A Flexible Strategy for More Effective Experimentation—Taking Designs Apart and Putting Them Together Again”
5:  Events alert: Informative talk on DOE in Orlando
6:  Workshop alert: Fall schedule—a great time to tool up on DOE

PS. Quote for the month: Four qualities to look for in mathematical models.

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1: Newsletter alert: New issue of the Stat-Teaser demonstrates Design-Expert® software, version 9 (DX9) being put to good use on a split-plot design for cupcake baking, and a DOE for fine-tuning hearing aids

Check out the latest issue of our Stat-Teaser newsletter via this link.  It leads with an article detailing “Split Plot Design for Cupcake Baking” by statistician Sebastian Hoffmeister of Statcon—Stat-Ease's German reseller of Stat-Ease software.  Are chocolate chips the secret to deliciousness?  Read Sebastian’s report to see how this pans out.

The newsletter also reports how Thomas Burns, PhD, an Engineering Principal with Starkey Hearing Technologies, Inc., used Design-Expert to improve an assistive listening device and make it more robust.  Hear, hear!

Thank you for reading our newsletter.  We appreciate you passing along the link to the posting of the Stat-Teaser to your colleagues.

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2: FAQ: How to deal with a mean model >: (

Original question from Validation Engineer:
“Your webinar on ‘Quality by Design (QbD) Space for Pharmaceuticals and Beyond’* shows a Design-Expert software screen with the model set to ‘mean’.  What do you mean (no pun intended) by this?”

*For slides and recordings of this and other Stat-Ease webinars go to this web page.

Answer from Stat-Ease Consultants Wayne Adams and Brooks Henderson:
“When none of the factors are selected for a model, the only estimate for the process output is the grand average of the response data.  The model is the average or mean of the data.

To change the model in Design-Expert, go to the "Model" tab. Then to add terms, double-click on them until you see a green “M”.  For example, let’s say you are considering the coded equation Y = 10 + 2A as your model.  Here, 10 is the grand mean of all the data (the intercept), since in coded terms every factor varies from a low level of -1 and a high level of +1.  If you are in the center of the design space (so A = 0), then Y = 10.  In the case of a ‘mean’ model, though, there is no 2A term.  The model is just Y = 10 (a straight and level line) and there are no factor effects.

The mean should only be used if you fail to get a good regression model for the experimental factors.  It doesn’t tell you much but it’s better than nothing (zero for the prediction).”

P.S. from Wayne: “Perhaps this answer ends up a little too meanly. ; ) Let’s just say that the mean model is used when no effects are expected (for verification) or observed to be significant.  This tells you the data gathered so far is not enough to differentiate the effects from the noise.”

P.P.S. For your information, here is an informative, illustrated detailing by Professor Robert Nau of Duke University’s Fuqua School of Business (him having told me he’s happy to share it) on “Mean (constant) model”. —Mark

(Learn more about modeling by attending the two-day computer-intensive workshop Response Surface Methods for Process Optimization.  Click on the title for a complete description.  Link from this page to the course outline and schedule.  Then, if you like, enroll online.)

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3: Info alert: “Practical Aspects for Designing Statistically Optimal Experiments”

The March issue of Journal of Statistical Science and Application (V2, N3, pp85-92) features advice on “Practical Aspects for Designing Statistically Optimal Experiments” by Stat-Ease Consultant Pat Whitcomb and I.  Look this up here at our collection of DOE case studies sorted by date.  While there you may want to filter for applications in your industry that provide helpful reinforcement for more widespread adoption of this powerful multifactor-testing tool.

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4: Webinar alert: “A Flexible Strategy for More Effective Experimentation—Taking Designs Apart and Putting Them Together Again”

In this webinar titled “A Flexible Strategy for More Effective Experimentation—Taking Designs Apart and Putting Them Together Again” (repeated three times for your scheduling convenience), Stat-Ease Consultant Shari Kraber provides methods for creating experiment designs progressively so that knowledge can be gained steadily via iterative steps.  Using Design-Expert software she will also demonstrate how to augment completed designs that fall short of adequately modeling the critical response(s).  This might salvage a great deal of experimental work that would otherwise go for naught. The webinar will illustrate all concepts with practical, real-world examples.  The information will be presented at an intermediate level.  The audience is assumed to have some working knowledge of factorial and response surface method (RSM) designs.

Reserve your Webinar seat now at by clicking one of the links below:

  1. Wednesday, October 22 at 8 pm USA-CT* for eastern Asia and Oceania (others welcome!),
  2. Thursday, October 23 at 6:30 am USA-CT* for Europe, Africa, Middle East and western Asia (others welcome!),
  3. Friday, October 24 at 11 am USA-CT* for the Americas and Caribbean (others welcome!).

If this is your first Stat-Ease webinar, please review these suggestions on how to be prepared.  If questions remain, direct them to our Communications Specialist, Karen Dulski, via

*(To determine the time in your zone of the world, try using this link.  We are based in Minneapolis, which appears on the city list that you must manipulate to calculate the time correctly.)

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5: Events alert: Informative talk on DOE in Orlando

Marketing Director Heidi Hansel Wolfe will tow along a technical colleague (maybe me) down the road from Stat-Ease headquarters to the MD&M Minneapolis (medical device and manufacturing) show at the Minneapolis Convention Center on October 29-30.  Please stop by our booth #733 if this is your industry and you happen to be in the area.

I will exhibit Stat-Ease software (booth #29) and speak on the “Factorial DOE Planning Process” (co-authored by Consultant Shari Kraber) at the inaugural American Society of Quality (ASQ) Technical Communities Conference in Orlando, FL, on October 30-31.  Come on down to see me and then knock yourself out at Disney World and the other huge attractions in the area.

Click here for a list of upcoming appearances by Stat-Ease professionals.  We hope to see you sometime in the near future!

PS.  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.

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6: Workshop alert: Fall schedule and a peek into 2015—a great time to tool up on DOE

All classes listed below will be held at the Stat-Ease training center in Minneapolis unless otherwise noted.  If possible, enroll at least 4 weeks prior to the date so your place can be assured.  Also, take advantage of a $400 discount when you take two complementary workshops that are offered on consecutive days.

*Receive a $200 discount per class when you enroll 2 or more students or enroll in consecutive 2-day workshops. Receive a $100 discount for enrolling in the FSPD workshop along with another class.

** Take both MIX and MIX2 to earn $400 off the combined tuition!

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 Rachel Pollack at 612-746-2038.  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

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

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


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

PS. Quote for the month—Four qualities to look for in mathematical models:

"Scientists look for four qualities in mathematical models.  The first is parsimony: the fewer the units and processes used to account for the phenomenon, the better.  The second quality is generality: the greater the range of phenomena covered by the model, the more likely it is to be true.  Next is consilience.  [Merriam-Webster Online defines this as “the linking together of principles from different disciplines.] Units and processes of a discipline that conform with solidly verified knowledge in other disciplines have proven consistently superior in theory and practice to units and processes that do not conform.  And finally, drawing from all of the above virtues, the definitive quality of good theory is predictiveness.  Those theories endure that are precise in the predictions they make across many phenomena and whose predictions are easiest to test by observation and experiment.”

—E. O. Wilson, excerpted from p.216 of this biologist’s 1998 book Consilience: The Unity of Knowledge.

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

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