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Vol: 11 | No: 1 | Jan/Feb'11
Stat-Ease
The DOE FAQ Alert
     
 

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.

Feel free to forward this newsletter to your colleagues. They can subscribe by going to this registration page.

TIP: Get immediate answers to questions about DOE via the search feature on the main menu of the Stat-Ease® web site. This not only pores over previous alerts, but also the wealth of technical publications posted throughout the site.

Also, Stat-Ease offers an interactive website—The Support Forum for Experiment Design. Anyone (after gaining approval for registration) can post questions and answers to the forum, which is open for all to see (with moderation). Furthermore the forum provides program help to Design-Ease® and Design-Expert® software. Check it out and search for answers. Also, this being a forum, we encourage you to weigh in!

Sign up for The Stat-Ease Professional Network on Linked In and start or participate in discussions with other software users.

 
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Topics discussed since the last issue of the DOE FAQ Alert (latest one first):

Also, check out this intriguing blog http://www.moresteam.com/blog/index.cfm by Bill Hathaway of MoreSteam.com on why “OFAT isn’t Lean.”  Find out what he learned by watching an ant doing trial-and-error experimentation.

 
     
 

If this newsletter prompts you to ask your own questions about DOE, please address them via e-mail to: [email protected].


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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:  Alert about the DOE FAQ Alert: Now going to bimonthly issues
2:  FAQ: Model selection and reduction from response surface method (RSM) experiments
3:  FAQ: How to tell if mixture components are synergistic
4: Expert-FAQ: Cannot run RSM within entire cubical region—how to set a multifactor constraint that cuts out infeasible region
5: Book giveaway: Response Surface Methodology: Process and Product Optimization Using Designed Experiments, 3rd Ed. by Myers, Montgomery and Anderson-Cook
6: Info Alert: DOE for life sciences
7: Events alert: Upcoming talks on DOE by Stat-Ease consultants
8: Workshop alert: See when and where to learn about DOE, including a rare appearance in Southern California
 
 
PS. Quote for the month:
Why do so many results that use statistical models as primary evidence later turn out to be wrong?

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


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1: Alert about DOE FAQ Alert: Now going to bimonthly issues

Beginning this year (2011) the DOE FAQ Alert will be published bimonthly, so the next issue will be March/April.  This change was necessitated by the change in format to HTML, which requires much more production than the plain-text layout used since this ‘e-zine’ began in 2001.

Furthermore, given the great amount of information flowing in from all directions, it seems prudent to lessen the pace of issues to maximize absorption. ;)

Note that this year the DOE FAQ Alert achieves a big milestone—it’s10th anniversary of publication!  Thank you all for subscribing.  I hope you like the new format.

—Mark


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2: FAQ: Model selection and reduction from response surface method (RSM) experiments

Original Question:

From a Mechanical Engineer studying for a Master’s Degree:
“How do you know which terms you have to select?  I notice in the predictive model you revised for me, that only some terms are selected by Design-Expert.  Is there any method you follow for model reduction?”

Answer:

From Stat-Ease Consultant Brooks Henderson:
“I usually start with the model suggested (underlined) by Design-Expert (“DX”) on the Fit Summary screen.  Then, I remove any terms that are not-significant.  You can let DX remove non-significant terms by turning on “Backward” selection with the alpha out value that you choose.  For example, below I started with the two-factor (“2FI”) model and it will remove any terms with p-values higher than 0.1000 (Alpha out value).  Make sure to click “yes” if asked about hierarchy and any terms needed for hierarchy will be put back in the model (even if they are insignificant.)

Backward Selection for Model Reduction
Backward selection for model reduction

As I am selecting a model, I’m looking to only keep significant terms or terms needed for hierarchy and looking for good agreement between adjusted and predicted R-squared values (less than 0.2 difference).  I would also like the raw R-squared values to be as high as possible and the Adequate Precision to be greater than 4.  After that, I check the diagnostics and that’s really all there is to it.”

(Learn more about model selection 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: FAQ: How to tell if mixture components are synergistic

Original Question:

From a Pesticide Formulator:
“When using the simplex lattice design, combining two or more components, is there a way to determine if results are synergistic or additive?”

Answer:

From Stat-Ease Consultant Shari Kraber:
  “Look at the coefficients of two-component terms (like AB) in the ANOVA. If the pseudo coefficient is positive, the results are synergistic.  If the coefficient is negative, the results are antagonistic.  Alternatively, you can look at the 3D model graph and see if the curves are going up or down.”

PS from Mark: This formulator hopes to kill bugs—the more the better, so Shari is dead on (pun intended) with her answer.  However, for responses that must be minimized, synergism comes from negative coefficients for non-linear blending terms.  For example, see the case study on blending gold with copper at the outset of our Primer on Mixture Design.  The goal is to reduce the melting point of a metal mixture used by goldsmiths to solder jewelry-parts.

(Learn more about mixture design and model interpretation by attending the computer-intensive two-day workshop "Mixture Design 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.)

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4: Expert-FAQ: Cannot run RSM within entire cubical region—how to set a multifactor constraint that cuts out infeasible region

Original Question:

From a Compounding Engineer:
“I would like to run a three-factor response surface method (RSM) design in the ranges of A: 100-200, B: 100-300 and C: 400-500.  However, because of machine limitations, we cannot process in the entire cubical region.  I would still like to run this design since we generally get the best properties along the hypotenuse of the triangle.  Aliasing, as far as I am concerned, looks ok for a quadratic model.  But the design will not be orthogonal.  What are the risks of running DOE's like this?”

Answer:

From Stat-Ease Consultant Wayne Adams:
 “Rather than setting up a cubical experiment and deleting 'bad' runs with combinations that cannot work, build the design with constraints by entering an inequality under the Edit Constraints tool.  The biggest problem with simply deleting runs is that the numeric optimization may allow solutions to occur in the 'impossible' region.

Here’s the process for dealing with your particular constraints: Identify the 'bad' vertex—in this case A cannot be low while B is high.  Thus you must exclude the 100, 300 vertex on the A x B face of the cube.  The C factor is not involved in this constraint.

After drawing a constraint line, figure out for each factor whether the 'good' area is less than or greater than the constraint line.

For greater-than constraints (the constraint for A) the formula is (A – LL) / (CP – LL), for less-than constraints (the constraint for B) the formula is (UL-B) / (UL – CP), where  UL is upper limit, LL is lower limit, and CP is Constraint Point.

For A the numbers are A - 100 / (200 – 100) = A – 100 / 100.  The formula for factor B is (300 - B) / (300 – 100) = (300 - B) / 200.

Placing these formula into the inequality and simplifying yields this inequality:
  1 <= (A – 100) / 100 + (300 – B ) / 200

Multiplying through by 200 produces this result:
  200 <= 2A – 200 + 300 – B

Finally, subtracting 100 from both sides of the equation reduces it to:
  100 <= 2A - B

This is what you should enter for your particular constraints.”

Entering a Multifactor Constraint
Entering a multifactor constraint

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Constrained region with design points shown for factors A & B—C fixed at its high (+1) level

PS from Mark: In Version 8 of Design-Expert software, we now provide a very handy tool designed by Wayne that does all this constraint-calculating for you.  To see a demonstration, follow this link to the “Multifactor RSM (Optimal design)” tutorial.

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5: Book giveaway: Response Surface Methodology: Process and Product Optimization Using Designed Experiments, 3rd Ed. by Myers, Montgomery and Anderson-Cook

(Sorry, due to the high cost of shipping, this offer applies only to residents of the United States and Canada.)

Simply reply to this e-mail by March 1st if you'd like one of three copies of Response Surface Methodology: Process and Product Optimization Using Designed Experiments, 3rd Ed. by Myers, Montgomery and Anderson-Cook.  We no longer re-sell this text due to its ready availability via the publisher, Wiley, and online booksellers such as Amazon.  See Wiley’s details on the textbook here.

(Reminder: If you reside outside the US or Canada, you are NOT eligible for the drawing because it costs too much to ship the books.)

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6: Info Alert: DOE for life sciences

See how Sarah Betterman, Scientist for Upsher-Smith, used design of experiments (DOE) to determine how key fluidized-bed coating parameters affected dissolution of their pharmaceutical product by reading this publication by BioScienceWorld (“Insights for Life Sciences Industry”*).  As detailed in this article showing how “Design of experiments helps optimize pharmaceutical coating process,” Sarah made use of a Box-Behnken design, a response surface method (RSM) well suited to the goal of process optimization.

*(Learn more about RSM for life science applications by attending the two-day computer-intensive workshop "Designed Experiments for Life Sciences." 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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7: Events Alert: Upcoming talks on DOE by Stat-Ease consultants

Stat-Ease Consultant Shari Kraber will show how to achieve “Continuous Improvement Using your Modern DOE Toolbox” on March 14 at the Minnesota Quality Conference in Minneapolis.  She will also make herself available at an exhibit by Stat-Ease.  The details on this conference can be found here.

On March 15 at the Annual National Test & Evaluation Conference, sponsored by the National Defense Industrial Association (NDIA), I will give a talk on “How to Frame a Robust Sweet Spot Via Response Surface Methods (RSM).”  I will also exhibit Stat-Ease software during the three-day conference, which be held in Tampa.  For details, see this web page.

On May 17 I will show “How to Frame Quality by Design (QbD) Space via Response Surface Methods (RSM) and Mixture Experiments” to professionals attending the annual conference of the Institute for Continual Quality Improvement.  This is held concurrently with the American Society for Quality (ASQ) World Conference on Quality & Improvement (WCQI) in Pittsburgh, May 16-18.  Stat-Ease will exhibit there.  See details on both conferences here.

Go to http://www.statease.com/events.html 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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8: Workshop Alert: See when and where to learn about DOE, including a rare appearance in Southern California

Seats are filling fast for the following DOE classes.  If possible, enroll at least 4 weeks prior to the date so your place can be assured.  However, do not hesitate to ask whether seats remain on classes that are fast approaching!  Also, take advantage of a $395 discount when you take two complementary workshops that are offered on consecutive days.

All classes listed below will be held at the Stat-Ease training center in Minneapolis unless otherwise noted.

* Take both EDME and RSM in June to earn $395 off the combined tuition!

** Attend both SDOE and DELS to save $295 in overall cost.

*** Take both MIX and MIX2 to earn $395 off the combined tuition!

See http://www.statease.com/clas_pub.html for complete schedule and site information on all Stat-Ease workshops open to the public.  To enroll, click the "register online" link on our web site or call Elicia 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 mailto:[email protected] .


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  Please do not send me requests to subscribe or unsubscribe—follow the instructions at the very end of this message. I hope you learned something from this issue. Address your general questions and comments to me at: [email protected].

Sincerely,

Mark

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


PS. Quote for the month—Why do so many results that use statistical models as primary evidence later turn out to be wrong?

"

The researchers were looking in the wrong direction: to the past, when they should have been looking to the future.”


—William Briggs.  See his facetious detailing of the 5-step approach to how statistical studies actually work for fitting models at this blog. It's wicked!

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 and Tryg Helseth
—Heidi Hansel Wolfe, Stat-Ease marketing director, Karen Dulski, and all the remaining staff that provide such supreme support!

DOE FAQ Alert ©2011 Stat-Ease, Inc.
All rights reserved.

 
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