Issue: Volume 8, Number 12
Date: December 2008
From: Mark J. Anderson, Stat-Ease, Inc., Statistics Made Easy® Blog

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, see below.

==> 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.

Feel free to forward this newsletter to your colleagues. They can subscribe by going to If this newsletter prompts you to ask your own questions about DOE, please address them via mail

For an assortment of appetizers to get this Alert off to a good start, see these new blogs at* (beginning with the most recent one):

—Roasty toasty in Puerto Rico
—Where to draw the line on old wine
—Presidential polls perplexing
* Need a feed from StatsMadeEasy to Microsoft's Outlook? See

Also, Stat-Ease offers an interactive web site — its Support Forum for Experiment Design at Whereas this monthly e-zine shares one-on-one communications with Stat-Ease StatHelp, anyone can post questions and answers to the Forum, which is open for everyone to see (with moderation). Check it out and weigh in!

Here's the most recent post (perhaps still unanswered) to be found in the DOE "Analysis" section. Feel free to contribute to this discussion thread.*

From: ajkugel (Registered Member)
"I'm having a hard time analyzing a factorial design with 4 categorical factors. The response is a biological assay quantified with an absorbance measurement...Our biologists give us the mean of three replicates compared to a control...Should I use the averaged data and keep my design orthogonal and analyze it as is without breaking out the replicate data? Or is there a way around the problem while still incorporating the replicate data?"

*(Moderator Wayne Adams says "We here at Stat-Ease would like to thank those of you who have participated on the forum, especially those who have taken the time to answer questions. Given a little
more time this forum has to the potential to grow into something beneficial not only for Stat-Ease customers, but statistics in general. Keep up the good work. Together we'll make STATistics as EASy as possible.")

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

1. FAQ: Why does my main effect plot warn "Factor involved in an interaction"t-Ease statisticians honored by Shewell Award for best presentation at Fall Technical conference
2. Expert-FAQs: Two models suggested by Design-Expert: Now what?
3. Question for a geek: Why does gak feel so cold?
4. Info Alert: Success stories applying DOE for molding rubber, stamping foil, and manufacturing semiconductors
5. Webinar alert (2nd): An Introduction to Mixture Design for Optimal Formulations
6. Events Alert: Do you need a speaker?
7. Workshop Alert: Most popular Stat-Ease class — Experiment Design Made Easy — coming to San Diego!

P.S. Quote for the month: A logical response by a statistician to a beggar experiencing economically hard times.


1. FAQ: Why does my main effect plot warn "Factor involved in an interaction"

-----Original Question-----

From: Process engineer at German research organization "Hello guys, I have a question concerning my analysis with Design-Expert® software. When I watch the one factor modelgraph of my test results, it always says "Warning! Factor involved in an interaction." What does this mean?"

Answer (from Stat-Ease Consultant Wayne Adams):
"By definition, an interaction occurs when the effect of one factor depends on the setting of another. For the warning to be activated, the factor you are investigating (via its main effect plot) must be involved in one or more interactions. Therefore, the setting of the other factor(s) must be taken into account. When looking at the one-factor plot, the effect being shown is only valid for the current settings of the factors it interacts with. See what happens when you move the slide-bar indicators on the floating factors tool. You will see some very interesting changes to your main effects plot when interactive factors are varied."

(Learn more about interactions by attending the three-day computer-intensive workshop "Experiment Design Made Easy." See for a description of this class and link from this page to the course outline and schedule. Then, if you like, enroll online.)


2. Expert-FAQ: Two models suggested by Design-Expert: Now what?

-----Original Question-----
Industrial statistician at manufacturer of cleaning products
"As I've been analyzing mixture data here, I've found that sometimes Design-Expert suggests a linear model, but the quadratic vs linear line in the model fit summary produces a p-value of 0.1 or 0.15. The software then reverts back to the linear model due to the arbitrary cutoff for significance. However, sometimes I find that, if I override this selection, bump up the model to quadratic and then apply the backward selection, I get a better fit. Is this data snooping or is that a legitimate approach?"

Debatable, I think. However, I confess to having tried this approach myself: If higher-order terms can be estimated without any aliasing and they are close to the threshold p value, try the backward regression, which then will likely produce at least one extra term above-and-beyond the suggested model. This may or may not prove useful, that is, very possibly it will be over-fitting the data. Only time can tell via follow-up confirmatory runs.

Wayne weighs in with this comment:
"I don't want to step on toes, but as a corollary to your recommendation take a look at the adjusted R-squares at the bottom of the fit-summary for the various orders. I often use backward reduction from the order with the highest adjusted R-squared. Again this is a rule of thumb rather than a proven statistical technique, but often it gives a better fitting model. Assume the model is wrong — confirm that it’s useful. Can I share this quote with George Box?"

I replied:
It seems to me that in this case, Design-Expert suggests two models and then one ought to go backward from the higher-order one.

Consultant Shari Kraber says:
"I use this approach any time the p-value of the next higher order is less than 0.2 (assuming no aliasing) and often find another significant term. I didn't think this approach was questionable at all, as long as the p-value of the additional term is strong. I've never thought of it as over-fitting, but instead I think that the group p-value used in the Fit Summary waters down the individual p-values and can easily mask a significant term.

I asked for Consultant Pat Whitcomb's take on this. He replied: "As the models get larger there is more of a chance that there are significant terms hidden in the next higher order model. Remember Pareto; it may be that there are only few vital and many trivial terms. Looking at the whole, the trivial many (insignificant terms) may dilute the vital few (significant terms) and make the group look insignificant. I use the adjusted R-squared as a guide. If the adjusted R-squared increases by a practical amount with the addition of the next group of terms, I add the higher order terms and look at the reduced model, even if additional terms are not significant as a whole. Be sure to check the agreement between the adjusted and predicted R-squares; a large difference (difference > 0.2) may indicate over- parameterization."

(Learn more about modeling mixture performance by attending the three-day computer-intensive workshop "Mixture Design for Optimal Formulations." For a complete description of this class, see Link from this page to the course outline and schedule. Then, if you like, enroll online.)


3. Question for a geek: Why does gak feel so cold?

-----Original Question-----
Wisconsin mom
"I wonder if you can answer a question for me... My son's preschool makes what they call gak* from glue, water and borax. It's very cold to the touch and it's often debated why this is. Do you know?"

The reaction between the glue and borax is described at and The reaction evidently is exothermic, that is, heat generating. However, it may be so little that it gets rapidly absorbed by the cold mass of water. My guess is that the handling of gak leaves a film of moisture that evaporates and creates the cooling effect. Evaporative cooling can create a surprising chill, especially if the humidity is low.

A fun experiment for preschoolers to demonstrate this effect would be to simply have them dampen the tip of their index finger and then blow air over that hand. Ask them if one finger feels colder. Which one?

PS. My wife teaches preschool and her kids experiment on Gak also!

* See this recipe for gak from the California Science Center: I experimented on a similar recipe and reported the results in "Mark's Play Putty Experiment" posted at


4. Info Alert: Success stories applying DOE for molding rubber, stamping foil, and manufacturing semiconductors

— "Design of Experiments Helps Reduce Injection Molding Scrap Rate by 90%" was published in the September issue of Rubber & Plastic News. It details how engineers at Robinson Rubber uncovered a combination of material selection and manufacturing protocol that created unacceptable results. Armed with this process knowledge, they achieved breakthrough quality improvements. See the original manuscript posted at

— Stat-Ease contract trainer Reed Wahlberg explains how a Six Sigma team made use of DOE to cut printer setup time 40% in this article posted by Graphic Arts Monthly:

— The most advanced article of this trio offers a case study on "Response surface methods for peak process performance" for semiconductor manufacturing. Solid State Technology published it in November. See the content at Learn how variation transmitted to responses from poorly-controlled process factors can be accounted for by the mathematical technique of propagation of error (POE), which facilitates 'finding the flats' on the surfaces generated by RSM. Another refinement is the application of the dual response approach to RSM to capture the standard deviation of the output(s) as well as the average, thus accounting for unknown sources of variation. Dual response plus POE provides a more useful model of overall response variation. The end-result of applying these statistical tools for design and analysis of experiments will be in-specification products that exhibit minimal variability — the ultimate objective of robust design.


5. Webinar alert (2nd): An Introduction to Mixture Design for Optimal Formulations

You are invited to attend a free web conference by me on "An Introduction to Mixture Design for Optimal Formulations." This free conference, which I will keep at a beginner level statistically, will be broadcast on Wednesday, January 21 at 2 PM USA Central Time* (CT). I will repeat my webinar on Thursday, January 22 at 8 AM. It is aimed at product formulators who at best may be using standard factorial designs, or worse yet, the one-variable-at-a-time method. Keeping it simple and making it fun, I will introduce tools of multicomponent mixture design, modeling and statistic analysis. My hope is to generate interest in these powerful DOE methods for quickly converging on the sweet spot — where all desired product attributes are achieved.

Stat-Ease webinars vary somewhat in length depending on the presenter and the particular session — mainly due to breaks for questions: Plan for 45 minutes to 1.5 hours, with 1 hour being the target median.

When developing these one-hour educational sessions, our presenters often draw valuable material from Stat-Ease DOE workshops. Attendance may be limited, so sign up as soon as you see your way clear by contacting our Communications Specialist, Karen, via If you can be accommodated, she will send you the link for the WebConnect and dial-in for ConferenceNow voice via telephone (toll-free access extends worldwide, but not to all countries).

*(To determine the time in your zone of the world, try using this link: Note that we are based in Minneapolis, which appears on the city list that you must manipulate to calculate the time correctly. It seems that figuring out the clock on international communications is even more complicated than statistics! Good luck!)


6. Events Alert: Do you need a speaker?

Click 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 reimbursements for airfare, hotel and meals — expenses only. In any case, it never hurts to ask Stat-Ease for a speaker on this topic. Contact if you have an event coming up with an open slot for a presentation.


7. Workshop alert: Most popular Stat-Ease class — Experiment Design Made Easy — coming to San Diego!

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!

—> Experiment Design Made Easy (EDME)
(Detailed at
> January 27-29, 2009 (San Diego, CA)
> February 24-26 (Minneapolis, MN)

—> Mixture Design for Optimal Formulations (MIX)
> February 3-5 (Minneapolis)

—> Response Surface Methods for Process Optimization (RSM)
> March 10-12 (Minneapolis)

—> Designed Experiments for Life Sciences (DELS)
> March 3-4 (Dulles, Washington D.C.)

—> DOE for DFSS: Variation by Design (DDFSS)
> May 5-6 (Minneapolis)

See 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 .

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




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

PS. Quote for the month —A logical response by a statistician to a beggar experiencing economically hard times:

"Give us a copper Guv" said the beggar to the Treasury statistician, when he waylaid him in Parliament Square. "I haven't eaten for three days." "Ah," said the statistician, "and how does that compare with the same period last year?"
—Russell Lewis

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 and Wayne Adams (see for resumes)
—Statistical advisor to Stat-Ease: Dr. Gary Oehlert (
—Stat-Ease programmers, especially Tryg Helseth and Neal Vaughn (
—Heidi Hansel Wolfe, Stat-Ease sales and marketing director, and all the remaining staff


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#1 Mar 01, #2 Apr 01, #3 May 01, #4 Jun 01, #5 Jul 01 , #6 Aug 01, #7 Sep 01, #8 Oct 01, #9 Nov 01, #10 Dec 01, #2-1 Jan 02, #2-2 Feb 02, #2-3 Mar 02, #2-4 Apr 02, #2-5 May 02, #2-6 Jun 02, #2-7 Jul 02, #2-8 Aug 02, #2-9 Sep 02, #2-10 Oct 02, #2-11 Nov 02, #2-12 Dec 02, #3-1 Jan 03, #3-2 Feb 03, #3-3 Mar 03, #3-4 Apr 03, #3-5 May 03, #3-6 Jun 03, #3-7 Jul 03, #3-8 Aug 03, #3-9 Sep 03 #3-10 Oct 03, #3-11 Nov 03, #3-12 Dec 03, #4-1 Jan 04, #4-2 Feb 04, #4-3 Mar 04, #4-4 Apr 04, #4-5 May 04, #4-6 Jun 04, #4-7 Jul 04, #4-8 Aug 04, #4-9 Sep 04, #4-10 Oct 04, #4-11 Nov 04, #4-12 Dec 04, #5-1 Jan 05, #5-2 Feb 05, #5-3 Mar 05, #5-4 Apr 05, #5-5 May 05, #5-6 Jun 05, #5-7 Jul 05, #5-8 Aug 05, #5-9 Sep 05, #5-10 Oct 05, #5-11 Nov 05, #5-12 Dec 05, #6-01 Jan 06, #6-02 Feb 06, #6-03 Mar 06, #6-4 Apr 06, #6-5 May 06, #6-6 Jun 06, #6-7 Jul 06, #6-8 Aug 06, #6-9 Sep 06, #6-10 Oct 06, #6-11 Nov 06, #6-12 Dec 06, #7-1 Jan 07, #7-2 Feb 07, #7-3 Mar 07, #7-4 Apr 07, #7-5 May 07, #7-6 Jun 07, #7-7 Jul 07, #7-8 Aug 07, #7-9 Sep 07, #7-10 Oct 07, #7-11 Nov 07, #7-12 Dec 07, #8-1 Jan 08, #8-2 Feb 08, #8-3 Mar 08, #8-4 Apr 08, #8-5 May 08, #8-6 June 08, #8-7 July 08, #8-8 Aug 08, #8-9 Sep 08, #8-10 Oct 08, #8-11 Nov 08, #8-12 Dec 08 (see above)

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