Issue: Volume 9, Number 3
Date: March 2009
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):

— Statistics on education and education on statistics
— How to arrest what’s-his-name’s forgetting curve
— Feeling belittled? Cut off relations with short people!

* Need a feed from StatsMadeEasy to Microsoft's Outlook? See
** (See the comment to this blog and follow its link to an amazing collection of stats and graphs on record temps across the USA)

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!

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

1. FAQ: Selecting effects graphically via the half-normal plot
2. Expert-FAQ: How can I improve the overall desirability from my multiple response numerical optimization?
3. Book Giveaway: Winners announced
4. Info Alert: No more "spray & pray" by cosmetic formulators — Alberto Culver leads the way with mixture design
5. Webinar Alert (1st): "DOE — What's In It for Me" — an executive summary on the power of matrix-based multifactor testing
6. Events Alert: Learn "Best Practices to Plan/Analyze a Verification DOE"
7. Workshop Alert: See when and where to learn about DOE
8. A heads-up on web-based statistics training:

P.S. Quote for the month: A different twist on why the world needs engineers — not just scientists.


1. FAQ: Selecting effects graphically via the half-normal plot

-----Original Question-----
From: Graduate researcher at a University in Scotland

"After taking your free statistics self-assessment at I now realize that I've got a lot to learn on the underlying fundamentals for design of experiments!*

Nevertheless, with the aid of Design-Expert® ("DX") software, I pressed ahead with the design and analysis of an experiment done for my PhD. I am trying to write up this part of my thesis, and I realize there are holes in my knowledge (lots of them!), especially in terms of what DX is doing to the data to get the results. In my two-level five-factor experiment, I managed to do a number of center points. I understand how the estimate of pure error are calculated based on these replicated runs. However, when I select the significant effects from the half-normal plot and then perform the analysis of variance (ANOVA), does DX automatically 'discard' the factors which are not involved in the significant effects and then use the data as a way of gaining 'hidden replication' (as Douglas Montgomery describes in his textbook "Design and Analysis of Experiments")?

I am not sure how the data set changes after I decide which effects are significant; what is the difference between testing every effect/interaction for significance and only testing those that appear to be significant from the half normal plot?"

*(The assessment steers those who need it to the web-based PreDOE course, which teaches the fundamental statistics used in design of experiments. For more details on this interactive resource, see

Answer (from Consultant Shari Kraber):
"When you choose effects on the half-normal, you are simply separating the effects into the "model" pool and the "error" pool. The error pool becomes the denominator for all the F tests done on the individual model terms. If you selected everything for the model, then nothing would be left in error and the F test would not be very valid. Using the half-normal plot for your initial selection really gives a quick way of getting your best estimate of the total error, and then testing the "most likely" terms against a good base.

The hidden replication in a two-level factorial design comes from the fact that every low and high level for a factor is done multiple times, even though there were no actual design point replicates. If you did 32 runs, then 16 of those had A set low and 16 runs had A set high. The calculation of the effect estimate for
A is then the difference between the average of the "high" A's and the average of the "low" A's. So there is a lot of inherent power in averages of 16, thus this idea of hidden replication. This is the magic of DOE! It provides more information with more accuracy than one factor at a time experimentation."

(Learn more about selecting effects 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: How can I improve the overall desirability from my multiple response numerical optimization?

-----Original Question-----
From: Graduate researcher at a university in Kansas

"How can I improve my overall desirability from a multiple response optimization? The best I have is just 0.423 — far short of the ideal of 1."

That’s actually not bad: It’s all relative. Usually just going with the most desirable setup of factors suffices to hit the sweet spot where the process meets all specifications. For example, see the case study that details desirability in the Engineering Statistics Handbook, which produces an overall result of 0.596: However, if the threshold limits come from competitive benchmarks, then an overall desirability much below 1 might be unacceptable. For example, let's say that you come up with a new process to manufacture a product at a far cheaper price. However, no one will buy it if it falls short on performance in comparison to the market leader. A low overall desirability in this case would likely be the project's death knell.

(Learn more about multiple response optimization by attending the three-day computer-intensive workshop "Response Surface Methods for Process Optimization." For a description of this class, see Link from this page to the course outline and schedule. Then, if you like, enroll online.)


3. Book Giveaway: Winners announced

These lucky readers were drawn at random from over 70 entrants to a drawing for several books on design of experiments (DOE) and response surface methods (RSM):

— > "DOE Simplified, 2nd edition" by Anderson & Whitcomb* to Dan Courtney, Lean Six Sigma Expert, JDSU, Connecticut
— > "Statistics for Experimenters, 2nd edition" by Box, Hunter & Hunter to Artie McKim, Technical Director, Gaylord Chemical, Louisiana
— > "RSM Simplified" by Anderson & Whitcomb* to Nathan Strong, Specialist, Watson Pharmaceuticals, Utah
— > "Response Surface Methodology, 2nd edition" by Myers & Montgomery to Dusty Vaughn, Project Engineer & Continuous Improvement Coordinator, Aerospace Testing Alliance, Tennessee

Congratulations to these four winners and condolences to the others who entered into this drawing. Keep watching for more great books to be given away in the future.

*(These very economical and informative how-to softcover primers on DOE and RSM are available via the Stat-Ease ecommerce web site at


4. Info Alert: No more "spray & pray" by cosmetic formulators — Alberto Culver leads the way with mixture design

Mixture DOE* helped cosmetic formulators at Alberto Culver push product performance of a cleansing scrub to a level that in the past was thought impossible. Global Cosmetic Industry Magazine published the highlights of this success story — follow this link to see it: For more details see

*(Learn more about searching out most desirable product recipes 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.)


5. Webinar alert (1st): "DOE — What's In It for Me" — an executive summary on the power of matrix-based multifactor testing

You are invited to attend a free web conference by Stat-Ease Consultant Wayne Adams on "DOE — What's In It for Me." This free conference, which Wayne will keep at a managerial level statistically, will be broadcast on Wednesday, May 27 at 2 PM USA Central Time* (CT). He will repeat his webinar on Thursday, May 28 at 8 AM. It is aimed at those who need convincing on how design of experiments harnesses the power of matrix-based multifactor testing. Ideally, after seeing what DOE can do, it will be favored over the old-fashioned one-factor-at-a-time (OFAT) method.

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 soon 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: Learn "Best Practices to Plan/Analyze a Verification DOE"

Stat-Ease Consultant Shari Kraber will present "Best Practices to Plan/Analyze a Verification DOE" on Tuesday, May 19 in a session sponsored by the Statistics Division of the American Society of Quality (ASQ) for the World Conference on Quality Improvement (WCQI) in Minneapolis. For details on WCQI, see

As an added bonus for those of you who attend WCQI, Stat-Ease Consultant Pat Whitcomb will speak at an Open Council Meeting of ASQ's Chemical & Process Industries Division (CPID) on "Sizing Mixture Designs" at 5 PM on Monday, May 18.

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.


7. Workshop alert: See when and where to learn about DOE

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
> April 28-30 (Minneapolis, MN)

—> Mixture Design for Optimal Formulations (MIX)
> April April 21-23 (Minneapolis)

—> Response Surface Methods for Process Optimization (RSM)
> July 7-9 (Minneapolis)

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

—> Designed Experiments for Life Sciences (DELS)
> July 28-29 (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


8. A heads-up on web-based statistics training: offers 80+ online courses in statistics, with courses in engineering statistics, SPC, regression, forecasting, and many other topics. Classes include hands-on assignments with feedback, as well as online discussions with distinguished instructors. There is no need to be online at any particular time. See


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 different twist on why the world needs engineers — not just scientists:

"Science seeks to understand the world as it is; only engineering can change it."

—Henry Petroski, a professor of civil engineering at Duke University, paraphrasing a quote by Von Karman presented in a prior DOE FAQ Alert: "The scientist seeks to understand what is; the engineer seeks to create what never was." This reinforcement by Petroski of the engineering role was written in response to President Obama's inaugural address call for restoring "science to its rightful place." Let's not forget engineers! See the editorial by Petroski at

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, led by Neal Vaughn and Tryg Helseth (
—Heidi Hansel Wolfe, Stat-Ease sales and marketing director, and all the remaining staff that provide such supreme support!


Interested in previous FAQ DOE Alert e-mail newsletters?
To view a past issue, choose it below.

#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, #9-01 Jan 09, #9-02 Feb 09, #9-03 Mar 09 (see above)

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