Issue: Volume 6, Number 12
Date: December 2006
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, please click on the links at the bottom of this page. If you have a question that needs answering, click the Search tab and enter the key words. This finds not only answers from previous Alerts, but also other documents posted to the Stat-Ease web 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
— "Holiday fun — tossing leftover fruit cakes with trebuchet"
— "In search of the non-obvious"
— "Exercise reduces fatigue — a counter-intuitive effect"
— "Mercury — a transitory mote in the eye of the sun."

Yesterday the citizens of our United States exhibited their mercurial mood by voting for a transit in their political landscape from the right to the left. Today they can see the planet Mercury transit the sun. This happens only about every decade. For an astronomer's eye view (Kitt Peak, Arizona), see the webcast from 11 am to 4 pm PST by San Francisco's Exploratorium at I expect that they will save the record of this astronomical event if you miss Mercury actually in transit this time around.

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: The December issue of the Stat-Teaser features "The 10 Most Common Designed Experiment Mistakes"
2. Expert-FAQ: Why mixture experiments exhibit low power (link to published article)
3. Info alert: Link to newly-published article on DOE and response surface methods (RSM) in Chemical Processing
4. Information sought: Application of DOE to drug discovery
5. Reader request: Getting a list of DOE FAQ's
6. Event alert: Do you seek a speaker on DOE?
7. Workshop alert: "Experiment Design Made Easy" in California

PS. Quote for the month: Why it's good to visualize data. (Page through to the end of this e-mail to enjoy the actual quote.)


1. Newsletter alert: December issue of the Stat-Teaser features "The 10 Most Common Designed Experiment Mistakes"

Many of you will soon receive a printed copy of the latest Stat-Teaser, but others, by choice or because you reside outside of North America, will get your only view of the December issue at

This issue of the Stat-Teaser features an article contributed by independent consultant Jeff Hybarger with his 10 tips for avoiding the most common designed experiment mistakes. Jeff believes that as many as 90% of DOE failures can be avoided by watching out for these pitfalls.

*See for details and link from there to free 45-day fully-functional trials of version 7 of Design-Expert software.


2. Expert-FAQ: Why mixture experiments exhibit low power (link to published article)

-----Original Question-----
From: Mark Bailey, Statistical Consultant, SAS
"I congratulate you and Pat on the wonderful paper 'Interpreting Power in Mixture DOE — Simplified' that appeared the latest issue (Vol. 25, No. 1) of the ASQ Statistics Division newsletter. (See Many members are involved in experiments with mixture components. This issue of power is often not apparent to the practitioner who faces constraints for the first time. Pat does a real service to these experimenters by raising their awareness and arming them with an understanding and a solution. Also, the explanation is so clear and straightforward. I especially like his choice of the pedagogical example and his use of graphics, so that it is immediately clear what the fundamental issue is about.

You demonstrate that even with low power for estimation and hypothesis tests, prediction variance is still acceptable. I wonder what you think of another direction. What about removing the 'quadratic' mixture effect from the model, since the contribution, as shown by the graphic, is so small in the first place. Would this change affect the power for the better?"

The term-by-term power in this case on single-order Scheffe polynomial terms for the linear model only is shown below for a two standard deviation effect (the signal that the experimenter wishes to detect at a minimum).

A 7.1 %
B 7.1 %
C 5.5 %

These power results are only marginally better than those for the same terms when evaluated as part of the quadratic mixture model. The problems stem from this formulation region being restricted to such a small sliver of the triangular mixture space (unconstrained). Ironically, although component C is the one that's severely constrained, A and B apparently pay the price by being 0.995 correlated according to Pearson's r for factors.

This mini-paper explains the problem, but Pat has only recently come up with a possible solution which he plans on unveiling at the 2007 Quality and Productivity Research Conference in a talk titled "A Mixture Design Planning Process" that will be co-authored by our advisor, Professor Gary W. Oehlert (School of
Statistics, University of Minnesota). Stay tuned!

PS. I thank Mark Bailey for the heads-up on this month's quote, which supports Pat's graphical approach to explaining this power puzzler.

(Learn more about mixture design 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. Info alert: Link to newly-published article on DOE and response surface methods (RSM) in Chemical Processing

The November issue of Chemical Processing magazine features an article by me and Patrick Whitcomb titled "Rethink experiment design." It makes a case for letting go of OFAT in favor of multivariable testing via DOE/RSM and provides a heads-up to minimum-run designs that make it easier than ever before to optimize many process factors. See this publication at

-----Unsolicited Compliment-----
From: Senior mechanical engineer in Texas
"I just finished reading through your interesting article 'Rethink DOE' (Chem. Processing, Nov. 2006). Computational advantages and ROI of computing are presented briefly yet clearly. I enjoyed reading it."


4. Information sought: Application of DOE to drug discovery

-----Original Question-----
From: Contact who prefers to remain anonymous

"We are engineers on a Product Development Team that is developing a new non-systemic/plasma drug delivery system. The pharmacokinetic scientists on the Team have no understanding of Design of Experiments (statistical designs). To determine the dose response and toxicological impact of a drug, they have been trained to evaluate only drug concentration and volume, one variable at a time.

However, drug infusion rate and length of infusion time are two independent variables (we can vary them significantly) that we know will produce different dose response/toxicology results, even if the resulting volumes are the same. So we'd like to run a three-factor design (concentration, flow rate and time).

Can anyone share their experiences in doing a DOE like this in the drug discovery process specifically for dose response or toxicology studies? We need "expert opinions" or case studies to help demonstrate that DOE is a viable alternative."


5. Reader response: Getting a list of DOE FAQ's

-----Original Question-----
From: Professor Douglas Montgomery, Arizona State University
"Hi Mark; Is it possible to get a comprehensive list of these FAQs? They are pretty interesting and I would like to make some of them available for my class."

I am glad you asked! See for links to all the FAQ's since I first started editing the Stat-Ease monthly DOE FAQ Alert over five years ago. For educational purposes like yours, feel free to copy any of these FAQ's that you think helpful and reference them to the specific
issue like you would any other source. As I note in the outset of every issue of this e-zine, the best way to get details on a DOE topic is via the "Search" featured in the main menu of every Stat-Ease web page. This not only looks through our FAQ Alerts, but also the great library of posted how-to articles and even the
software tutorials. Over the years we've amassed what may be an unparalleled collection of information about design of experiments for process and product improvement — all at your fingertips.

PS. Purchase Dr. Montgomery's "Design and Analysis of Experiments," 6th Edition via the Stat-Ease e-commerce site at


6. Events alert: Do you seek a speaker on DOE?

Click for a list of 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. 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 — we are at the foremost ranks of practical expertise on design of experiments for process and product improvement. Contact me at if you have an event coming up with an
open slot for a presentation.


7. Workshop alert: "Experiment Design Made Easy" in California

If you work near the West Coast (or want to visit there this winter) and you want to get going on DOE, attend our three-day computer-intensive "Experiment Design Made Easy" workshop
( this January 16-18 in San Jose, California.

See for 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 Stat-Ease at 1.612.378.9449. 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.* Call us to get a quote.

*Believe it or not, it only takes a class of 4 students to make it economical for Stat-Ease to come and teach at your site versus sending them out to one of our public presentations. The economics are detailed in the July 2006 issue of the Stat-Teaser newsletter at


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 monthwhy it's good to visualize data:

"We cannot learn efficiently about nature by routinely taking the rich information in data and reducing it to a single number."
—William Cleveland, 1993, "Visualizing Data" (I've always believed that a graph is worth 1000 numbers, so several years ago I purchased this book not long after publication. It led to a revealing article in the September 1999 "Stat-Teaser" newsletter, which you can see at
Trademarks: Design-Ease, Design-Expert and Stat-Ease 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, Stat-Ease marketing director, and all the remaining staff


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 (see above)

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