Issue: Volume 9, Number 8
Date: August 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 to:

For an assortment of appetizers to get this Alert off to a good start, follow this link,* (-> new web site!), and see a number of new blogs (listed below, beginning with the most recent one):

—Walk fast to stay ahead of the grim reaper
—USA health care system "Pareto-inefficient"?
—Coriolis effect continues to make the rounds despite efforts to flush it down the drain
—Cartoon guides to math & stats
*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 ezine—the DOE FAQ Alert—shares one-on-one communications with Stat-Ease StatHelp, anyone (after gaining approval for registration) can post questions and answers to the Forum, which is open for all to see (with moderation). Check it out and weigh in!

Here's a synopsis of the most recent question posted (found in the DOE "Analysis" section):
>From: A Registered Member
>Topic: "Orthogonality violation"
See the question and answer and feel free to contribute to this discussion thread by becoming registered as a user of the Support Forum for Experiment Design (if you're not already).

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 give up degrees of freedom for multiple blocks?
2. FAQ: How do the predictive model equations in coded versus actual terms differ and which is most appropriate to use?
3. Expert FAQ: What is the difference between the internally and the externally studentized residuals?
4. Info Alert : DOE for process cheese
5. Webinar Alert (2nd): Analyzing historical data
6. Book Giveaway : Three first editions of "RSM Simplified"
7. Events Alert: Talk on practical aspects of algorithmic design of physical experiments
8. Workshop Alert : See when and where to learn about DOE

P.S. Quote for the month: Something to ponder at the beach.


1. FAQ: Why give up degrees of freedom for multiple blocks?

-----Original Question-----
Research Fellow, Belgium
"I created a design with 6 blocks. Why does this use up 5 degrees of freedom? I would think that 1 degree of freedom should suffice."

Answer (from Stat-Ease Consultant Wayne Adams):
"Although block effects are not included in the predictive model, they do use up degrees of freedom. These are needed to estimate the difference between the overall grand average and the individual block averages. So if there are six blocks, you need to estimate five block differences from the overall average, with the sixth difference being found through subtraction. Our software mathematically removes this difference from the analysis by applying the block effects. We consider all blocks to be random effects. As such, we still cannot use the block effects in the final predictive model; blocking is merely a way to protect the analysis of the factor effects from uncontrolled changes and/or lack of randomization."

(Learn more about blocking 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. FAQ: How do the predictive model equations in coded versus actual terms differ and which is most appropriate to use?

-----Original Question-----
A chemist
"How do the predictive model equations in coded versus actual terms differ and which is most appropriate to use?"

Answer (from Stat-Ease Consultant Wayne Adams):
"This is one of those "it depends" answers. As far as the difference, the units of measure must be considered in the actual model, whereas the coded model compares the lows to the highs within the design ranges. For example, consider two factors—temperature and time. Temperature ranges from a low (-1 coded) setting of 20 deg C to a high (+1 coded) of 40 deg C; time ranges from a low (-1 coded) of 30 seconds to a high (+1 coded) of 60 seconds. If you wanted to know the predicted outcome at 30 deg C for 45 seconds you would enter 30 deg C and 45 seconds into the actual model; or 0 temp and 0 time into the coded model. The prediction would be exactly the same. Due to this simplicity and having the model centered on the design space (middle of the space is all 0), the coded model is the best one to use for interpretation. You can easily answer questions like how different the outcome is between low and high settings equally well for any factor. If you are an advanced user and exporting the model into software that performs capability analysis then you want the actual model to correctly account for the factor standard deviation that is reported in the same units of measure as the factor."

PS. Also see Stat-Ease Consultant Shari Kraber's take on this at #1 (May DOE FAQ Alert).


3. Expert FAQ: What is the difference between the internally and the externally studentized residuals?

-----Original Question-----
A chemist
"I have some doubt about externally studentized and internally studentized residuals—please explain the difference between them and how they help in the model diagnostics."

Studentizing residuals overcomes differences in leverages that cause some points to be more tightly fitted than others. See for the details. Calculating these externally is useful for assessing outliers because each run is set aside as it's statistic gets calculated—what's called a "deletion diagnostic." Also see this primer on studentized residuals that, oddly enough, I found on a reference site for astronomers: Further details on these statistics can be found in your Design-Expert® program Help. That's a good place to start whenever you need statistical information. Refer to topic #2 in this year's February issue of the DOE FAQ Alert posted at Last, but not least, check out this out-take from the Stat-Ease "Handbook for Experimenters" on all the diagnostics we provide in our software:

Consultant Wayne Adams adds:
"There isn't a difference between internally and externally studentized residuals if you have an infinite sample size. ;) On a more practical note, keep in mind that the 'residual' is the actual observation less the prediction from a model. The model used for internal statistics is generated to best fit all the observations. The models (note the plural) used for external statistics are generated missing one observation at a time. The residual shows how well the "new" models fit (predict) the "externalized" observation. So to summarize difference between the two residuals:

-> Internal: one model with all the data; used to check constant variance, independence, and normality of the residuals.
-> External: new model (residual) calculated for each observation; used to check whether or not a point should be considered a significant outlier."

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


4. Info Alert: DOE for process cheese

Food Product Design magazine ( published an article on "DOE for Process Cheese" that details how Senior scientist Mostafa Galal, Ph.D., and senior technologist Michael Scheller, applied a general factorial design on emulsifiers. They discovered an ideal ratio of salts for improved appearance without degrading taste or meltability of the processed cheese. See If you are interested in publishing your DOE story, please contact Heidi via or call her 612.746.2033. See our current collection of DOE case studies and articles at We are especially in need of applications from the life sciences. Factor details can be coded for secrecy sake, so confidentiality need not be compromised.


5. Webinar alert (2nd): Analyzing historical data

You are invited to attend a free web conference by Stat-Ease Consultant Pat Whitcomb on "Analyzing Historical Data." This free conference, which Pat will present at an intermediate level statistically, will be broadcast on Wednesday, September 15th at 2 PM USA Central Time* (CT). He will repeat his webinar on Thursday, September 16th at 8 AM. It is aimed at those who need help in trying to make any sense out of pre-existing data using regression modeling. As Pat will point out, there are many perils and pitfalls to watch for when working with happenstance data. 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 Dulski, 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. Book Giveaway: Three first editions of "RSM Simplified"

(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 February 13 if you'd like (free!) one of three copies of "RSM Simplified" by Anderson & Whitcomb. This book just underwent a new printing*, so we can give away these first editions. I will forward your e-mail entries to my assistant Karen. Do not expect to hear from either of us unless your name is drawn as a winner. However, we do appreciate your participation in these giveaways. Watch for more of these in future DOE FAQ Alerts. Your odds of winning a free book increase by entering each time around!

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.

* See details at and from there order online. For information on the prerequisite "DOE Simplified" book, see

PS. On August 3rd I received this very kind kudo from a reader: "As an engineer in the semiconductor-industry I enjoyed reading your book RSM Simplified and made use of it very sucessfully. :) The style of writing is perfectly for learning and using RSM designs! I think there is no comparable book on the market."


7. Events Alert: Talk on practical aspects of algorithmic design of physical experiments

Pat Whitcomb will deliver a talk (co-authored by Wayne Adams) on the practical aspects of algorithmic (optimal) design of physical experiments at the annual conference of the European Network for Business and Industrial Statistics (ENBIS), which will be held September 20-24 in Goteborg, Sweden. For details on this event, see 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.


8. 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
> August 18-20, 2009 (Minneapolis)
> November 3-5, 2009 (Minneapolis)

—> Mixture Design for Optimal Formulations (MIX)
> August 11-13 (Minneapolis)
> October 27-29 (Minneapolis)

—> Response Surface Methods for Process Optimization (RSM)
> December 1-3 (Minneapolis)

—> Designed Experiments for Life Sciences (DELS)
> November 10-11, 2009 (Cambridge, MA)

—> DOE for DFSS: Variation by Design (DDFSS)
> November 17-18, 2009 (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 — something to ponder at the beach:

"Statistics are like a bikini—what they reveal is suggestive, but what they conceal is vital."

—Aaron Levenstein, 1911-1986, Professor of Business Administration, Baruch College 1961-1981.

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, #9-04 Apr 09, #9-05 May 09, #9-06 June 09, #9-07 July 09, #9-08 Aug 09 (see above)

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