Issue: Volume 5, Number 11
Date: November 2005
From: Mark J. Anderson, Stat-Ease, Inc. (

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

Here's an appetizer to get this Alert off to a good start: I have not tried making a potato gun, but I've heard about it. Christopher Kimball, Founder and Editor of "Cook's Illustrated" called me some years ago about making their tests more statistical, but evidently his chefs consider cooking an art and would not go along with this. However, I suspect that the sort of person who would make a potato gun might welcome help with their experimentation.* At this time of the year,post-Halloween in the USA, pumpkins naturally become the thing to fling. One such pumpkin gun purportedly shoots these golden globes, the heavier the better (?), more than one mile.

*(For ideas on how to build potato cannons, paper match rockets, fire kites, tennis ball mortars and the like, read "Backyard Ballistics" by engineer William Gurstelle: See

Here's what I cover in the body text of this DOE FAQ Alert (topics that delve into statistical detail are designated "Expert"):

1. Software alert (2nd): New version of Design-Expert® software
2. FAQ: Determining the minimal effect for power calculation
3. FAQ: What would be a good R-squared for a manufacturing study?
4. Expert-FAQ: Follow-up to response surface method (RSM) design
5. Book alert: Positive reviews for "RSM Simplified"
6. Workshop alert: California workshops on DOE

PS. Quote for the month: Encouraging creativity (for example, when considering variables for an experiment).


1. Software Alert (2nd): New version of Design-Expert® software

Stat-Ease announces a major new release—version 7 of Design-Expert software (DX7). For a free, fully-functional 45-day trial, click this link: Pricing for new licenses and upgrades can be seen at the Stat-Ease e-commerce site:

Those of you who've used previous versions will be impressed with the many improvements in V7, including:

> Pareto chart of t-values of effects: Quickly see the vital few effects relative to the trivial many from two-level factorial experiments.
> Min-Run Res IV (two-level factorial) designs for 5 to 50 factors: Screen main effects with maximum efficiency in terms of experimental runs.
> Full-color contour and 3D surface plots: Graduated or banded colorization adds life to reports and presentations.
> Magnification feature: Incredible tool for expanding areas of interest on trilinear mixture graphs.

This is only a small sample from the features in this landmark upgrade from Stat-Ease that you will see highlighted at


2. FAQ: Determining the minimal effect for power calculation

-----Original Question-----
Design-Expert user and "fanatically loyal customer of Stat-Ease"

"I was looking at the "Eight Keys to Successful DOE"—an article that you and Shari Kraber wrote for Quality Digest (posted at I have re-read this article several times over the past months. The 3rd key is "Replicate to dampen uncontrollable variation (noise)." To illustrate this point via the case study on injection molding, the article states that "... control charts reveal a standard deviation of 0.60. Management wants to detect an effect of magnitude 0.85." How would management come up with that value?"

First of all, thanks for being such a fan of our company and its software! To assess how much power will be needed in an experimental design, and thus the number of runs, it's vital that the signal to noise ratio be estimated.* I think the concept of noise is readily grasped by technical professionals. The tricky part is assessing what signal the experimenter hopes to detect. This relates to what's considered important at the bare minimum by the customers and/or management. I advise bouncing off some numbers—beginning with one that's ridiculously low. So in this case, ask if your management would be pleased if your experiment produced a result of 0.01 effect on the response, in this case percent shrinkage of a molded part. "No way!" they may say. OK, you continue, "How about a 0.50 response change?" Management now responds, "Maybe." You then toss out a potential effect of 1 and get a very definite affirmative that this would be of interest. Obviously the value of 0.85 used in the article is somewhat arbitrary, but to calculate power some number is needed. As a general rule, when signal-to-noise reaches a level of 1.5 (in this case it is 0.85/.6—close enough), a typical design with 16 runs will likely reveal even the marginally important effects—if they are produced (no guarantee of this—depends whether the right factors are chosen and the levels set far enough apart).

*(V7 of Stat-Ease software now allows users to enter a specific signal-to-noise ratio when they do a design evaluation for assessing power.)

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


3. FAQ: What would be a good R-squared for a manufacturing study?

-----Original Question-----
From: Minneapolis
"Would 0.5 be a good R-squared for a manufacturing study?

Based on having seen hundreds of analyses over the past three decades, I'd agree that 0.5 is not an unreasonable benchmark for R-squared from a manufacturing experiment. However, the real key is having a low p-value on the overall model ANOVA. I recall Doug Montgomery talking about how people always pester him about R-squared and how much he hates this statistic being over-emphasized. After a day of lecturing at an industrial client, he walked down the hall and overheard people still talking about their R-squared results. When these R-squared addicts saw him, the door was quickly closed so the discussions about this statistic could continue without Dr. Montgomery intervening! I did some searching on the Internet and found an article called "R2 is a Big Fat Idiot." I liked the title, but the article did not prove very helpful. To see why R-squared is not a reliable statistic, see an article by Stat-Ease consultant Pat Whitcomb at


4. Expert-FAQ: Follow-up to response surface method (RSM) design

-----Original Question-----
"After I run an RSM design (such as a central composite—CCD) I often need to run additional experiments in a region next to my previous data to explore it more fully. Could you direct me to the methodology for doing this properly? I thought perhaps you had answered this question previously. Thank you."

I do not recall fielding a question quite like this before: It is a good one! When performing evolutionary operation (EVOP), a conservative move is to keep sequential experiments adjoined to the one done previously. For example, after replicating two factors such as time and temperature in a 2^2 design enough to achieve the power needed for statistical confidence in the path of steepest ascent, the experimenter would create a new design with at least the corners of the square regions (DOE #1 and DOE #2) touching.* I infer from your question that a relatively broadly-ranged CCD for response surface methods (RSM) may generate a rising ridge, the upper reaches of which you then want to explore in more detail. The EVOP philosophy dictates that you take the attitude of "been there and done that" on the CCD #1 and re-adjust your focus to the new region. My advice is to set up a new CCD, perhaps with ranges somewhat narrowed, that stands up on its own with sufficient data to create a new predictive model.

*(This is pictured at a site maintained by a Swedish consultant:

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


5. Book alert: Positive reviews for "RSM Simplified"

The October issue of "Quality Progress" magazine provides, on page 89, a review of "RSM Simplified", a book on response surface methods co-authored by me and my colleague Pat Whitcomb. It concludes that "this book is perfect introductory material for quality and Six Sigma professionals who need to learn the tools for identifying critical process parameters and optimizing their processes." :)

Also, I just caught up with the March/April 2005 on-line version of the "Journal for Healthcare Quality" in which Suzanne Belanger says*: "Even though I had virtually no idea what I was reading about, RSM Simplified was entertaining and enlightening...Unless the reader is engaged in basic research in pharmaceuticals or medicine, RSM Simplified won't have much applicability to the kind of data analysis done in most healthcare organizations. Read it for fun." Hmmm, I would say this is a real compliment!

*(p16 at

(For more details on "RSM Simplified: Optimizing Processes Using Response Surface Methods for Design of Experiments" and sample chapters, see From there you can link to an on-line page to order this soft-cover book, which is accompanied by a free CD-ROM of Design Expert V7 software for educational use.)


6. Workshop alert: California workshops on DOE

Stat-Ease makes its annual escape from the cold Minnesota winter with two presentations of Experiment Design Made Easy in California:
—Anaheim, December 6–8
—San Jose, January 10–12
If you work elsewhere in the country (nowhere near the west coast of the USA), your management might be more amenable to funding the workshop hosted on the 8th through the 10th this month of November at our Minneapolis training center. These are likely to be great days for being indoors studying statistics!

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.


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—Encouraging creativity (for example, when considering variables for an experiment):

"It is easier to tone down a wild idea than to think up a new one."

Alex Osborne, "Father of the Brainstorm" according to

Pat Whitcomb suggests some other methods to enhance creativity:
—Dr. Edward de Bono’s Lateral Thinking methods (see
—Triz theory of solving inventive problems (see
Warning: This is a big file, so it takes a while to download.

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
—Fellow Stat-Ease consultants Pat Whitcomb and Shari Kraber (see for resumes)
—Statistical advisor to Stat-Ease: Dr. Gary Oehlert (
—Stat-Ease programmers, especially Tryg Helseth (
—Heidi Hansel, Stat-Ease marketing director, and all the remaining staff


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#1 Mar 01
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