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 http://www.statease.com/doealertreg.html. If this newsletter prompts you to ask your own questions about DOE, please address them via mail to:StatHelp@StatEase.com.
Here's an appetizer to get this Alert off to a
good start: http://news.bbc.co.uk/1/hi/magazine/4579681.stm. I found this article by Tom Geoghegan of BBC News Magazine intriguing
because some statistics instructors fall far short of the charisma
needed to make this dry subject invigorating to their students. The
article defines a charismatic person by three attributes:
Here's what I cover in the body text of this DOE FAQ Alert (topics that delve into statistical detail are designated "Expert"):
1. FAQ: How many
samples should you take per experimental run
PS. Quote for the month: A definition of enthusiasm (ties in to my appetizer on charisma).
From: Salt Lake City
"In our design of experiments (DOE) for every run we collect some samples. It is always better to have more samples to reduce error, but how many? Normally we do measurements on 10 samples. However, my current optimization design on 5 factors requires 38 runs which would generate 380 samples—far more than we can afford. What is the optimum sample size in this situation?"
I went through a similar discussion with a client doing a DOE on their sterilization process. Normally they take three samples and test for pathogens in triplicate. They realized that a 30-run DOE would generate several thousand tests! I convinced them to take only one sample and do one test per pathogen. It generated significant results that satisfied FDA for validation of process changes, but with only 1/9th the tests they first thought necessary.
This really illustrates the power of the averaging built into two-level factorial DOE.
*(Learn more about sample size by attending the two-day computer- intensive workshop "Statistics for Technical Professionals." See http://www.statease.com/clas_stp.html for a course description. Link from this page to the course outline and schedule. Then, if you like, enroll online.)
2. FAQ: Duplication versus true replication and its impact on statistical lack of fit
"I've attached data from a bio-assay for insulin. Each treatment combination was run 6 times. I've looked at this data a number of ways and I always get significant lack of fit. My question is: what are the contributors to lack of fit? Does it have anything to do with the design or is it the data?"
Next time around you ought to fully replicate (no short cuts!) the 2^2 design in a randomized run plan. Then enter the average results from each set of duplicates, which will provide much more stable results (by Central Limit Theorem—variance of sample-to-sample plus test-to-test will be reduced by n).
*For more details, including illustrations, see "FAQ—Interpreting Lack of Fit" by Stat-Ease Consultant Shari Kraber on page 2 of the May 2004 "Stat-Teaser" newsletter posted at http://www.statease.com/news/news0405.pdf.
(Learn more about lack of fit by attending the three-day computer-intensive workshop "Experiment Design Made Easy." See http://www.statease.com/clas_edme.html for a complete description. Link from this page to the course outline and schedule. Then, if you like, enroll online.)
3. Help Wanted: Stat-Ease seeks an enthusiastic DOE professional
We at Stat-Ease, Inc., a Minneapolis-based statistical software
company, offer an opportunity for an energetic person to join
our team. The full-time job opening encompasses a combination
of teaching and technical development. Job responsibilities include:
Minimum qualifications for this DOE professional are:
We desire experience in:
E-mail resumes to Shari Kraber at firstname.lastname@example.org by August 10th to be considered for this position as a DOE professional. Watch for our job notice at the Joint Statistical Meetings in Minneapolis, MN, August 8–11.
4. Events alert: Stat-Ease is exhibiting at the Joint Statistical Meetings (JSM) next week in Minneapolis
Please visit us at Booth #402 at the exhibit hall for the Joint Statistical Meetings in our home town of Minneapolis, Minnesota, next week, August 8-11.
Click on http://www.statease.com/events.html for a list of appearances by Stat-Ease professionals. We hope to see you sometime in the near future!
5. Reader response: A unsung pioneer for DOEKirstine Smith
From: Selden B. Crary, President, Crary Group, Palo Alto, CA
"I just read your article "Trimming the FAT out of Experimental Methods" (http://www.statease.com/pubs/doeprimer.pdf). It starts with a bit of history, emphasizing Fisher's 2^k factorial designs, and ends by encouraging RSM for the 21st century. You might be interested to learn that the true pioneer in DOE/RSM was Kirstine Smith, a graduate student from Denmark, who studied with Karl Pearson in London during WWI. In 1918 she published a wonderful paper, "On the 'Best' Distribution of Observations" in Biometrika that laid out the foundation of what is now known as statistical optimal design of experiments. Her example designs, which were all calculated by pencil and paper, are in the class of optimality now known as global optimality or G-optimality. You may learn a bit more about the history of Ms. Dr. Lecturer Smith from the biographical information on the WebDOE site at URL: http://www.webdoe.cc/publications/kirstine.php.
6. Workshop alerts: See when and where to learn about DOE; Also, note the short course on process analytical chemistry
See http://www.statease.com/clas_pub.html 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.
Also, as a favor to a Dr. Ann M. Brearley, with whom I shared duties as speaker for a recent symposium on chemical engineering, I am passing along this note from her: "If you have any clients who want to improve their chemical measurements, here is a short course that they might be interested in. Katherine Bakeev (GlaxoSmithKlineGSK) and I will be teaching a one-day short course on process analytical chemistry at the Eastern Analytical Symposium (Somerset, New Jersey) this coming November. The course is intended for chemists or engineers involved in process improvement projects who need better, faster chemical measurement tools such as NIR or in-line analyzers. Further information is available at the EAS web site, http://www.eas.org, under Short Courses.* Feel free to pass this along to anyone interested.
Ann M. Brearley, Ph.D.
*I notice several workshops at the Eastern Analytical Symposium on statistics and experiment design. Mark :)
I hope you learned something from this issue. Address your general questions and comments to me at: Mark@StatEase.com.
Mark J. Anderson, PE, CQE
PS. Quote for the month: A definition of enthusiasm (ties in to my appetizer on charisma):
is a kind of faith that has been set on fire."
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