Stat-Ease | The DOE FAQ Alert Vol. 11 No. 1

Newly-released version 8.0.6 of Design-Expert software is posted at this download site for free trial evaluation. This web site also provides free patches to update older licensed versions of 8.0.

The release is primarily for maintenance, but it does provides new features, for example, improved color differentiation for design points displayed on model graphs.

View the ReadMe file for other features, installation tips, known ‘bugs,’ change history, and FAQs.

PS. Heads-up: If you want to receive notice when an update becomes available, go to Edit on the main menu of your program, select Preferences and, within the default General tab, turn on the “Check for updates on program start” option.

2: Newsletter alert: August issue of the Stat-Teaser explains how you can “Fly Your Way to Better DOE” via in-class experimentation

Many of you have received, or soon will, 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 August issue at this link.

It features an article by Consultant Brooks Henderson that explains how you can “Fly Your Way to Better DOE” via in-class experimentation. He reports on results from the latest paper-helicopter fly-offs at South Dakota School of Mines & Technology—his alma mater.

This Stat-Teaser also provides an explanation on the “Power of DOE Using Qualitative vs. Quantitative Variables” contributed by Arved Harding—a “friendly neighborhood statistician” who hails from Kingsport, Tennessee.

Thank you for reading our newsletter. If you get the hard copy, but find it just as convenient to read what we post to the Internet, consider contacting us to be taken off our mailing list, thus conserving resources. (Note: You will be notified via the DOE FAQ Alert on new newsletter posts.) In any case, we appreciate you passing along hard copies and/or the link to the posting of the Stat-Teaser to your colleagues.

3: FAQ: How can you explain the meaning of an interaction?

Original Question:

From a Technology Improvement Head:
“Thanks so much for the webinar on How to Get Started with DOE, it was very clear and useful for me. I have a question regarding interactions: How can you explain the meaning of the interaction? I mean, is it correct to say that when you increase the value of your variable A (stirring) and decrease the value of B (pre-heat) you will have an additional effect on taste due to the interaction? Is this a positive interaction?”

Answer:

From Stat-Ease Consultant Brooks Henderson:
“You are welcome. I am glad you enjoyed the webinar.

Let me answer your question by going back to the example that I presented. What happens with an interaction is that the graph for one factor will change slopes depending on the level of another factor. Here are some pictures to show you what happens. For the popcorn DOE let’s look at the taste response. If we look at just a one factor plot of stirring (see first graph below), you don’t get the entire picture (note the warning in red). On the first graph, I have factor B set to the low level. In this case, the slope of the line is steeper. In the second graph below, I have dragged factor B to the high level (by clicking and dragging the red bar on the factors tool below—see the green circle). Here, with factor B at the high level (second graph), the slope of the line is not as steep. That is due to the significant AB interaction. The plot of A changes depending on the level of B.

One Factor Plot 3-1 One Factor Plot 3-2
The effect of one factor (A) depends on the other (B)

In the two-factor interaction (2FI) plot I showed during the webinar, the two different slopes are shown on the same graph (see below). This is the plot of the AB interaction. Here, the black line indicates what happens to my response as we go from the low level to the high level of factor A while factor B is held at the low level (B- = 0.00 seconds). The red line is what the factor A graph looks like when B is held at the high level (B+ = 360.00 seconds). Again, the interaction just indicates that the graph for factor A will change depending on what level B is set to. The same is true for the factor B graph. It will depend on what level of A you are looking at.

Interaction Graph
Interaction plot

So, to summarize, just be sure to look at the interaction graph when there is a significant interaction involving a factor. Do not look at the graph for just that factor, because it does not show the whole story. You need to look at the interaction graph.”

(Learn more about interactions by attending the two-day computer-intensive workshop “Experiment Design Made Easy.” Click on the title for a complete description. Link from this page to the course outline and schedule. Then, if you like, enroll online.)

4: Expert FAQ: Selecting effects via the half-normal versus a backwards regression: How do you explain discrepancies between these two approaches?

From an Industrial Statistician:
“For several designs that I have analyzed the half-normal and Pareto plots indicate that nothing is significant, but if you do a backwards regression on the effects list things show up as being significant. I would like to communicate to my colleagues the correct way to interpret results, especially when two methods indicate that nothing is significant and the third method indicates that several terms are significant. For example, try analyzing these responses from a 2³ design (listed in standard order): 15.6, 18.2, 20.0, 40.4, 55.2, 55.4, 23.0, 41.2—the half-normal shows nothing significant but ANOVA lists all effects significant except for the ABC.”

From Stat-Ease Consultant Wayne Adams:
“When effects are selected, they are moved from the error pool into the model pool. The ANOVA compares the size of the model pool to the size of the error pool. If the model pool is enough larger, the test comes up significant. The key is you must only move explanatory terms from the error pool to the model. If you move the largest error term, then the error pool is being underestimated and the tests can show false significance. Backward regression algorithms start with everything (all terms) in the model before removing the least-significant term. In your example this term is ABC. The effect attributed to ABC is orders of magnitude smaller than everything else! Given that this is the only thing in error, the test shows the model pool is significantly larger than the error pool, when the truth is plain to see on the half-normal plot (shown below): All of the effects should be part of the error pool, that is, nothing is significant.

Selection algorithms should only be used when you know something truly is significant in the model. Even then, the selection needs to be backed up with a careful review of the ANOVA report that addresses these questions:

The half-normal plot is the best method we’ve found for selecting the correct effects for the model.”

5: Webinar alert: Basics of Response Surface Methodology (RSM) for Process Optimization, Part 1

Response Surface Methods (RSM) can lead you to the peak of process performance. In this intermediate-level webinar presented on Thursday, September 8th, 2011 at 2 pm CDT*, Stat-Ease Consultant Shari Kraber will introduce the fundamental concepts of response surface methods (RSM).

If you are new to RSM, this webinar is for you! 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 karen@statease.com. If you can be accommodated, she will provide immediate confirmation and, in timely fashion, the link with instructions from our web-conferencing vendor GotoWebinar.

*(To determine the time in your zone of the world, try using this link. We are based in Minneapolis, which appears on the city list that you must manipulate to calculate the time correctly. Evidently, correlating the clock on international communications is even more complicated than statistics! Good luck!).

6: Events alert: ‘Heads-up’ on “Design Space via DOE Using Intervals to Manage Uncertainty” for industrial statisticians in Europe

Stat-Ease Consultant Pat Whitcomb will detail “Design Space via DOE Using Intervals to Manage Uncertainty” at the European Network for Business and Industrial Statistics (ENBIS) annual gathering in Coimbra, Portugal on September 4-8. Get details on the conference here.

On September 13 at IEEE’s Holm Conference on Electrical Contacts in Minneapolis I will present the Morton Antler Lecture on “Modern Design of Experiments (DOE)—75 Years of Advancements in Multifactor Test Methods”. For more information click here.

Click here 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 reimbursement for travel expenses. 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 in the USA, Belgium and India

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! Also, take advantage of a $395 discount when you take two complementary workshops that are offered on consecutive days.

All classes listed below will be held at the Stat-Ease training center in Minneapolis unless otherwise noted.

*** Take both MIX and MIX2 to earn $395 off the combined tuition!

See this web page 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 workshops@statease.com.