It was my pleasure yesterday to teach design of experiments (DOE) at the Ohio State University’s Fisher College of Business for their Web-Leveraged Six Sigma Black Belt program. It turned out that one of the participants, Dennis Dubose — a statistician with Sumco USA, teaches DOE also, so we compared notes. He shared a nice explanation of the difference between simply repeating measures versus performing a true replication of the system setup for purposes of estimating error. Imagine you flip a coin and it comes up heads. You ask a colleague to look at it and call out the result. Then another colleague is asked to observe the coin and state which side came up. This is a repeated measure. A true replication is accomplished only by re-flipping the coin.

In response to studies that demonstrated chronic release of mercury vapor from amalgam fillings during chewing and brushing, the National Institute of Dental and Craniofacial Research (NIDCR) sponsored the Children’s Amalgam Trial (CAT). Today’s issue of Journal of American Medical Association (JAMA) unveiled the results of CAT, which is summarized by this intriguing quote from the lead author:

“The studies indicate that on average we probably don’t have much to worry about…”
(David Bellinger, Harvard Medical School)

In my prior career as a quality professional I’d heard that positive word-of-mouth about a company’s products and services may be passed along to three people, whereas bad news transmits much further — to at least twice as many individuals. This is a powerful inducement for businesses to go all out to avoid bad will. A new study, done in part by the prestigious Wharton School, adds a great deal of insult to the injury caused by customer complaints: As reported in the April 17th Business Week magazine and previously by CBC News, the stories about bad products and/or service become magnified with each re-telling, so that people down the line are up to 5 times as likely to avoid the business in question as the original unhappy customer. Evidently Hollywood is well aware of this phenomenon, because when they know a major movie will be a bomb, they roll it out to as many theaters as possible to maximize revenue the first weekend before the bad buzz can kill it.

While teaching mixture design for optimal formulation at two chemical companies the last two weeks, I was surprised to see that they still specify ingredients as pounds per hundred (pph)– bsed on their main component. This is really old-fashioned, not only for the use of English units of measures,* but because it does not account for the dilution effect. For example, a Stat-Ease client making a construction caulk ran a central composite design, a response surface method (RSM), that varied the filler from 100 to 250 pph and plasticizer from 50 to 100 pph based on 100 pounds of polymer. They kept the other 57 pounds in their recipe constant in terms of relative proportions for the ingredients. We did some simple calculations with a spreadsheet to make the bargraph shown on a 100 percent weight basis. Seeing how everything (depicted by colored segments) varied by concentration (not just the two ingredients they intended to change), it now became clear to the client that they would best re-design their experiment as a mixture.

*(Hired into the petroleum industry as chemical engineer in 1975, I quickly learned that there are 42 gallons in a barrel. Further research into English units of volume revealed that some number of firkins would fit into hogshead — I forget now. Just this last week I discovered a new measure of volume at a brew pub — they sold a “growler” of beer (64 fluid ounces) — a grrrreat deal!)

Having had enough of statistical tea-testing (inside joke), I bought two brands of beer this week — Summit Grand “pilsener” (Bohemian style) and Schell “pilsener” — for an impromptu test of my wife’s tasting abilities. She comes from 100 percent German stock consider beer as dear as mother’s milk. Thus, I was not surprised she got every one of these four combinations correct (“her answer in quotes”), even though I revealed nothing about the sources, saying there may be from one to four beers:
– Grand vs Grand “same”
– Grand vs Schell “different, but I don’t prefer one or the other”
– Schell vs Grand “different, but I don’t prefer one or the other”
– Schell vs Schell “same”.
The Grand beer is clearer, less expensive and offers a twist-off cap, so my wife and I will stay with this brand made in my home town of Saint Paul, Minnesota (Schell is made in New Ulm — a German settlement in the southwest of the State).

I would appreciate other ideas for a simple, but scientifically valid (?) taste-off like this for beverages or foods. My very cursory internet search brought up an article by James Fallows in the e-zine “Slate” titled Booze You Can Use, Getting the best beer for your money, but I do not necessarily advocate its methodology. I do happen to like Sam Adams a lot, which the Microsoft employees doing the tasting rated as best. Well before 1984, when Jim Koch founded The Boston Beer Company and the idea of a microbrewery, a chemist friend of mine snobbishly proclaimed that he only drank the “champagne of bottled beer,” Miller, and not the much cheaper (at that time) Old Milwaukee brand. I’d just taken a marketing class for my MBA that revealed that beers of this era (late 1970’s) were all essentially the same, but advertisers duped drinkers into paying more for “premium” brews. The chemist refused to believe this, so two of us chemical engineers set up a beer-tasting contest for a Super Bowl party. First we all sipped 10 brands of beer straight out of the bottle (perhaps a bit too much!). The chemist rated Miller top and “Old Swill-waukee” bottom. Then we repeated the test with beers poured blind to the tasters. The chemist unknowingly declared Old Milwaukee his favorite and, you guessed it, Miller the worst tasting. Of course by then he was good and drunk, but the point was made. Anyways, we sure had a lot of fun pretending to have a taste for beer!

Is there method in some of the March Madness of college basketball playoffs? Point shaving occurs in five percent of games with a spread higher than 12 points according to self-styled forensic economist Justin Wolfers in an interview with Jeff Mason. Wolfers is a University of Pennsylvania (Wharton School) professor who teaches the world’s only known course on the Economics of Sports Betting Markets. He extracted 9,244 games where teams were favored by 12 points or more from 44,120 college basketball games played between 1989 and 2005. Significantly, 46.2 percent of strong favorites won outright but failed to cover, which Wolfers thinks is a clear indication of point shaving. All a fellow has to do is miss on basket when his team leads by 10 points to come up short on the point spread of 12, thus making good on a big money bet. All this can be very tempting, so much so that the NCAA Gets Proactive On Gambling by inviting the FBI to talk with teams such as those going into the 2006 finals. Evidently players must get a lot smarter if they want to beat the system. Perhaps they had best pass on the basket-weaving classes and take a course on statistics!

According to statistics from a recent survey by America Online and Opinion Research Corporation cited in the New York Times*:
– 41 percent of the respondents check their e-mail in the morning before going to work
– 25% said they never go more than a few days without checking e-mail
– 60% check it on vacation
– 5% looked at e-mail in the bathroom.
That last one is where I draw the line!

Based on the title of her book, time-management expert Julie Morgenstern evidently advises that to avoid too much stress due to doing work at home one should Never Check E-Mail in the Morning. However, according to Steve Pavlina once you check email in the morning (before work?), you should not check it again until the end of the day. That seems extreme to me, but I like Steve’s idea to experiment on how infrequently you can check it without causing problems, that is, assuming feel that your email has gone out of control. I wonder about this myself. The thing is that I love to read and I like to write, so for me, email really may be addictive. I will know this is so the first time I find myself doing it in the bathroom!

*(11/20/05 article by Lizette Alvarez titled “Got 2 Extra Hours for Your E-Mail?”)

The Twin Cities got dumped on today with nearly a foot of heavy, wet snow. The weight of it snapped trees branches around my yard and left our Stat-Ease office powerless due to downed lines. I cannot say that we Minnesotans were not warned, but just before the storm hit, I heard one television weather man, a new guy giving his first forecast, say that the snow would fizzle due to the low pressure sucking in dry air. This reminds me of the movie “Weather Man” a dark comedy starring Nicolas Cage, whose character at one point before going on television decides to get the forecast from the real meteorologist behind the scenes:
> Meteorologist (M): “You should say, ‘We might see some snow…’ “…but it might shift south, miss us.'”
> Cage as Chicago TV weather guy: “I can say it, but I sort of wanna understand it. Why is it?”
> M: “Well, it’s Canadian trade winds.”
> Cage: “Behind all of it?”
> M: “Yeah, this will get pushed by wind out of Canada.”
> Cage: “So what’s it gonna do?”
> M: “I don’t know. It’s a guess. It’s wind, man. Blows all over the place.”
It doesn’t matter how variable snowfall can be for any given area, the new weathercaster in the Twin Cities television market will be in the doghouse now for a long time because he got this biggest storm of the winter wrong. As the move “Weather Man” says in its tag line: “In life, accuracy counts.” On the other hand, in our business of industrial statistics for predictive modeling we allow ourselves an out by this disclaimer: “Statistics means never having to say your certain.” This is lot more realistic!

PS. FYI, here’s a link to a forecasting model devised from neural network analysis of historical records that allows you to predict the ratio of snowfall depth to liquid water, a vary tricky property: University of Wisconsin – Milwaukee (UWM) realtime snow ratio forecast page .

I just finished off a bottle of Rosso Classic by Francis Coppola, the famous film director of the Godfather series and Apocalypse Now (the movie based on Conrad’s Heart of Darkness). This wine, which I really liked, is a blend of 47% Zinfandel, 32% Cabernet Sauvignon and 21% Syrah from California.

A couple of summers ago I promoted use of statistical design of experiments (DOE) to the Society of Industrial Microbiology at their annual conference. One of the attendees was a professor in the Department of Viticulture & Enology, University of California. What a great job!

I first became interested in the potential of DOE for wine-making when I came across some intriguing case studies by Douglas Montgomery in his textbook Design and Analysis of Experiments. A recent writeup on Professor Montgomery at Arizona State University (ASU) Regents’ Professor: Douglas Montgomery mentions that he’s “delved into aspects of chemical engineering in a project to develop an efficient wine-making process for an Oregon winery.” I believe his specialties are Pinot Noir and Pinot Grigio. The sensory evaluation of wine involves smell and taste.

I find these intriguing facts about human sensations at International Journal of Food Science & Technology

— There are around 10,000 taste buds, each of which contains 50–100 cells detection sweet, sour, salty, bitter and umami (produced by monosodium glutamates and the like).
— Humans can distinguish around 10,000 chemicals with upwards of 100 million olfactory receptors!

If you really want to enjoy an flavor sensation (combination of taste and smell), add cheese to the wine and make a pairing. According to Discovery News Brief “the first attempt to study wine and cheese pairing using a controlled environment and a scientific experimental design” found that the ideal combinations are Gewurtztraminer with a creamy blue cheese, unoaked Chardonnay with a semi-soft washed rind cheese and a blush wine with an aged cheddar. My suggestion is that these findings require confirmatory experiments!

I just returned from a week of surf and sun — a welcome respite from the coldest spell this winter in Minnesota. I went from -15 to 85 F from takeoff to touchdown over the four hour flight south — a span of 100 degrees! Mazatlan is home to the family of a young lady named Clarissa who spent a year with us as an exchange student — joining my three daughters in our residence in Stillwater. (You cannot imagine the pileup in the bathroom each morning before school!) Clarissa frequented the local health food store where, if I had not forbidden it, she would have purchased a scary collection of unregulated medicines for losing weight. It’s bad enough that the US Food and Drug Administration (FDA) provides no protection against the snake oils and such that are peddled to naive consumers, but south of the border in Mexico it is far worse. There, for example, many desperately ill Americans find “alternative” medicines and treatment, although as a colleague at Stat-Ease pointed out, either it is medicine or the alternative. I see in Mexico an unabashed belief in the supernatural, which I suppose is just as prevalent here in the USA, but relegated to tabloid and cable television features about haunted houses. Coincidentally I brought along for beach-reading the latest issue of American Scientist which included an article titled “The Cognitive Psychology of Belief in the Supernatural” that explains why humans harbor an innate faith in the unreal. I found this quote enlightening:

“…our brains have evolved so that science eludes us but religion comes naturally.”

PS. This issue of American Scientist also features an article telling why “Three statistical strategies —- replicating, blocking and modeling —- can help scientists improve accuracy and accelerate progress.” I agree! See Volume 94, Number 2, March-April 2006 .