In my last column I explained how many situations have an inherent response surface, which is the “truth.” However, any experimental result represents this true response, which is unfortunately obscured by the process’s common-cause variation. Regardless of whether you are at a low state of knowledge (factorial) or a high state of knowledge, the same sound design principles apply.

The contour plot: a quadratic ‘French curve’

Response surface methodology’s objective is to model a situation by a power series truncated after the quadratic terms. In the case of three independent variables (x1, x2, x3), as in the tar scenario from my column, “90 Percent of DOE Is Half Planning,” in May 2016:

Y = B0 + (B1 x1) + (B2 x2) + (B3 x3) + (B12 x1 x2) + (B13 x1 x3) + (B23 x2 x3) + [B11 (x1**2)] + [B22 (x2**2)] + [B33 (x3** 2)]

Which designs give the best estimates of these B coefficients?

In part one yesterday, we looked at stats of the Boston Red Sox bullpen, a typical example of baseball’s tendency to find special cause in just about anything. The Boston Globe article on which these two columns are based has been a gold mine for teaching many useful, basic concepts about variation. Today we’ll continue the analysis with a closer focus on common cause.

Once again, for those of you who aren’t interested in the statistical mechanics but want to be aware of how this type of analysis can drastically change one’s thinking, just skip to the bottom-line conclusions where indicated. For my non-U.S. readers, I hope you’ll be able to follow the analysis philosophy and see parallels to your favorite sports and news articles.

Any italics in direct quotes are mine, and if I make comments within a quote, I show that by inserting [DB:...].

April Fool’s Day (today) and the opening of baseball season (this Sunday) are upon us. To mark the first event, I’ll let my distinguished colleague Donald Wheeler make some eloquent and crucial statistical points that turn out to be, well, laughably simple. (No fooling!) Regarding the baseball connection, please bear with me.

Wheeler is one of the world’s leading W. Edwards Deming statistical experts, and he’s been an influential stealth mentor of mine for 30 years. He’s an excellent theoretician who can certainly get appropriately technical with statistics if he needs to; however, when he does, it’s usually to soundly deflate what Deming called “such nonsense” being taught today.

Marketers are relentless in their efforts to seduce you with fancy tools, acronyms, Japanese terminology—and promises—about their versions of formal improvement structures such as Six Sigma, lean, lean Six Sigma, or the Toyota Production System, each with its own unique toolbox.

In my last column, I discussed the need of becoming more effective by using far fewer tools in conjunction with critical thinking to understand variation.

In the midst of all this, I’ve seen W. Edwards Deming’s and Walter A. Shewhart’s brilliant, fundamental plan-do-study-act (PDSA) morph to an oft-spouted platitude. I laugh when I hear people casually comment that PDSA and plan-do-check-act (PDCA) are the same thing. They’re not.