Content By Donald J. Wheeler

Donald J. Wheeler
By: Donald J. Wheeler, James Beagle III

Last month we provided an operational definition of when measurement systems are equivalent in terms of bias. Here we will look at comparing the within-instrument measurement error between two or more systems.

Donald J. Wheeler
By: Donald J. Wheeler, James Beagle III

As soon as we have two or more instruments for measuring the same property the question of equivalence raises its head. This paper provides an operational definition of when two or more instruments are equivalent in practice. 

Churchill Eisenhart, Ph.D., while working at the U.S. Bureau of Standards in 1963, wrote: “Until a measurement process has been ‘debugged’ to the extent that it has attained a state of statistical control it cannot be regarded, in any logical sense, as measuring anything at all.” Before we begin to talk about the equivalence of measurement systems we need to know whether we have yardsticks or rubber rulers. And the easiest way to answer this question is to use a consistency chart.

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By: Donald J. Wheeler

Managers the world over want to know if things are “in control.” This usually is taken to mean that the process is producing 100-percent conforming product, and to this end an emphasis is placed upon having a good capability or performance index. But a good index by itself does not tell the whole story. So, if you want to learn how to be sure that you are shipping 100-percent conforming product, read on.

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By: Donald J. Wheeler

With the click of your mouse you can turn a list of values into a bubble plot. No thought or effort is required. Simply sit back and let the software gods do the heavy lifting of transforming your list of numbers into a fancy graph. What could possibly go wrong?

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By: Donald J. Wheeler


Story update 1/15/2019: Thanks to the sharp eye of Dr. Stan Alekman, who spotted an inconsistent value in figure 2, I discovered an error in the program used to construct the table of critical values for the prediction ratio. I have now corrected that problem and updated the entries in the table in figure 2. If you previously downloaded this column, you might want to download the corrected version below.

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By: Donald J. Wheeler

Process behavior charts are the interface between your data and your brain. But you have to begin by making a choice about which type of chart to use. You can either plot the individual values themselves, or you can organize your data into rational subgroups and plot the subgroup averages. This paper will discuss the issues involved and provide guidelines for when to use each chart.

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By: Donald J. Wheeler

In Part One and Part Two of this series we discovered some caveats of data snooping. In Part Three we discovered how listening to the voice of the process differs from the model-based approach and how it also

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By: Donald J. Wheeler

Parts One and Two of this series illustrated four problems with using a model-building approach when data snooping. This column will present an alternative approach for data snooping that is of proven utility. This approach is completely empirical and works with all types of data.

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By: Donald J. Wheeler

In “Data Snooping Part 1” (Quality Digest, Aug. 6, 2018) we discovered the basis for the first caveat of data snooping. Here we discover three additional caveats of data snooping.

Last month we discovered:

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By: Donald J. Wheeler

Data mining is the foundation for the current fad of “big data.” Today’s software makes it possible to look for all kinds of relationships among the variables contained in a database. But owning a pick and shovel will not do you much good if you do not know the difference between gold and iron pyrite.