Content By Donald J. Wheeler

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By: Donald J. Wheeler

During the past three months James Beagle and I presented columns that made extensive use of analysis of means techniques. Since these techniques may be new to some, this column explains when to use each technique and where to find tables of the appropriate scaling factors.

In 1967, Ellis R. Ott published his analysis of means technique (ANOM) for comparing treatment averages with their grand average. This technique is a generalized version of the average and range chart. However, the assumption that allows this generalization also imposes a restriction of where this technique can be used. The generalization allows us to compute limits with a fixed overall alpha level (the user-specified risk of a false alarm). The restriction is that we can only use ANOM for the one-time analysis of a finite amount data (such as occurs in experimental studies).

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

In Parts One and Two we defined the equivalence of instruments in terms of bias and measurement error based on studies using a single standard. Here we look at comparing instruments for differences in bias or differences in measurement error while using multiple standards.

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.

Once again we must emphasize that it makes no sense to seek to compare measurement systems that do not display a reasonable degree of consistency. Consistency must be demonstrated, it cannot be assumed, and a consistency chart is the simplest way to do this. 

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.

Donald J. Wheeler’s picture

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?

Donald J. Wheeler’s picture

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.