Content By Donald J. Wheeler

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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.

Donald J. Wheeler’s picture

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

Donald J. Wheeler’s picture

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.

Donald J. Wheeler’s picture

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.

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By: Donald J. Wheeler

The ultimate purpose for collecting data is to take action. In some cases the action taken will depend upon a description of what is at hand. In others the action taken will depend upon a prediction of what will be. The use of data in support of these two types of action will require different types of analyses. These differences and their consequences are the topic of this article.

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By: Donald J. Wheeler

Some properties of a probability model are hard to describe in practical terms. The explanation for this rests upon the fact that most probability models will have both visible and invisible portions. Understanding how to work with these two portions can help you to avoid becoming a victim of those who, unknowingly and unintentionally, are selling statistical snake oil.