Content By Donald J. Wheeler

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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.

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.

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By: Donald J. Wheeler

In The Music Man, the con man Prof. Harold Hill sells band instruments and uniforms and then tells the kids that they can play music if they will “just think about the notes and then play them.” In many ways this “think system” is similar to what you are asked to do with the define, measure, analyze, improve, control (DMAIC) approach to quality improvement.

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By: Donald J. Wheeler

Last month we looked at what the empirical rule tells us about the data in a histogram. This month we will consider if there are any commonalities between different probability models that will allow us to make categorical statements without having to know the exact form of the probability model.

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By: Donald J. Wheeler

How can we use descriptive statistics to characterize our data? When I was teaching at the University of Tennessee I found a curious statement in a textbook that offered a practical answer to this question. This statement was labeled as “the Empirical Rule,” and it is the subject of what follows.

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

Whenever we make a measurement, we have to decide how many digits to record. Traditional answers for this question are often little more than guesswork glorified by time. And with digital readouts, are all the displayed digits real? This column provides a sound and practical answer to these perennial questions.