Vol. 11, No. 1

January 2009

PQ Systems

Quality brain workout

Dumping control chart data into handy ‘containers’

Quality Quiz: With a video!

Data in everyday life

DOE with Jackie Graham

Bytes and pieces

FYI: Current releases


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Dumping control chart data into handy ‘containers:’
Histograms at work

Underlying the statistical concepts featured in Quality Quiz Classics, of course, are tools for problem-solving, data collection, and other important stages in the analytical process. Among these key tools is the histogram, for example. A glance at this simple but powerful tool suggests its relationship to other statistical concepts, such as normal distribution, and to control charts themselves. Transforming raw data into useful information is facilitated with histograms, as well as by other key tools.

If one were to take a control chart and turn it on its side, the data would fall into a pattern that forms a histogram.

For example: A control chart reflects data collected and recorded as follows:

If it were theoretically tipped sidewise, the following histogram would ensue:

This graphic demonstration of the control chart data gives information about central location, spread, and shape—key statistical concepts that can act as a guide for improvement of a process.

Histograms are used both to understand a situation as it stands, and to evaluate improvement after changes have been made. Because histograms give information about spread, central location, and shape, they provide unique insights that other data collection tools may not offer.

For example, in the figure below, both sets of data related to test scores have the same average—85. But the widths of the distributions are different, with distribution A wider than that of distribution B.

In the section of Quality Quiz Classics that addresses shape, the significance of this difference becomes clear. The wider distribution (A) reflects greater variation in the data. If this data were related to test scores for a classroom, to illustrate further, the histogram with the wider distribution indicates greater variation among scores. The distribution in B suggests that student scores are all closer to the average, rather than spread far from it. With respect to teaching, this information is useful, for it indicates that more of the students’ test scores approach the average, and that with continued teaching, that average can be improved.

Understanding variation and its meaning lies at the heart of improvement efforts. Seeing the shape of data and its distribution supports this understanding, giving additional insight that a simple control chart may not always convey.

‘Dumping’ the data into a histogram offers yet another way to communicate valuable information about a process that will inevitably lead to its improvement.

To learn more about histograms and other statistical and problem-solving tools, check out the Practical Tools for Continuous Improvement training materials. Call our toll-free number, 800-777-3020, for more information about these materials.


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