Abstract
The data we have been considering so far were on the continuous scale. In classical SPC terminology they are “variables” measures. The other classical SPC data type is “attribute” data, originally counts of the number of good and of defective (or in current terminology “conforming” and “nonconforming ” items) in a sample. We draw the distinction a little differently: into the standard statistical distinction between continuous and discrete measurements.
The ants go marching one by one, hurrah, hurrah ⋯ American camp song
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© 1998 Springer Science+Business Media New York
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Hawkins, D.M., Olwell, D.H. (1998). Discrete data. In: Cumulative Sum Charts and Charting for Quality Improvement. Statistics for Engineering and Physical Science. Springer, New York, NY. https://doi.org/10.1007/978-1-4612-1686-5_5
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DOI: https://doi.org/10.1007/978-1-4612-1686-5_5
Publisher Name: Springer, New York, NY
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