Abstract
Suppose you have some “process” whose ongoing quality you want to assess. You do this by making regular readings on some measurable property of the process. Some examples might be:
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You run a sugar packaging plant where a continuous filler line fills paper bags with sugar. Each bag is supposed to contain 10 pounds of sugar. Although the inevitable random variability makes a constant weight in all bags impossible, you would like to check that there is no excessive variability from one bag to another, and that the average weight of all bags is correct. To achieve this, you take random samples of the production from each shift and weigh the sugar in each sampled bag accurately. These weights are your process measurements.
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You run the emergency room in a hospital. There is some concern about the time taken with the paperwork admitting accident victims. To check this, you have an observer watch a random sample of incoming accident victims and see how long it takes to fill out the forms for each of them. It is clearly important to have a sample that represents all different times of day and different days of the week, so you ensure that your sample correctly represents all these different time periods.
Happy is he who has been able to learn the causes of things. Virgil
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© 1998 Springer Science+Business Media New York
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Hawkins, D.M., Olwell, D.H. (1998). Introduction. 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_1
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DOI: https://doi.org/10.1007/978-1-4612-1686-5_1
Publisher Name: Springer, New York, NY
Print ISBN: 978-1-4612-7245-8
Online ISBN: 978-1-4612-1686-5
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