Control limits are one of the main elements of control charts. Generally speaking, a control chart is the graphical display of a statistic regarding the quality characteristic of interest, computed from a sample randomly drawn from a process at different time instances. As natural variability is always present in a process, we expect some variability on the control chart. Excessive variability, owing to special cause events, is referred to as being due to an assignable cause. Otherwise, when only chance causes – also called common causes of variation – are operating, the process is said to be in statistical control. In order to make a decision about the status of the process, control limits are typically positioned so that under the hypothesis of no deviation in the process, a type I probability error corresponds to an economically acceptable average run length. In this article, we discuss in a general manner how to compute control limits, and give some remarks on how limits should be computed so as to reduce false out of control signals.

Zappa, D., Control Limits, in Marie Davidia, M. D., Brian Everit, B. E., Ron S. Kenet, R. S. K., Geert Molenbergh, G. M., Walter Piegorsc, W. P., Fabrizio Rugger, F. R. (ed.), Wiley StatsRef: Statistics Reference Online, Wiley, New York 2016: 1- 7. 10.1002/9781118445112.stat07844 [http://hdl.handle.net/10807/101089]

Control Limits

Zappa, Diego
Primo
2016

Abstract

Control limits are one of the main elements of control charts. Generally speaking, a control chart is the graphical display of a statistic regarding the quality characteristic of interest, computed from a sample randomly drawn from a process at different time instances. As natural variability is always present in a process, we expect some variability on the control chart. Excessive variability, owing to special cause events, is referred to as being due to an assignable cause. Otherwise, when only chance causes – also called common causes of variation – are operating, the process is said to be in statistical control. In order to make a decision about the status of the process, control limits are typically positioned so that under the hypothesis of no deviation in the process, a type I probability error corresponds to an economically acceptable average run length. In this article, we discuss in a general manner how to compute control limits, and give some remarks on how limits should be computed so as to reduce false out of control signals.
2016
Inglese
Wiley StatsRef: Statistics Reference Online
9781118445112
Wiley
Zappa, D., Control Limits, in Marie Davidia, M. D., Brian Everit, B. E., Ron S. Kenet, R. S. K., Geert Molenbergh, G. M., Walter Piegorsc, W. P., Fabrizio Rugger, F. R. (ed.), Wiley StatsRef: Statistics Reference Online, Wiley, New York 2016: 1- 7. 10.1002/9781118445112.stat07844 [http://hdl.handle.net/10807/101089]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10807/101089
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