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In statistical process control (SPC), the ¯ and R chart is a type of scheme, popularly known as control chart, used to monitor the mean and range of a normally distributed variables simultaneously, when samples are collected at regular intervals from a business or industrial process. [1]
Use variable-width control limits [6] Each observation plots against its own control limits as determined by the sample size-specific values, n i, of A 3, B 3, and B 4: Use control limits based on an average sample size [7] Control limits are fixed at the modal (or most common) sample size-specific value of A 3, B 3, and B 4
Next, the upper control limit (UCL) and lower control limit (LCL) for the individual values (or upper and lower natural process limits) are calculated by adding or subtracting 2.66 times the average moving range to the process average: = ¯ + ¯.
For the purposes of control limit calculation, the sample means are assumed to be normally distributed, an assumption justified by the Central Limit Theorem. The X-bar chart is always used in conjunction with a variation chart such as the x ¯ {\displaystyle {\bar {x}}} and R chart or x ¯ {\displaystyle {\bar {x}}} and s chart .
Regression control chart differs from a traditional control chart in four main aspects: It is designed to control a varying (rather than a constant) average. The control limit lines are parallel to the regression line rather than the horizontal line. The computations here are much more complex. It is appropriate for use in more complex ...
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The control limits are set at three standard deviations on either side of the process mean, and are known as the upper control limit (UCL) and lower control limit (LCL) respectively. [2] If the process data plotted on the control chart remains within the control limits over an extended period, then the process is said to be stable. [2] [3]
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