and R chart

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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] It is often used to monitor the variables data but the performance of the and R chart may suffer when the normality assumption is not valid.

and R chart
Originally proposed byWalter A. Shewhart
Process observations
Rational subgroup size1 < n ≤ 10
Measurement typeAverage quality characteristic per unit
Quality characteristic typeVariables data
Underlying distributionNormal distribution
Performance
Size of shift to detect≥ 1.5σ
Process variation chart
Center line
Upper control limit
Lower control limit
Plotted statisticRi = max(xj) - min(xj)
Process mean chart
Center line
Control limits
Plotted statistic

Properties

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The "chart" actually consists of a pair of charts: One to monitor the process standard deviation (as approximated by the sample moving range) and another to monitor the process mean, as is done with the   and s and individuals control charts. The   and R chart plots the mean value for the quality characteristic across all units in the sample,  , plus the range of the quality characteristic across all units in the sample as follows:

R = xmax - xmin.

The normal distribution is the basis for the charts and requires the following assumptions:

  • The quality characteristic to be monitored is adequately modeled by a normally distributed random variable
  • The parameters μ and σ for the random variable are the same for each unit and each unit is independent of its predecessors or successors
  • The inspection procedure is same for each sample and is carried out consistently from sample to sample

The control limits for this chart type are:[2]

  •   (lower) and   (upper) for monitoring the process variability
  •   for monitoring the process mean
where   and   are the estimates of the long-term process mean and range established during control-chart setup and A2, D3, and D4 are sample size-specific anti-biasing constants. The anti-biasing constants are typically found in the appendices of textbooks on statistical process control.

Usage of the chart

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The chart is advantageous in the following situations:[3]

  1. The sample size is relatively small (say, n ≤ 10—  and s charts are typically used for larger sample sizes)
  2. The sample size is constant
  3. Humans must perform the calculations for the chart

As with the   and s and individuals control charts, the   chart is only valid if the within-sample variability is constant.[4] Thus, the R chart is examined before the   chart; if the R chart indicates the sample variability is in statistical control, then the   chart is examined to determine if the sample mean is also in statistical control. If on the other hand, the sample variability is not in statistical control, then the entire process is judged to be not in statistical control regardless of what the   chart indicates.

Limitations

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For monitoring the mean and variance of a normal distribution, the   and s chart chart is usually better than the   and R chart.

See also

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References

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  1. ^ "Shewhart X-bar and R and S Control Charts". NIST/Sematech Engineering Statistics Handbook]. National Institute of Standards and Technology. Retrieved 2009-01-13.
  2. ^ Montgomery, Douglas (2005). Introduction to Statistical Quality Control. Hoboken, New Jersey: John Wiley & Sons, Inc. p. 197. ISBN 978-0-471-65631-9. OCLC 56729567.
  3. ^ Montgomery, Douglas (2005). Introduction to Statistical Quality Control. Hoboken, New Jersey: John Wiley & Sons, Inc. p. 222. ISBN 978-0-471-65631-9. OCLC 56729567.
  4. ^ Montgomery, Douglas (2005). Introduction to Statistical Quality Control. Hoboken, New Jersey: John Wiley & Sons, Inc. p. 214. ISBN 978-0-471-65631-9. OCLC 56729567.
  5. ^ McCracken, A. K.; Chakraborti, S.; Mukherjee, A. (2013-10-01). "Control Charts for Simultaneous Monitoring of Unknown Mean and Variance of Normally Distributed Processes". Journal of Quality Technology. 45 (4): 360–376. doi:10.1080/00224065.2013.11917944. ISSN 0022-4065. S2CID 117307669.