Xbar and R chart

$\bar x$ and R chart
Originally proposed by	Walter A. Shewhart
Process observations
Rational subgroup size	1 < n ≤ 10
Measurement type	Average quality characteristic per unit
Quality characteristic type	Variables data
Underlying distribution	Normal distribution
Performance
Size of shift to detect	≥ 1.5σ
Process variation chart
Center line	$\bar R = \frac {\sum_{i=1}^m max(x_{ij}) - min(x_{ij})}{m}$
Upper control limit	$D_4 \bar R$
Lower control limit	$D_3 \bar R$
Plotted statistic	Ri = max(xj) - min(xj)
Process mean chart
Center line	$\bar x = \frac {\sum_{i=1}^m \sum_{j=1}^n x_{ij}}{mn}$
Control limits	$\bar x \pm A_2 \bar R$
Plotted statistic	$\bar x_i = \frac {\sum_{j=1}^n x_{ij}}{n}$

In statistical quality control, the $\bar x$ and R chart is a type of control chart used to monitor variables data when samples are collected at regular intervals from a business or industrial process.^[1]

The chart is advantageous in the following situations:^[2]

1. The sample size is relatively small (say, n ≤ 10—$\bar x$ 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

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 $\bar x$ and s and individuals control charts. The $\bar x$ and R chart plots the mean value for the quality characteristic across all units in the sample, $\bar x_i$, plus the range of the quality characteristic across all units in the sample as follows:

R = x_max - x_min.

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:^[3]

• $D_3 \bar R$ (lower) and $D_4 \bar R$ (upper) for monitoring the process variability
• $\bar x \pm A_2 \bar R$ for monitoring the process mean

where $\bar x$ and $\bar R = \frac {\sum_{i=1}^m \left ( R_{max} - R_{min} \right )}{m}$ 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.

As with the $\bar x$ and s and individuals control charts, the $\bar x$ chart is only valid if the within-sample variability is constant.^[4] Thus, the R chart is examined before the $\bar x$ chart; if the R chart indicates the sample variability is in statistical control, then the $\bar x$ 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 $\bar x$ chart indicates.

See also

• $\bar x$ and s chart
• Shewhart individuals control chart

References

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. External link in |work= (help)
2. 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.
3. 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.
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.

External links

• Online tool to build Xbar and R control charts