x̅ and s chart

${\bar {x}}$ and s chart
Originally proposed by	Walter A. Shewhart
Process observations
Rational subgroup size	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	${\bar {s}}={\frac {\sum _{i=1}^{m}{\sqrt {\frac {\sum _{j=1}^{n}\left(x_{ij}-{\bar {\bar {x}}}\right)^{2}}{n-1}}}}{m}}$
Upper control limit	$B_{4}{\bar {S}}$
Lower control limit	$B_{3}{\bar {S}}$
Plotted statistic	${\bar {s}}_{i}={\sqrt {\frac {\sum _{j=1}^{n}\left(x_{ij}-{\bar {x}}_{i}\right)^{2}}{n-1}}}$
Process mean chart

Center line	${\bar x}={\frac {\sum _{{i=1}}^{m}\sum _{{j=1}}^{n}x_{{ij}}}{mn}}$
Control limits	${\bar {x}}\pm A_{3}{\bar {s}}$
Plotted statistic	${\bar x}_{i}={\frac {\sum _{{j=1}}^{n}x_{{ij}}}{n}}$

In statistical quality control, the ${\bar {x}}$ and s 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]

The sample size is relatively large (say, n > 10— ${\bar {x}}$ and R charts are typically used for smaller sample sizes)
The sample size is variable
Computers can be used to ease the burden of calculation

The "chart" actually consists of a pair of charts: One to monitor the process standard deviation and another to monitor the process mean, as is done with the ${\bar {x}}$ and R and individuals control charts. The ${\bar {x}}$ and s chart plots the mean value for the quality characteristic across all units in the sample, ${\bar x}_{i}$ , plus the standard deviation of the quality characteristic across all units in the sample as follows:

s={\sqrt {\frac {\sum _{i=1}^{n}{\left(x_{i}-{\bar {x}}\right)}^{2}}{n-1}}}

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]

$B_{3}{\bar {s}}$ (lower) and $B_{4}{\bar {s}}$ (upper) for monitoring the process variability
${\bar {x}}\pm A_{3}{\bar {s}}$ for monitoring the process mean

where

{\bar {x}}

and

{\bar {s}}={\frac {\sum _{i=1}^{m}s_{i}}{m}}

are the estimates of the long-term process mean and range established during control-chart setup and A₃, B₃, and B₄ 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 R and individuals control charts, the ${\bar {x}}$ chart is only valid if the within-sample variability is constant.^[4] Thus, the s chart is examined before the ${\bar {x}}$ chart; if the s 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.

When samples collected from the process are of unequal sizes (arising from a mistake in collecting them, for example), there are two approaches:

Technique	Description
Use variable-width control limits^[5]	Each observation plots against its own control limits as determined by the sample size-specific values, n_i, of A₃, B₃, and B₄
Use control limits based on an average sample size^[6]	Control limits are fixed at the modal (or most common) sample size-specific value of A₃, B₃, and B₄

References

↑ "Shewhart X-bar and R and S Control Charts". NIST/Sematech Engineering Statistics Handbook. National Institute of Standards and Technology. Retrieved 2009-01-13.
↑ 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.
↑ Montgomery, Douglas (2005). Introduction to Statistical Quality Control. Hoboken, New Jersey: John Wiley & Sons, Inc. p. 225. ISBN 978-0-471-65631-9. OCLC 56729567.
↑ 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.
↑ Montgomery, Douglas (2005). Introduction to Statistical Quality Control. Hoboken, New Jersey: John Wiley & Sons, Inc. p. 227. ISBN 978-0-471-65631-9. OCLC 56729567.
↑ Montgomery, Douglas (2005). Introduction to Statistical Quality Control. Hoboken, New Jersey: John Wiley & Sons, Inc. p. 229. ISBN 978-0-471-65631-9. OCLC 56729567.

External links

Online tool to build Xbar and S control charts

This article is issued from Wikipedia - version of the 10/25/2016. The text is available under the Creative Commons Attribution/Share Alike but additional terms may apply for the media files.

x̅ and s chart

See also

References

External links