When control limits are computed from the input data, three methods (referred to as default, MVLUE, and RMSDF) are available for estimating the process standard deviation . The method depends on whether you specify the STDDEVIATIONS option. If you specify this option, is estimated using subgroup standard deviations; otherwise, is estimated using subgroup ranges.
For an illustration of the methods, see Example 17.35.
If you do not specify the STDDEVIATIONS option, the default estimate for is

where N is the number of subgroups for which , and is the sample range of the observations , . . . , in the ith subgroup.

A subgroup range is included in the calculation only if . The unbiasing factor is defined so that, if the observations are normally distributed, the expected value of is . Thus, is the unweighted average of N unbiased estimates of . This method is described in the American Society for Testing and Materials (1976).
If you specify the STDDEVIATIONS option, the default estimate for is

where N is the number of subgroups for which , is the sample standard deviation of the ith subgroup

and

Here denotes the gamma function, and denotes the ith subgroup mean. A subgroup standard deviation is included in the calculation only if . If the observations are normally distributed, the expected value of is . Thus, is the unweighted average of N unbiased estimates of . This method is described in the American Society for Testing and Materials (1976).
If you do not specify the STDDEVIATIONS option and you specify SMETHOD=MVLUE, a minimum variance linear unbiased estimate (MVLUE) is computed for . Refer to Burr (1969, 1976) and Nelson (1989, 1994). The MVLUE is a weighted average of N unbiased estimates of of the form , and it is computed as

where

A subgroup range is included in the calculation only if , and N is the number of subgroups for which . The unbiasing factor is defined so that, if the observations are normally distributed, the expected value of is . The MVLUE assigns greater weight to estimates of from subgroups with larger sample sizes, and it is intended for situations where the subgroup sample sizes vary. If the subgroup sample sizes are constant, the MVLUE reduces to the default estimate.
If you specify the STDDEVIATIONS option and SMETHOD=MVLUE, a minimum variance linear unbiased estimate (MVLUE) is computed for . Refer to Burr (1969, 1976) and Nelson (1989, 1994). This estimate is a weighted average of N unbiased estimates of of the form , and it is computed as

where

A subgroup standard deviation is included in the calculation only if , and N is the number of subgroups for which . The MVLUE assigns greater weight to estimates of from subgroups with larger sample sizes, and it is intended for situations where the subgroup sample sizes vary. If the subgroup sample sizes are constant, the MVLUE reduces to the default estimate.
If you specify the STDDEVIATIONS option and SMETHOD=RMSDF, a weighted rootmeansquare estimate is computed for .

The weights are the degrees of freedom . A subgroup standard deviation is included in the calculation only if , and N is the number of subgroups for which .
If the unknown standard deviation is constant across subgroups, the rootmeansquare estimate is more efficient than the minimum variance linear unbiased estimate. However, in process control applications, it is generally not assumed that is constant, and if varies across subgroups, the rootmeansquare estimate tends to be more inflated than the MVLUE.
When each subgroup sample contains a single observation (), the process standard deviation is estimated as , where is the average of the moving ranges of consecutive measurements taken in pairs. This is the method used to estimate for individual measurements and moving range charts. See Methods for Estimating the Standard Deviation.