Specify the options for Process Report

Six Sigma > Process Report > Options

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Use unbiasing constants when estimating
For subgroup size = 1, calculate short-term Z using
Use Box-Cox power transformation (W=Y^λ) with

Use unbiasing constants when estimating

Specify whether to use unbiasing constants when you estimate short-term and long-term standard deviations.

Unbiasing constants vary with sample size and are equal to the ratio of the expected value of the standard deviation estimate for samples of a given sample size to the standard deviation of the population. The default is to use unbiasing constants when you estimate short-term standard deviation, but not when you estimate long-term standard deviation.

For more information, go to Calculations for process statistics and capability values for Process Report.

Short-term standard deviation: Use unbiasing constants to calculate the short-term standard deviation.
Long-term standard deviation: Use unbiasing constants to calculate the long-term standard deviation.

For subgroup size = 1, calculate short-term Z using

Specify how to calculate the short-term standard deviation for data that are not collected in rational subgroups (that is, when subgroup size = 1). With subgroup size = 1, you cannot calculate the short-term standard deviation in the usual way.

Moving range to estimate short-term standard deviation: Use the moving range method.
Long-term Z + a σ shift of: Use an estimate of long-term Z standard deviation. Select this option when you can't assume that units that are produced consecutively are less variable than units that are that are produced non-consecutively. You can specify a value that is between 0 and 6 for the assumed sigma shift.

Use Box-Cox power transformation (W=Y^λ) with

Use the Box-Cox power transformation when your data are very skewed or when the within-subgroup variations are unstable. The transformation takes the original data to the power λ, unless λ = 0, in which case it takes the natural log.

Optimal λ: Use the optimal lambda, which should produce the best fitting transformation. Minitab rounds the optimal lambda to 0.5 or the nearest integer.
Other [a value between -5 and 5]: Use a specified value for lambda. Other common transformations are square (λ = 2), inverse square root (λ = −0.5), and inverse (λ = −1). In most cases, you should not use a value outside the range of −2 and 2.