Capability settings for each measure

You can specify the capability analysis settings for each measure within the process for a product.

This setting applies to each station of the measure and overrides the capability settings for each station.

Go to the Components page and select a product. Then select the process flow step of your product.
Open the process step and go to the Process Summary section.
Select Additional Settings to access the capability settings for each measure.

Note

This setting applies only to the capability analysis for this measure. Changing transformation settings here will not affect the analyses preferences. To change the preferences for all capability analyses, go to Capability analysis preferences.

Use the default normal distribution if your data follow a normal distribution. If you have nonnormal data, you can either transform the data to fit a normal distribution or select a nonnormal distribution that fits your data.

Normal distribution

Select to perform a normal capability analysis.

Normal distribution with Box-Cox transformation

Use the Box-Cox transformation if your nonnormal data are all positive (> 0) and you want to obtain estimates of within-subgroup (potential) capability as well as overall capability.

Select the lambda (λ) value to transform the data.

Optimal λ: Use the optimal lambda, which should produce the best fitting transformation.
λ = 0 (ln): Use the natural log of your data.
λ = 0.5 (square root): Use the square root of your data.
Specify λ: 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.

Fit distribution

Select the distribution that best fits your data and perform a nonnormal capability analysis.

Most often, it is best to use engineering and historical knowledge of your process to identify a distribution that fits your process data. However, Minitab^® Statistical Software has many tools, such as Individual Distribution Identification, that can help you assess the fit of various distributions.