Users with engineer privileges can specify default preferences for all capability
analyses. These preferences are the initial settings for all capability analyses. To change
settings by measure, go to Specify capability settings for each measure.
Shared preferences
This global setting sets the shared preferences for each capability analysis. Select
Analysis Preferences
in the Engineering
portal, then navigate to .
Specify the tolerance and display preferences that are shared across the capability
analyses.
- Tolerance of K * σ
- Enter the width of the tolerance in number of
standard deviations (σ). By default, the tolerance is 6 standard
deviations wide (3 standard deviations on each side of the process
mean).
The K value represents the width of a two-sided tolerance. If you want to use a one-sided tolerance, enter a two-sided tolerance value that is twice that of the one-sided tolerance. For example, if you want to use a one-sided tolerance of 3σ, enter 6.
- Display performance metrics
- Select how you want to display the expected and observed
out-of-specification values.
- Parts per million: Display the values in parts per million (PPM).
- Percents: Display the values as percentages.
- Display capability metrics
- Select the measures of capability to display.
- Capability statistics (Cp, Pp): Calculate and display capability indices, such as Cp and Pp.
- Benchmark Z's (σ level): Calculate and display Z.bench values. The choice to use Z.bench often depends on company or industry practices.
Normal capability analyses
This global setting sets the estimation methods for each capability analysis. Select
Analysis Preferences
in the
Engineering
portal, then navigate to .
- Estimation method for within-subgroup standard deviation (subgroup size > 1)
- Select a method to estimate the
within–subgroup standard deviation when the subgroup size is larger than
1.
- Pooled standard deviation: The pooled standard deviation is the weighted average of subgroup variances, which gives larger subgroups more influence on the overall estimate. This method provides the most precise estimate of standard deviation when the process is in control.
- Rbar: Rbar is the average of the subgroup ranges. This method is a common estimate of the standard deviation and works best with subgroup sizes from 2 to 8.
- Sbar: Sbar is the average of the subgroup standard deviations. This method provides a more precise estimate of the standard deviation than Rbar, especially with subgroup sizes > 8.
- Estimation method for within-subgroup standard deviation (subgroup size = 1)
- Select a method to estimate the within–subgroup standard deviation when
the subgroup size equals 1.
- Average moving range: The average moving range is the average value of the moving range of two or more consecutive points. This method is commonly used when the subgroup size is 1.
- Median moving range: The median moving range is the median value of the moving range of two or more consecutive points. This method is best to use when data have extreme ranges that could influence the moving range.
- Square root of MSSD: The square root of MSSD is the square root of the mean of the squared differences between consecutive points. Use this method when you cannot reasonably assume that at least 2 consecutive points were collected under similar conditions.
- Length of moving range
- Enter the number of observations used to calculate the moving range. The length must be ≤ 100. The default length is 2 because consecutive values have the greatest chance of being alike.
- Use unbiasing constants
- Unbiasing constants reduce the bias that can occur when a parameter is
estimated from a small number of observations. As the number of
observations increases, unbiasing constants have less effect on the
calculated results.
Note
Often, the choice to use unbiasing constants depends on company policy or industry standards.
Between/within capability analyses
This global setting sets the estimation methods for each between-within capability analysis.
Select Analysis Preferences
in the
Engineering
portal, then navigate to .
- Estimation method for within-subgroup standard deviation
- Select a method to estimate the within–subgroup standard deviation.
- Pooled standard deviation: The pooled standard deviation is the weighted average of subgroup variances, which gives larger subgroups more influence on the overall estimate. This method provides the most precise estimate of standard deviation when the process is in control.
- Rbar: Rbar is the average of the subgroup ranges. This method is a common estimate of the standard deviation and works best with subgroup sizes from 2 to 8.
- Sbar: Sbar is the average of the subgroup standard deviations. This method provides a more precise estimate of the standard deviation than Rbar, especially with subgroup sizes > 8.
- Estimation method for between-subgroup standard deviation
- Select a method to estimate the between–subgroup standard deviation.
- Average moving range: The average moving range is the average value of the moving range of two or more consecutive points. This method is commonly used when the subgroup size is 1.
- Median moving range: The median moving range is the median value of the moving range of two or more consecutive points. This method is best to use when data have extreme ranges that could influence the moving range.
- Square root of MSSD: The square root of MSSD is the square root of the mean of the squared differences between consecutive points. Use this method when you cannot reasonably assume that at least 2 consecutive points were collected under similar conditions.
- Length of moving range
- Enter the number of observations used to calculate the moving range. The length must be ≤ 100. The default length is 2 because consecutive values have the greatest chance of being alike.
- Use unbiasing constants
- Unbiasing constants reduce the bias that can occur when a parameter is
estimated from a small number of observations. As the number of
observations increases, unbiasing constants have less effect on the
calculated results.
Note
Often, the choice to use unbiasing constants depends on company policy or industry standards.
