Unless otherwise specified, all Y, Process variables are analyzed using the assumption that they follow a normal distribution. Use the Distribution Options outline to assign other distributions or calculation methods to variables in the Y, Process list and to specify options related to nonnormal calculations.
• The available distributions are the Normal, Beta, Exponential, Gamma, Johnson, Lognormal, Mixture of 2 Normals, Mixture of 3 Normals, SHASH, and Weibull distributions. Except for Johnson distributions, maximum likelihood estimation is used to fit distributions. See Johnson Distribution Fit Method.
• The Best Fit option determines the best fit among the available distributions and applies this fit.
• The Nonparametric option fits a distribution using kernel density estimation.
For more options related to nonnormal fits, see Nonnormal Distribution Options.
1. Select a variable or variables in the Y, Process list.
2. Select a distribution from the Distribution list.
3. Select Set Process Distribution to assign that distribution to the selected variables.
The specified distribution appears in parentheses in the expression “&Dist()” to the right of the variable names in the Y, Process list.
Note: If you select a distribution other than Normal, you cannot assign a Subgroup ID column or a Historical Sigma. These selections are not supported by the methods used to calculate nonnormal capability indices. See Capability Indices for Nonnormal Distributions: Percentile and Z-Score Methods.
Nonnormal Capability Indices Method
Specifies the method used to compute capability indices for nonnormal distributions. See Capability Indices for Nonnormal Distributions: Percentile and Z-Score Methods.
Johnson Distribution Fit Method
Specifies the method used to find the best-fitting Johnson distribution. Before estimating the parameters, the best-fitting family of distributions is determined from among the Johnson Su, Sb, and Sl families. The procedure described in Slifker and Shapiro (1980) is used to find the best-fitting family.
Quantile Matching
The default method. It is more stable and faster than Maximum Likelihood. Quantile Matching Parameter estimates, assuming the best-fitting family, are obtained using a quantile-matching approach. See Slifker and Shapiro (1980).
Maximum Likelihood
Parameters for the best-fitting family are determined using maximum likelihood.
Distribution Comparison Criterion
(Available when a Best Fit Distribution is selected.) Specify the criterion that you want to use in determining a Best Fit. This criterion also determines the ordering of distributions in the Comparison Details report. See Order by Comparison Criterion.