Use the Continuous or Discrete Fit options to fit a distribution to a continuous or discrete variable.
A curve is overlaid on the histogram, and a Parameter Estimates report is added to the report window. A red triangle menu contains additional options. See Fit Distribution Options.
Note: The Life Distribution platform also contains options for distribution fitting that might use different parameterizations and allow for censoring. See the Quality and Process Methods book.
Use the Continuous Fit options to fit the following distributions to a continuous variable.
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The Normal Mixtures distribution fits a mixture of normal distributions. This flexible distribution is capable of fitting multimodal data. You can also fit two or more distributions by selecting the Normal 2 Mixture, Normal 3 Mixture, or Other options.

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The Smooth Curve distribution... A smooth curve is fit using nonparametric density estimation (kernel density estimation). The smooth curve is overlaid on the histogram and a slider appears beneath the plot. Control the amount of smoothing by changing the kernel standard deviation with the slider. The initial Kernel Std estimate is formed by summing the normal densities of the kernel standard deviation located at each data point.

The All option fits all applicable continuous distributions to a variable. The Compare Distributions report contains statistics about each fitted distribution. Use the check boxes to show or hide a fit report and overlay curve for the selected distribution. By default, the best fit distribution is selected.
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The Diagnostic Plot option creates a quantile or a probability plot. Depending on the fitted distribution, the plot is one of four formats.







Descriptions of the Diagnostic Plot Options describes the options in the red triangle menu next to Diagnostic Plot.
Draws Lilliefors 95% confidence limits for the Normal Quantile plot, and 95% equal precision bands with a = 0.001 and b = 0.99 for all other quantile plots (Meeker and Escobar (1998)).


The Goodness of Fit option computes the goodness of fit test for the fitted distribution. The goodness of fit tests are not Chisquare tests, but are EDF (Empirical Distribution Function) tests. EDF tests offer advantages over the Chisquare tests, including improved power and invariance with respect to histogram midpoints.
The Spec Limits option launches a window requesting specification limits and target, and then computes generalizations of the standard capability indices. This is done using the fact that for the normal distribution, 3σ is both the distance from the lower 0.135 percentile to median (or mean) and the distance from the median (or mean) to the upper 99.865 percentile. These percentiles are estimated from the fitted distribution, and the appropriate percentiletomedian distances are substituted for 3σ in the standard formulas.