You can fit distributions to forecast charts, a feature that is similar to distribution fitting for assumptions (described in Fitting Distributions to Data).
You can fit distributions to forecasts two ways:
You can choose Forecast, then Fit Probability Distribution in the forecast chart menubar to do a quick fit with the default or currently selected distributions and ranking method. You can also use this command to switch off distribution fitting that is set with either the Forecast menu or Preferences menu.
You can choose Preferences, then Forecast, then Forecast Window in the forecast chart menubar to specify particular distributions and to choose a fit ranking method. Then, you can also change the fit options or use Apply To to set these preferences for other forecasts.
To fit a probability distribution to a forecast chart using the Preferences, then Forecast command:
In the forecast chart menubar, choose Preferences, then Forecast.
In the Forecast Window tab of the Forecast Preferences dialog, check Fit A Probability Distribution To The Forecast and click Fit Options.
Specify which distributions to fit:
AutoSelect performs a basic analysis of the data to choose a distribution fitting option and ranking method. If the data includes only integers, fitting to all discrete distributions (with the exception of Yes-No) is completed using the Chi-square ranking statistic choice.
All Continuous fits the data to all of the built-in continuous distributions (these distributions are displayed as solid shapes on the Distribution Gallery).
All Discrete fits to all discrete distributions except Yes-No.
Choose displays another dialog from which you can select a subset of the distributions to include in the fitting.
Specify how the distributions should be ranked. In ranking the distributions, you can use one of three standard goodness-of-fit tests:
Anderson-Darling — This method closely resembles the Kolmogorov-Smirnov method, except that it weights the differences between the two distributions at their tails greater than at their mid-ranges. This weighting of the tails helps to correct the Kolmogorov-Smirnov method’s tendency to over-emphasize discrepancies in the central region.
Chi-Square — This test is the oldest and most common of the goodness-of-fit tests. It gauges the general accuracy of the fit. The test breaks down the distribution into areas of equal probability and compares the data points within each area to the number of expected data points.
Kolmogorov-Smirnov — The result of this test is essentially the largest vertical distance between the two cumulative distributions.
The first setting, AutoSelect, enables Crystal Ball to choose the ranking statistic. If all data values are integers, Chi-square is chosen.
If you know location, shape, or other parameter values that might help create a more accurate fit with certain distributions, check Lock Parameters and enter appropriate values in the Lock Parameters dialog (Locking Parameters When Fitting Distributions).
By default, values for all appropriate ranking statistics are calculated but only values for the selected ranking statistic are displayed in Goodness Of Fit view. To show values for all three statistics, check Show All Goodness-of-fit Statistics at the bottom of the Distribution Options panel.
During a simulation, Crystal Ball disables distribution fitting on forecast charts and overlay charts after 1,000 trials and until the simulation stops to enhance performance. A final fit is performed at end of the simulation.