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**Discrepancy from Fits Performed by Other Software**

**Applies to:** @RISK 5.x and newer, Professional and Industrial Editions

When I fit my data in @RISK, I get a very different result from the ________ software. Maybe @RISK fails to converge at all, or maybe it converges on a fit but the parameters are very different. Is there some setting I need to change?

Probably there is. Specifically, if the process that generated the data has a natural lower bound, you should specify that lower bound on the *Distributions to Fit* tab of the fitting dialog.

Why is this necessary? Many software packages assume a lower bound of zero for distributions that don't have a left-hand tail. Other packages, including @RISK, take a more general approach and make the lower bound subject to fitting also, as a shift factor. This allows, for instance, a distribution shaped like a log-normal but offset to left or right, if that matches the data best. But sometimes that is actually too much freedom, and @RISK fails to converge on a fit. (In general, "convergence failed" means that the numerical process of homing in on an answer for the MLE got stuck in a loop and couldn't finish.)

When the data have a natural lower bound, and you specify that lower bound to @RISK, it can do a better job of fitting more efficiently. Specifying the lower bound may even make the difference between "convergence failed" and a successful fit, as for example in some Weibull distributions with shape parameter less than 1.

On the *Distributions to Fit* tab of the fitting dialog, "bounded but unknown" restricts the fit to distributions that don't have left-hand tails, but it doesn't affect the fitting algorithm for those distributions. But when you specify a specific lower bound, then @RISK uses that as a fixed shift factor, and the mathematics of doing the fit are simplified.

Last edited: 2015-06-01

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