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One can also build logic into the model that rejects nonsensical values. For example, using the IF function: A2:=IF(A1<0,ERR(),0) only allows values into cell A2 from cell A1 that are >=0 and produces an error in cell A2 otherwise. Crystal Ball eliminates the error values form from its analysis of the simulation results.

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You will notice from the table below that only one of all the distributions is bounded on the right extreme; the MinimumExtreme distribution. If you need any of the other distributions to be right-bounded for some reason, you can also simply invert a left bounded distribution. For example: =-Weibull(0,5,2) produces a left-skewed (e.g. right-bounded) distribution with an unbounded minimum and a maximum of 0; =10-Gamma(0, 1.5, 2) produces a left-skewed distribution with an unbounded minimum and a maximum of 10, as shown in the figures below. Also, the model Image Removed Image Added Fitting_ExtValue illustrates how to fit minimal data to an ExtremeValue distribution.

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1. The theory underpinning the distribution applies to the particular problem;

2. It is generally accepted that a particular distribution has proven to be very accurate for modeling a specific variable without actually having any theory to support the observation;

3. The distribution matches the observed data very well indeed; or

4. One wishes to use a distribution that has a long tail extending beyond the observed minimum or maximum. These issues are discussed in more detail in the optional module on fitting distributions to data.

Univariate and multivariate distributions

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Univariate Univariate

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