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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 Fitting_ExtValue illustrates how to fit minimal data to an ExtremeValue distribution.
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The theory underpinning the distribution applies to the particular problem;
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;
The distribution matches the observed data very well indeed; or
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 AnchorUnivariate Univariate
Univariate | |
Univariate |
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| Continuous | Discrete |
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Unbounded |
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Left bounded | ||
Right bounded |
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Left and right bounded | Revised PERT |
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