Finessing Monte Carlo Simulation Input Distribution Selection with @RISK

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Presented by

Steve Van Drew

About this talk

For many spreadsheet-based Monte Carlo simulation modelers, making input distribution selection decisions can be both time consuming and angst inducing. Whether basing these decisions on goodness of fit tests when fortunate enough to have relevant data, or on insight gained from a carefully chosen expert’s judgment, the modeler often feels compelled to choose a seemingly unsuitable distribution. Fortunately for @RISK users several lesser-known features exist that allow the modeler to “finesse” their selection.

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