A simulation model for seeing how important a given lead is after early rounds of the tournament.
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A simulation model for seeing how important a given lead is after early rounds of the tournament.
This model illustrates the RiskResultsGraph function for creating non-interactive graphs of specified inputs or outputs when a simulation is run.
The RiskSimtable feature can be used to run multiple simulations to test the sensitivity of the model, for example to changes in the parameters of a distribution. This model shows how the RiskSimtable feature is used to test the sensitivity of the distribution of profit to changes in the standard deviation of the revenues.
The various @RISK functions in this series of examples generate families of distributions: the normal family, the binomial family, and so on. Each family is characterized by one or more parameters, and each parameter is generally labeled a location, scale, or shape parameter. The purpose of the current file is to explain these terms.
'@RISK doesn't have a RiskMultinomial function but this example shows you how to generate such distribution by using the RiskBinomial function repeatedly.
The RiskBeta functions are a set of flexible functions for generating an uncertain quantity known to be between given minimum and maximum values. This example explains how to use three different versions of these functions which are available in @RISK.
This example explains how to use the Binomial and Bernoulli distributions.
The RiskCumul function provides a high degree of flexibility in describing the distribution of a continuous uncertain quantity. This example explains how to use it as an alternative to the RiskGeneral and RiskHistogram functions.
This example explains how to use the RiskDiscrete function which is used when you want to model an uncertain quantity with a finite -- that is, discrete -- set of possible values and corresponding probabilities. It is very general in that there can be any number of possible values, they can be any values (positive or negative, equally spaced or not), and the probabilities can be any positive values that sum to 1. This means that the distribution can have any shape: symmetric, skewed, or even multi-modal.
This example explains how to use the RiskDUniform and RiskIntUniform functions to generate equally likely integer values.
This example explains how to use the RiskExtValue and RiskExtValueMin functions which are widely used in the Extreme Value Theory.
