A model for selecting a vendor on the basis of quality and cost.
Resources
According to ESPN Magazine, the NCAA tournament is one of the most wagered-on events in sports. Veteran bookmakers estimate bets range from $12 billion to $26 billion (roughly the GDP of Honduras). If you are looking for that edge to improve your office pool odds, take a look at this @RISK model of the 2015 NCAA tournament, built by Andrew Pulvermacher of Nighthawk Intelligence. Using publicly available data, you will be able to tame March Madness by managing uncertainty.
Most PGA golf tournaments are played over 4 rounds, from Thursday to Sunday. If you follow the PGA, you have probably noticed that one or more golfers shoot really low scores, such as 7 or 8 under par, on Thursday. This might lead you to guess that the best score after all 4 rounds will be somewhere around 28 to 32 under par. However, this is almost never the case. The winning score is almost never nearly this low, as this simulation model illustrates._x000D_
The World Cup features 32 international soccer teams that are allocated into eight groups of four. Within each group, all four teams play against each other. The top two teams from each group advance to a group of 16 teams. A win at this level accounts for 3 points, a defeat for 0 points, and a draw accounts for only one point to both teams. An astounding number of results are possible at this stage. Once the group is winnowed down to 16 teams, a bracket-style tournament ensues. This simulation details how teams of high, intermediate, and low ranking will fare in in the 2014 World Cup tournament using @RISK and PrecisionTree. Read more about this model here.
A model for simulating a best-of-7 game baseball World Series.
Click here to see a video of this example.
A model for simulating the NCAA Basketball "March Madness" Tournament
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.
