Beginning with release 6.0, @RISK offers parametric bootstrapping. Compared to nonparametric bootstrapping, parametric bootstrapping requires less resampling and is more robust with smaller data sets. You can get parameter confidence intervals as well as goodness-of-fit statistics.
In the bootstrapping process, @RISK takes each fitted distribution and generates a large number of new sample data sets from it, each with the same size as the original data set. It then refits these new data sets and tabulates information about each of the resampled fits. @RISK takes a conservative approach. If it is unable to fit a distribution to even one of the new sample data sets that it generated (meaning that the parameters of that distribution did not converge for that new data set), then @RISK considers that the bootstrap has failed for that distribution.
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You can embed overlay graphs in your worksheet with the RiskResultsGraph( ) function. The graphs will be updated automatically when you run a simulation, and the latest versions will be stored with your workbook even if you don't store simulation results. Please download the attached example (about 130 KB).
Upgrade to the latest @RISK if you have @RISK 6.0. The accelerated engine introduced in @RISK 6.1 makes many simulations with projects run dramatically faster, and there were further improvements in later versions.
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