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Monte Carlo Analysis

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Monte Carlo Statistical Analysis
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The Monte Carlo method is a mathematical method used for problem solving through the generation of random numbers and then observing a fraction of these numbers and the properties they obey. It is useful in obtaining numerical solutions to problems that are too complicated for analytical solutions. It is a form of probability used to understand the impact of risk and uncertainty in various areas such as financial and cost forecasting. It involves computation of the likelihood of given events occurring or not occurring, without taking into account the interaction of the elements involved in influencing the occurrence. The mathematical method was invented by Stanislaw Ulam in 1946 and named by Nicholas Metropolis after a classy gambling resort in Monaco, where a relative of Ulam frequently gambled [ (Fishman, 1996) ].
Concepts of the Monte Carlo method
Uncertainty
Being a forecasting model, there are assumptions that need to be made due to the uncertainty of various factors. One therefore needs to be able to make estimations of the expected results as they cannot predict with certainty what the end value will be. Important factors such as historical data and past experiences in the field can be helpful in making an accurate estimate.
Estimation
In some cases, estimation may be possible but in others it is not. In situations where estimation is possible, it is wise to use a wide range of possible values instead of single values. This ensures that a more realistic outcome is predicted. The actual outcome is likely to lie in the range.
It is important to note that this method only assists in forecasting how the actual results are likely to look like, not the actual results of the analysis.
Experiment
Each calculation using this method is a numerical experiment. Therefore, it is likely to have...

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