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# As we've previously seen, equations describing situations often contain uncertain parameters, that is, parameters that aren't...

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As we've previously seen, equations describing situations often contain uncertain parameters, that is, parameters that aren't necessarily a single value but instead are associated with a probability distribution function. When more than one of the variables is unknown, the outcome is difficult to visualize. A common way to overcome this difficulty is to simulate the scenario many times and count the number of times different ranges of outcomes occur. One such popular simulation is called a Monte Carlo Simulation. In this problem-solving exercise you will develop a program that will perform a Monte Carlo simulation on a simple profit function. Consider the following total profit function: P1 =nP Where Pr is the total profit, nt is the number of vehicles sold and P, is the profit per vehicle. PARTA Compute 5 iterations of a Monte Carlo simulation given the following information: n follows a uniform distribution with minimum of 1 and maximum 10 P follows a normal distribution with a mean of S7000 and a standard deviation of $1750 Number of bins: 10 Recall that for all practical purposes we will use 3 std. deviations from the mean as the maximum value for parameters following a normal distribution. Obviously, 5 iterations are not very many. In fact, typically you would simulate 10,000 iterations or so to view meaningful results but I figured that I'd give you a break . If you'd like to compute 10,000 iterations by hand for extra credit, go ahead... ii.) What are the ranges for the 10 bins? Fill in the table below: Iteration 3 Iteration + Iteration 5 Parameter Iteration 1 Iteration 2 38 S3750$4500 S7000 S6700 S7750 S Range

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