18. Continuing the previous problem, assume, as inProblem 11, that the damage amount is normallydistributed with mean $3000 and standard deviation$750. Run @RISK with 5000 iterations to simulatethe amount you pay for damage. Compare your resultswith those in the previous problem. Does it appear tomatter whether you assume a triangular distribution ora normal distribution for damage amounts? Why isn’tthis a totally fair comparison? (Hint: Use @RISK’sDefine Distributions tool to find the standard deviationfor the triangular distribution.)
Continuous Probability Distributions
Probability distributions are of two types, which are continuous probability distributions and discrete probability distributions. A continuous probability distribution contains an infinite number of values. For example, if time is infinite: you could count from 0 to a trillion seconds, billion seconds, so on indefinitely. A discrete probability distribution consists of only a countable set of possible values.
Normal Distribution
Suppose we had to design a bathroom weighing scale, how would we decide what should be the range of the weighing machine? Would we take the highest recorded human weight in history and use that as the upper limit for our weighing scale? This may not be a great idea as the sensitivity of the scale would get reduced if the range is too large. At the same time, if we keep the upper limit too low, it may not be usable for a large percentage of the population!
18. Continuing the previous problem, assume, as in
Problem 11, that the damage amount is
distributed
$750. Run @RISK with 5000 iterations to simulate
the amount you pay for damage. Compare your results
with those in the previous problem. Does it appear tomatter whether you assume a triangular distribution or
a normal distribution for damage amounts? Why isn’t
this a totally fair comparison? (Hint: Use @RISK’s
Define Distributions tool to find the standard deviation
for the triangular distribution.)
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