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Nov 27, 2018
12:00 PM

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Nov 27, 2018
12:00 PM

Determine Relative Contribution of Sources of Variance | Monte Carlo

Hello,

I have a working simulation worksheet that runs a Monte Carlo analysis on a function. With the results, I can bin the simulations for the input variables as well as for the output. I am struggling though, to find references on how I might "tease" out the relative contribution of sources of variance.

Are there any suggestions for using the input function, along with known means and standard deviations of the input variables to establish these values?

Is there an analytical way to look at the result of the Monte Carlo simulations and determine relative variance?

Sorry for the abstraction in advance...I attached my Monte Carlo simulation up to this point, but couldn't even begin to try and "mock up" what I am asking for help with.

Solved! Go to Solution.

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Nov 28, 2018
05:43 PM

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Nov 28, 2018
05:43 PM

A little experimenting:

That's pretty close to the result of your MonteCarlo run's stdev (4300 N)...But no proof!

Success!

Luc

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Nov 27, 2018
01:04 PM

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Nov 27, 2018
01:04 PM

I can't open your file (5.0?). But here's a sheet I've posted before that makes a tornado graph to compare contributions.

Nov 28, 2018
05:04 PM

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Nov 28, 2018
05:04 PM

You might want to look here

https://en.wikipedia.org/wiki/Variance#Product_of_independent_variables

for information on symbolically/analytically determining the variance of your function result from the variances (and expected values) of its input variables.

Success!

Luc

In probability theory and statistics, variance is the expected value of the squared deviation from the mean of a random variable. The standard deviation (SD) is obtained as the square root of the variance. Variance is a measure of dispersion, meaning it is a measure of how far a set of numbers is

Nov 28, 2018
05:43 PM

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Nov 28, 2018
05:43 PM

A little experimenting:

That's pretty close to the result of your MonteCarlo run's stdev (4300 N)...But no proof!

Success!

Luc