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SUBMIT YOUR IDEAI have never used this tool before so it took some playing around and I don't know if it was the best approach but I got a solid solution. Cool stuff!
I used the Central Limit Theorem (CLT), the uniform random number function RAND(), an iterative macro, and some trial and error.
The CLT proposes that the mean of a sample of data from any distribution is itself distributed normally regardless of the distribution of the underlying data.
In other words, if you take a sample, say 1,000 values, of any data population, then take the mean of that sample, and repeat this many times, then the distribution of those means will be normal.
1) Randomly generate 1,000 numbers with low and high values that will result in an average (over those 1,000 numbers) between 50 and 90. This was the hard part.
In a formula tool >>> Average(70 - 850 * RAND(), 70 + 850 * RAND())
2) Iterate 1,000 of these 1,000 observation samples and take the mean from each, giving you 1,000 means.
3) You're done! Just build a histogram of the means ...
Simulation Achieved !
Solution:
Explanation: