Standard deviation of series of trials

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SUMMARY

The discussion focuses on calculating the standard deviation (SD) for two methods of sawing planks, both having an expected value (EV) of 1m and a standard deviation of 0.005m. The first method involves sawing all planks at once, while the second method involves sawing them individually. The formula for the standard deviation of the sum of independent random variables is highlighted: SD(A + B + ... + Z) = √(SD(A)² + SD(B)² + ... + SD(Z)²). Additionally, it is established that the variance is additive, allowing for the derivation of SD(10X) by setting all variables equal.

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Gauss M.D.
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Say we want to saw ten planks and we have two methods available - one is sawing them all at once, ensuring they're all exactly uniform length. The other method is sawing them individually. Either method has EV of 1m and a standard deviation of 0.005m. I want to find the standard deviation of both methods.

In other words, given a random variable X, I guess what we're trying to figure out is SD(10X) and SD(X1 + X2 + ... + X10).

I'm not sure how to calculate the second one. Anyone want to give me a push? :S
 
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[tex]\operatorname{SD}(A + B + \cdots + Z) = \sqrt{\operatorname{SD}(A)^2 + \operatorname{SD}(B)^2 + \cdots + \operatorname{SD}(Z)^2}[/tex]

What this basically says is that the variance Var(X) = SD(X)² is linear:
[tex]\operatorname{Var}(A + B + \cdots + Z) = \operatorname{Var}(A) + \operatorname{Var}(B) + \cdots + \operatorname{Var}(Z)[/tex]

Also note that by setting A = B = ... = X you can actually derive the result for SD(10X).
 
Thanks a ton!
 

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