Can I use root-sum-square for this sort of problem?

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SUMMARY

The discussion centers on applying the root-sum-square equation to determine the uncertainty of the final length of an assembled rod made of n sections, each with a 3-sigma manufacturing uncertainty. The conclusion is that if the uncertainties are independent, the final 3-sigma uncertainty can be calculated using the formula u = √(∑u_i²), where u_i represents the uncertainties of individual sections. However, it is crucial to note that if the distribution of section lengths is not normal, the 3-sigma interpretation may not hold true, as non-normal distributions do not maintain additive properties.

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Homework Statement


This isn't actually a problem I came across in a textbook, but close enough. Let's say I have a rod made of n sections. Each section has a 3-simga manufacturing uncertainty of +/- some value normally distributed about the mean. What is the uncertainty of the final length of the assembled rod?

Homework Equations


Root-sum-square equation

The Attempt at a Solution


My thought is to simply plug in each of the uncertainties into the root-sum-square equation to get the final uncertainty on the length of the assembles rod. I'm not entirely confident in this as I only ever recall that equation being applied to measurements, although I see this problem as being analogous. Assuming, the equation does apply, does the final answer maintain the 3-sigma uncertainty on the length of the rod?
 
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If by '3-sigma uncertainty = x' you just mean that the std dev is x/3, and the lengths of the sections are assumed to be independent (which is a questionable assumption in a manufacturing process), then the answer is Yes.

However 'terms like '3-sigma uncertainty' are often understood to imply a certain percentile of the distribution. For a normal dist, 99.73% of points lie within 3-sigma of the mean. The 3-sigma figure obtained by summing the squares and then square rotting will not necessarily still be a 99.73 percentile if the distribution of section lengths is not normal, because most non-normal distributions are not additive - ie the distribution changes shape upon addition. So if a percentile/confidence level is implied and the distribution of section lengths is not normal, the answer is No.
 
ehilge said:

Homework Statement


This isn't actually a problem I came across in a textbook, but close enough. Let's say I have a rod made of n sections. Each section has a 3-simga manufacturing uncertainty of +/- some value normally distributed about the mean. What is the uncertainty of the final length of the assembled rod?

Homework Equations


Root-sum-square equation

The Attempt at a Solution


My thought is to simply plug in each of the uncertainties into the root-sum-square equation to get the final uncertainty on the length of the assembles rod. I'm not entirely confident in this as I only ever recall that equation being applied to measurements, although I see this problem as being analogous. Assuming, the equation does apply, does the final answer maintain the 3-sigma uncertainty on the length of the rod?

If section ##i## has 3-sigma uncertainty ##u_i##, then (presumably) it has 1-sigma uncertainty ##u_i/3##, so the standard deviation is ##\sigma_i = u_i/3##. If the uncertainties in the individual sections are "independent"---which you are claiming is the case---then the variance in the final length is
\sigma^2 = \sum_{i=1}^n \sigma_i^2 = \frac{1}{9} \sum_{i=1}^n u_i^2
Therefore, the standard deviation of the total length is
\sigma = \frac{1}{3} \sqrt{ \sum_{i=1}^n u_i^2}.
So, yes, indeed, the 3-sigma uncertainty in the total length is ##u = \sqrt{\sum_i u_i^2},## as you want.

For more on this, Google "variance of sum".
 

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