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

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The discussion centers on calculating the uncertainty of the final length of a rod composed of multiple sections, each with a 3-sigma manufacturing uncertainty. The root-sum-square equation is proposed as a method to determine the overall uncertainty, assuming the uncertainties of the sections are independent. It is clarified that if the individual section uncertainties are normally distributed, the final uncertainty will maintain the 3-sigma confidence level. However, if the distribution of section lengths is not normal, the additive nature of uncertainties may not hold, potentially altering the confidence level. The conclusion affirms that the root-sum-square method is valid under the assumption of independence and normal distribution.
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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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