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

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In summary, the question asks about the uncertainty of the final length of a rod made of n sections, each with a 3-sigma manufacturing uncertainty. The suggested solution is to use the root-sum-square equation and assuming independent uncertainties, the final uncertainty is equal to the square root of the sum of squares of the individual section uncertainties. However, if the distribution of section lengths is not normal, this may not hold true.
  • #1
ehilge
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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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  • #2
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.
 
  • #3
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
[tex] \sigma^2 = \sum_{i=1}^n \sigma_i^2 = \frac{1}{9} \sum_{i=1}^n u_i^2 [/tex]
Therefore, the standard deviation of the total length is
[tex] \sigma = \frac{1}{3} \sqrt{ \sum_{i=1}^n u_i^2}. [/tex]
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".
 

1. What is root-sum-square and when should it be used?

Root-sum-square, also known as RSS, is a mathematical formula used to calculate the combined uncertainty of multiple measurements. It should be used when dealing with random and independent errors, such as in measurements or data sets.

2. Can root-sum-square be used for all types of uncertainties?

No, root-sum-square should only be used for random and independent uncertainties. It is not appropriate for systematic uncertainties, which are consistent and predictable. For systematic uncertainties, other methods such as error propagation should be used.

3. How do I calculate root-sum-square?

To calculate root-sum-square, you first square each individual uncertainty, then add all of the squared uncertainties together. Finally, take the square root of the sum to get the combined uncertainty. The formula is: RSS = √(Δx1² + Δx2² + ... + Δxn²).

4. Is root-sum-square the same as standard deviation?

No, root-sum-square and standard deviation are not the same. Standard deviation is a measure of the spread or variability of a data set, while root-sum-square is a method for combining uncertainties. However, for a data set with only random and independent uncertainties, the root-sum-square value will be equivalent to the standard deviation.

5. Can root-sum-square be used for large data sets?

Yes, root-sum-square can be used for any number of uncertainties, making it suitable for large data sets. However, it is important to ensure that all uncertainties are random and independent before using the RSS formula. If there are any systematic uncertainties, other methods should be used to calculate the combined uncertainty.

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