Aggregating Standard Deviations HELP

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In summary, the conversation is about a problem with aggregating centers of gravity and the inertia tensor for multiple elements with their own mean and uncertainty. The speaker has been using a Monte Carlo method to generate aggregate statistics, but it is time-consuming and needs to be rerun each time an element is changed. They are looking for a mathematical process to update the mean and standard deviation for an element and recalculate the aggregated values. The speaker has set up a spreadsheet and has had success with simpler properties, but is struggling with complex properties such as the aggregate center of gravity standard deviation. They are asking for help and wondering if the issue can be solved mathematically.
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Czar78
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Aggregating Standard Deviations... HELP!

Can anyone help with this stats problem?

I'm working on a problem where I'm aggregating centers of gravity and the inertia tensor for multiple elements – each element has it’s own mean and uncertainty.

What I’ve been doing is using a Monte Carlo method to generate aggregate statistics for various assembly levels and it works quite well, but is incredibly time consuming. When an element is changed, it necessitates rerunning the MonteCarlo for each assembly which includes it.

What I would like to do is implement a mathematical process which will allow me to update the current value for mean & standard deviation (SD) for that element and recalculate the aggregated values.

I’ve set up a spreadsheet to help me and what I have is the results of a MonteCarlo for an aggregated system. I then broke out several sub-components and retrieved their individual mean and SDs. So far I’ve been unsuccessful for the more complex properties. When I try to combine the SDs for the various elements, I can’t always figure out how to combine them to get something close to the total values from the MonteCarlo. Things such as the calculated total weight & SD come out easily since the total weight is just the sum of the elemental weights and the aggregate weight SD is just the RSS of the elemental SDs and my calculations using RSS SD are close enough to the aggregate SD from the MonteCarlo to call it a success. Where I hit a brick wall is trying to figure out the aggregate center of gravity SD – and I haven’t even attempted the inertia tensor yet. The equation for the aggregate CG is the (SUM of each element Weight * element CG) divided by the Total Weight. The mean is relatively easy to calculate, but for the life of me I can not get the SDs to equate. Any ideas?
 
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I don’t know if this is something that can even be done mathematically, but my gut instinct tells me it should be. Thanks in advance for any help you can provide!
 

1. What does it mean to aggregate standard deviations?

Aggregating standard deviations refers to combining multiple standard deviations into one overall measure. This is often done to summarize the variability or spread of a dataset.

2. Why is aggregating standard deviations important?

Aggregating standard deviations can help to simplify and summarize a large amount of data, making it easier to interpret and compare across different groups or time periods. It can also provide a more accurate estimate of the overall variability in a dataset.

3. How do you calculate an aggregated standard deviation?

To calculate an aggregated standard deviation, you would first calculate the standard deviation for each individual dataset or group. Then, you would use a specific formula to combine these individual standard deviations into one overall measure.

4. What are some common methods for aggregating standard deviations?

Some common methods for aggregating standard deviations include the root mean square method, the weighted mean variance method, and the pooled variance method. Each method may be more appropriate for different types of data or research questions.

5. Are there any limitations to aggregating standard deviations?

Yes, there are some limitations to aggregating standard deviations. For example, it assumes that the individual datasets or groups are normally distributed and have similar variances. If these assumptions are not met, then the aggregated standard deviation may not accurately represent the variability in the data.

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