Standard deviation of aggregated data

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To calculate the standard deviation of aggregated data across different intervals, one can use the weighted mean and combine the second moments derived from the means and standard deviations of the individual intervals. The process involves calculating the weighted mean first, then combining the second moments using the same weighting method. It is important to account for the varying lengths of time for each interval when aggregating the data. An unbiased estimator for the standard deviation should be used, as referenced from statistical resources. This approach ensures accurate representation of the aggregated data's variability.
KThy
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This might be embarrasingly easy or impossible; I've been a computer programmer for too long since my statistics classes to tell for sure :blushing:

I have a set of records with the following data for each record: interval, mean speed, standard deviation of speed, number of observations. Exempli gratia:

Code:
1, 77.2, 1.75, 10
2, 75.9, 2.05, 12

Now, if I want to aggregate the data to get mean speed and standard deviation over two or more intervals (1 and 2 above), calculating the weighted mean is no problem but how - if possible - do I calculate the standard deviation for the aggregated intervals?
 
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Given the std. dev. and mean, you can easily get the second moment. Combine the second moments by the same procedure you used for the means. Finally calculate the std. dev. from the weighted mean and weighted second moment.
 
mathman said:
Combine the second moments by the same procedure you used for the means. Finally calculate the std. dev. from the weighted mean and weighted second moment.

Right, of course. As I said, embarrasingly simple - thanks!
 
I recently encountered a similar problem at work.

However, for me the problem is that some of the intervals used for measuring average speed are different lengths of time. I believe the problem is the same?

Since we consider the data to be a sample, we want the unbiased estimator for the standard deviation which takes on the following form given on wikipedia website (i derived it alone to make sure).


http://en.wikipedia.org/wiki/Mean_square_weighted_deviation

Cheers.
 
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