Determining std dev with errors in measurements

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To compute the mean and standard deviation of a sample of measurements that includes inherent errors, it is essential to incorporate the error values into the calculations. The mean can be calculated using a weighted average, where each measurement is weighted by the inverse of its error. This approach gives more influence to measurements with smaller errors. For the standard deviation, a similar weighted method can be applied, considering the errors in the calculation of deviations from the mean. Assuming the errors are uniformly distributed, adjustments may be necessary if the error distribution differs. Visualizing the data as horizontal bars can help conceptualize how the errors affect the mean and standard deviation, akin to finding a "center of gravity" for the weighted measurements. This method ensures that both the measurements and their associated errors are appropriately accounted for in the statistical analysis.
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I have the following sample of measurements "V" and each has error "E" incorporated in the measurement. If I want to take mean of the sample how should I proceed. I figure that I can compute "sigma" by taking square root of the (sum of square of deviations from the mean divided by the number of sample-1). but that sigma will not incorporate the "E" errors inherent in the "V"

V E

6 1
5 0.2
4 3
6 2

I can compute mean and stddev suing Matlab, but how do I incorporate "E" in the computation.

thanks,
 
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Hint:
Assuming your error is uniformly distributed, if not adjust accordingly.
Suppose you have a horizontal bar one unit high and
centered at 6 and 1 unit wide
centered at 5 and 0.2 unit wide
centered at 4 and 3 unit wide
centered at 6 and 2 unit wide.
Does that correctly represent your data? Or do they need to be other than one unit high?
Can you perhaps think "center of gravity" to then calculate your mean?
Can you perhaps think of another calculation to then calculate your standard deviation?
 
thanks for your help !
 

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