Weighted average that takes error into account?
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My data consists of values with an error (from non-linear curve fitting), for example:
value#1=2.3322e-10 error#1=8.7707e-13
value#2=2.3257e-10 error#2=1.2317e-12
What would be the most appropriate way to produce an average of the values that would take the error of each value into account?
For example, value#2 has a larger error so it would have less "weight" in the total average.
Thanks.
6 commentaires
Walter Roberson
le 9 Nov 2019
Then it is really up to you. There are circumstances where the weighting should be inverse linear. There are circumstances where it should be . There are circumstances where the relationship should be exponential.
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Star Strider
le 9 Nov 2019
It depends on what you mean by ‘error’. In regressions, data are commonly weighted by the inverse of the variance, so the larger the variance, the smaller the weight.
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