I Experimental Data - Error in slope

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The discussion revolves around calculating the modulus of elasticity and its associated error from tensile test data. The user plans to use linear regression to obtain slope values and their standard deviations for five specimens. There is a suggestion to use a weighted average to combine the errors, but the appropriateness of this method depends on the relationship between the slopes. It is noted that if the specimens are comparable, averaging the errors without weighting may be acceptable. Overall, combining uncertainties in this manner is common in scientific experiments, but careful judgment is necessary to account for potential outliers.
raniero
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I have conducted a tensile test on five specimens. I intend to do a linear regression for every set of data and get a value for the slope (modulus of elasticity) and its error by finding the standard deviation (using LINEST function on excel) of the slope.

I will now end up with 5 slope values and 5 errors. I will then find the average of 5 slopes, but, how can I find the 'average' of the 5 errors to finally obtain one value for the modulus of elasticity and one error?

Thanks in advance
 
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BvU said:
Google weighted average. For the error you have this

Thanks for your reply. Is this method to combine uncertainties typically used in science experiments ?
 
You could do some kind of weighted average, but if you think the slopes are related in such a way that the ""average slope" makes sense, then this really calls for some kind of multilevel model. I recommend looking up (or finding someone in your department familiar with) mixed-effect model.
 
raniero said:
Thanks for your reply. Is this method to combine uncertainties typically used in science experiments ?
Yes. But Number9 has a good point. If your experiments are comparable and your specimens are unsuspect (*), there is no good reason to assume the errors should come out significantly different. Some judgement comes in. Mechanically following the algorithms may not be the best way to go (*) suppose one test strip has a mechanical defect or a slightly different composition -- that would produce an outlier and you simple want to ignore that and average the others - maybe even without weighting.
 
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