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Calculating the error uncertainty of a slope requires a more precise method than simply subtracting maximum and minimum slopes derived from data point uncertainties. The discussion highlights the importance of using Weighted Least Squares Straight Line Fitting, particularly when data points have associated uncertainties in their y-values. This method accounts for the variability in the data more accurately than the previous approach described. A resource is provided for further guidance on this statistical technique. Understanding and applying this method will lead to a more reliable estimation of slope uncertainty.
johnnnnyyy
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So I am a little confused about how to calculate the error uncertainty of a slope. Let's say I have data points (1,2), (2,2.75), (3,3.75), (4,4.7), (5,5.5) which when put in excel gives me a slope of .895. Let's say the error uncertainty for every point is +/-0.1. What I used to do is subtract the maximum possible slope of the first and last points and the minimum possible slope and divide it by 2 to find the error uncertainty. So the max slope would be ((5.5+.1)-(2-.1))/((5-.1)-(1+.1)) and then I would basically do the opposite to find the min slope. But recently I discovered that it is not an accurate way to find the uncertainty of the slope.

Does anybody else no how to find the error uncertainty of a slope?
 
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This appears to be what you're looking for:

https://www.che.udel.edu/pdf/FittingData.pdf‎

Since your data points have uncertainties associated with them (more precisely, with their "y-values"), scroll down to the section Weighted Least Squares Straight Line Fitting which begins on page 8. It might help to skim through the preceding pages first.
 
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