I Uncertainty of coefficients after a least square fit

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Calculating the uncertainty of coefficients after fitting data using least squares can be complex, especially when off-diagonal elements are present in the covariance matrix. The diagonal elements represent the square of the uncertainties for each coefficient, while off-diagonal elements indicate the correlation between coefficients. When evaluating expressions involving both coefficients, the off-diagonal elements must be considered to determine the overall uncertainty. A reference to Eq 22 of Kirchner's note provides clarity on this topic. Understanding these relationships is crucial for accurate uncertainty estimation in linear regression analysis.
sth
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Fitting data to a linear function (y=a0+a1*x) with least square gives the coefficients a0 and a1. I am having trouble with calculating the uncertainty of a0. I understand that the diagonal elements of the covariance matrix C is the square of the uncertainty of each coefficient if there are no off-diagonal elements. But what is the uncertainty of a0 if there are off-diagonal elements?
 
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Hello sth, :welcome:

Found your answers and a good reference in this thread

[edit] on second thought: the errors are the diagonal elements. The off-diagonal elements come in when you evaluate expressions where both coefficients appear and you want the uncertainty in the result.
 
Hi BvU,
Thank you for welcoming and the reference. Seems like Eq 22 of Kirchner's note is what I was looking for.
 
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