Archived Weighted least-squares fit error propagation

[mbigras]

Homework Statement

Suppose we measure N pairs of values (xi, yi) of two variables x and y that are supposed to statisfy a linear relation y = A + Bx suppose the xi have negligible uncertainty and the yi have different uncertainties \sigma_{i}. We can define the weight of the ith measurement as w_{i} = 1/\sigma_{i}. Then the best estimates of A and B are:

<br /> A = \frac{\Sigma w x^{2}\Sigma w y - \Sigma w x \Sigma w x y}{\Delta}\\<br /> B = \frac{\Sigma w \Sigma w x y - \Sigma w x \Sigma w y}{\Delta}\\<br /> \Delta = \Sigma w \Sigma x^{2} - \left(\Sigma w x \right)^{2}<br />

Use error propagation to prove that the uncertainties in the constants A and B are given by

<br /> \sigma_{A} = \sqrt{\frac{\Sigma w x^{2}}{\Delta}}\\<br /> \sigma_{B} = \sqrt{\frac{\Sigma w}{\Delta}}<br />

Homework Equations

rules for sums and differences
<br /> q = x \pm z\\<br /> \delta q = \sqrt{(\delta x)^{2} + (\delta z)^{2}}\\<br />
rules for products and quotients
<br /> \delta q = \sqrt{(\delta x/x)^{2} + (\delta z/z)^{2}}\\<br />

The Attempt at a Solution

What I'm thinking is because the uncertainty in x is negligible it will be treated like a constant. I'm not sure how to deal with all these sums. I feel like I don't know how to approach this question.

 

[haruspex]

Try calculating E(A²) etc. The negligible uncertainty in the x allows you to treat the Δ denominators as constant.