What is the best estimate for B in Least Squares Fitting?

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SUMMARY

The best estimate for the coefficient B in the least squares fitting of the linear relationship y = Bx is derived using the formula B = [Sum(xy)]/[Sum(x^2)]. This conclusion is supported by the equations provided, where Del = [N(Sum(x^2))] - (Sum(x))^2 serves as the denominator for the calculation of B. The discussion emphasizes the importance of correctly simplifying the expressions to arrive at the best estimate without unnecessary complications. The participant's attempts to manipulate the equations highlight common challenges faced in deriving the least squares estimate.

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1. Homework Statement

Suppose two variables x and y are known to satisfy a relation y=Bx. That is a graph of x vs. y is a line through the origin. Suppose further that you have N measurements (xi,yi)and that the uncertainties in x are negligible and those in y are equal. Prove the best estimate for B is B= [Sum(xy)]/[Sum(x^2)]

2. Homework Equations

B= [(N Sum(xy))-(Sum(x))*(Sum(y))]/[Del]

Del = [N(Sum(x^2))] - (Sum(x))^2]

3. The Attempt at a Solution [/b]

So I plugged the equation of Del into the equation for B so I can try to simplify it and therefor show the best estimate. But it just gets more and more complicated. Is that for sure where I should start?
 
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I simplified the expression for B into...

[Sum(x)Sum(y)] * [(N - 1)] / [Sum(x^2)] [N - Sum(x^2)]

This almost gives me what I want but I'm not sure what to do with the N - 1 and N - Sum(x^2). Might it be that when N = 0 (at the origin) it reduces the expression to just the best estimate for B?
 

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