Understanding Error Bars on Least Squares Fit Line

Thread starter radiator
Start date Jan 29, 2012
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Error Error bars Fit Least squares Line Squares

AI Thread Summary

Error bars on the least squares fit line represent the uncertainty in the predicted values. The discussion clarifies that the relevant formula for calculating the variance of the error is σ_y^2 = (1/(N-2)) * Σ(y_i - A - Bx_i)^2. This formula accounts for the differences between observed values and the fitted line. The user initially sought clarification on the concept but later confirmed the solution. Understanding this formula is crucial for accurately interpreting the reliability of the fit line in data analysis.

Jan 29, 2012

radiator

Hello,

What are the error bars on the least squares fit line?

recall the variables are:
x ; y ; x^2 ; xy ; y^2 ; yi=mx+c ; d =(y-yi) ; d^2

---------
is it d?

Thanks

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Jan 29, 2012

radiator

never mind... Its solved

Jan 29, 2012

radiator

the answer is: \sigma_y^2 = \frac{1}{N-2} \times \displaystyle \sum_i^N (y_i - A - Bx_i)^2

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