Undergrad Plot Linear Fit w/ Error Bounds: y=ax+b

[kelly0303]

Hello! I made a linear fit, $y=ax+b$, to some data points and I get the best parameters with their (1 sigma) errors: $a\pm\delta a$ and $b\pm\delta b$. I want to plot this fit on top of my data points in such a way as to reflect the error on the parameters. The "main" fit is simply $y=ax+b$ with the parameters obtained from the fit, but I would like 2 more lines as un upper and limit to that (like a 1 sigma band). What formula should I use for the upper and lower lines associated to the boundaries of this band? Thank you! (I would like something like this, although I am not sure why their band is curved)

 

[scottdave]

I believe that the curved band is a 95% prediction band. This is telling us we are 95% confident that the true line that the data came from, lies in the band. Imagine you can pivot the band a little bit and you'll understand why it is curved.

I believe it can be produced using the R statistical software (available as a free download). Take a look at this from stackexchange - https://stats.stackexchange.com/que...tion-of-confidence-bands-in-linear-regression

If you are unfamiliar with R, then I suggest taking a look at Swirl, a free site that helps you to get it downloaded and has some nice tutorials. https://swirlstats.com/students.html

 

[scottdave]

Think about you hook up a voltage source to a resistor, and you measure current. From Ohm's Law, we expect a straight line. The slope of the line represents the resistance. But due to factors such as temperature, noise, accuracy of your measuring equipment, you get dots which are not collinear. So you do regression and get the best fit line. The curved band would represent other places that the line might actually lie (with 95% confidence) for this resistor, based on the data measurements.

 

[WWGD]

Usually during your regression analysis you do your hypothesis tests which give you confidence intervals for both the intercept and the slope. I believe these are used, at least in/with the frequentist approach. Are you maybe using a Bayesian approach?