I was under the impression that if I called `lm(@formula(y~x),data)`

from the GLM.jl package was fitting a simple linear regression of the form:

y=a+bx_i+\epsilon_i

The results from the regression being:

\hat{y}=\hat{a}+\hat{b}x

Which is our linear predictor. But we want to predict a value we have to use:

\hat{y}=\hat{a}+\hat{b}x+\epsilon_i

Why doesn’t lm return an estimate for error (\epsilon_i) in the summary of coefficients? Isn’t this an important part of the regression analysis? R returns the estimate as the auxiliary parameter. For a least squares regression wouldn’t this estimate just be the standard deviation of the residuals? Is there any way to access this information without just creating a helper function to calculate that value on my own? Im probably just missing something very dumb!