13.2 Error terms

There’s going to be a difference between the line that we fit and the observation we get.

• Error: theoretical, difference between the actual outcome and prediction we’d make if we had infinite observations to estimate our prediction (true best-fit line)
• Residual: observed, difference between the actual outcome and the prediction

• error contains everything that causes Y that is not included in the model
• if our model is $Y = \beta_0 + \beta_1 X + \epsilon$, then what’s included in $epsilon$?