1. The intercept estimates how much the response changes on average with changes in the predictor.
2. The estimated value ŷ = b0 + b1 x approximates the average value of the response when the explanatory variable equals x.
3. The horizontal distance between y and ŷ is known as the residual and so takes its scale from the predictor.
4. The sum of the fitted value ŷ plus the residual e is equal to the original data value y.
5. The plot of the residuals on the predictor should show a linear pattern, with the data packed along a diagonal line.
6. Regression predictions become less reliable as we extrapolate farther from the observed data.