Interpreting the Coefficients
What each number in the equation actually means
Let's break down the estimated regression equation from the additive model. What does each coefficient tell us?
There are three coefficients. Let's look at each one.
b₀ is the predicted market value for a goalkeeper earning €0M salary.
When salary = 0 and position = goalkeeper, the model predicts a market value of €-1.4M.
b₁: for each additional €1M in salary, market value increases by b₁ M€ — regardless of position.
The slope is identical for both lines. Whether goalkeeper or outfield, an extra €1M in salary predicts an increase of €3.04M in market value.
b₂: the position (being an outfield) effect — at any salary level, outfield players are worth b₂ M€ more than goalkeepers.
The gap is the same at every salary level. This is what "additive" means — position shifts the prediction by a constant amount.
Plug in any salary + position to get a prediction.