# Explanation of multiple linear regression output

Just looking for some help with the interpretation of my multiple linear model output and also some validation on the methods I used.

I have 1 response - Ball speed and 9 continuous predictors and 1 categorical predictor (the type of club with 4 levels).

I have used forward entry method with condition for entry alpha = 0.05

I get from the output with mini-tab, 4 regression equations one for each club. With only a changing y-intercept but the coefficient for other predictors between the clubs stays the same. Is this correct and can anyone explain why? I was expecting the coefficient for the other predictor variables to change with each club.

$$y_i = \beta_{intercept} + \sum_{j=1}^9 \beta_jx_{ij} + \beta_{A}x_{iA} + \beta_{B}x_{iB} + \beta_{C}x_{iC}$$
The $$x_{iA}$$, $$x_{iB}$$, and $$x_{iC}$$ are the binary (0 or 1) indicator variables in row $$i$$ of your data matrix. If you run your regression this way, you should get some familiar-looking numbers from when you ran it the way you did.