Applying a multiple regression model involves three main steps:

1. Nominate the predictor variables, represented as $X_1, X_2, \dots, X_n$.
2. Define a parameter (slope coefficient) $\beta_j$ for each predictor variable $X_j$ to measure its association with the outcome variable $Y$.
3. Multiply each predictor variable by its corresponding parameter and sum them together (along with an intercept parameter $\alpha$) to define the model's linear predictor: $\mu_i = \alpha + \sum_{j=1}^n \beta_j X_{j,i}$