More often than not, we want to include not just one, but multiple predictors (independent variables), in our predictive models. Luckily, linear regression can easily accommodate us! The technique? Multiple regression.
By giving each predictor its very own beta coefficient in a linear model, the target variable gets informed by a weighted sum of its predictors. For example, a multiple regression using two predictor variables looks like this:
Now, instead of estimating two coefficients ( and ), we are estimating three: the intercept, ...