Real life rarely fits a single straight line. When you try to explain house prices, for example, you might need square footage, number of bedrooms, age of the house, and location all working together. This chapter takes simple linear regression and extends it to handle several predictors at once, so we can build richer, more honest models. We will learn how to interpret coefficients when many variables compete, how to spot when predictors step on each other’s toes, and how to choose the right set of variables without fooling ourselves.