In this chapter, we introduced simple linear regression, which models the relationship between a single explanatory variable and a continuous response variable. We worked through a toy problem to predict the price of a pizza from its diameter. We used the residual sum of squares cost function to assess the fitness of our model, and analytically solved the values of our model's parameter that minimized the cost function. We measured the performance of our model on a test set. Finally, we introduced scikit-learn's estimator API. In the next chapter, we will compare and contrast simple linear regression with another simple, ubiquitous model, k-Nearest Neighbors (KNN).