In this chapter, we studied linear regression, a method that allows us to fit a linear model in a supervised learning setting where we have a number of input features and a single numeric output. Simple linear regression is the name given to the scenario where we have only one input feature, and multiple linear regression describes the case where we have multiple input features. Linear regression is very commonly used as a first approach to solving a regression problem. It assumes that the output is a linear weighted combination of the input features in the presence of an irreducible error component that is normally distributed and has zero mean and constant variance. The model also assumes that the features are independent. The performance of linear regression can be assessed by a number of different metrics from the more standard MSE to others, such as the R² statistic. We explored several model diagnostics and significance tests designed to detect problems from violated assumptions...