Introducing linear regression

At the core of linear regression is the concept of fitting a straight line – or more generally, a hyperplane – to the data points. Such fitting aims to minimize the deviation between the observed and predicted values. When it comes to simple linear regression, one target variable is regressed by one predictor, and the goal is to fit a straight line that best mimics the relationship between the two variables. For multiple linear regression, there is more than one predictor, and the goal is to fit a hyperplane that best describes the relationship among the variables. Both tasks can be achieved by minimizing a measure of deviation between the predictions and the corresponding targets.

In linear regression, obtaining an optimal model means identifying the best coefficients that define the relationship between the target variable and the input predictors. These coefficients represent the change in the target associated with a single unit change...