Multiple Linear Regression

Multiple linear regression models define the relationship between two or more features and the continuous outcome variable using y = α + β1xi1 + β2xi2 + … + βp-1xi,p-1. Again, α represents the intercept and β denotes the slope for each feature (x) in the model. Thus, if we are predicting the weight of an individual in kg using height in m, total cholesterol in milligrams per deciliter (mg/dL), and minutes of cardiovascular exercise per day, and the multiple linear regression model computes 1.5 as the value for α, 50 as the coefficient for β1, 0.1 as the coefficient for β2, and -0.4 as the coefficient for β3, this model can be interpreted as for every 1 m increase in height, weight increases by 50 kg, controlling for all other features in the model. Additionally, for every 1 mg/dL increase in total cholesterol, weight increases by 0.1 kg, controlling for all other features in the model. Lastly, for...