ABSTRACT
Regression analysis is one of the most widely used tools in statistical analysis. It is a powerful technique due to both its ease of calculation and simplicity of assumptions. This chapter cover the main aspects of regression analysis starting up with a motivation to the problem and covering both linear and polynomial regression techniques. It discusses how feature selection can be done with the help of appropriate regularisation techniques. The regression parameters can be obtained with the params method of the fitted model object. One of the principal tenets of the linear regression model is the idea that the relationship between the variables at play is linear. Multivariate regression refers to having more than one input variable in our model. In general, linear regression exhibits high variance and low bias and it should therefore stand to reason that lowering the variance at the expense of the bias is the way to go.
