Linear Regression in R

In this chapter, we will introduce linear regression, a fundamental statistical approach that’s used to model the relationship between a target variable and multiple explanatory (also called independent) variables. We will cover the basics of linear regression, starting with simple linear regression and then extending the concepts to multiple linear regression. We will learn how to estimate the model coefficients, evaluate the goodness of fit, and test the significance of the coefficients using hypothesis testing. Additionally, we will discuss the assumptions underlying linear regression and explore techniques to address potential issues, such as nonlinearity, interaction effect, multicollinearity, and heteroskedasticity. We will also introduce two widely used regularization techniques: the ridge and Least Absolute Shrinkage and Selection Operator (lasso) penalties.

By the end of this chapter, you will learn the core principles of linear regression...