This chapter provides a comprehensive overview of multiple linear regression, designed for readers who are familiar with basic statistical concepts and are looking to deepen their understanding of regression techniques. The field of multiple linear regression is a cornerstone of predictive analytics, enabling researchers and practitioners to model relationships between multiple variables and make data-driven predictions. The chapter begins with an introduction to regression and its various types, followed by an in-depth exploration of multiple linear regression (MLR). It covers the evaluation of MLR models, estimation and prediction methods, and the critical assumptions underlying MLR. Subsequent sections address advanced topics such as the standard error of the estimate, the t-test and F-test for statistical significance, and the inclusion of categorical predictors in regression models. Additionally, the chapter discusses data transformations, model building strategies, and the identification and handling of influential points. Practical demonstrations using R-Studio and SAS are provided to illustrate the application of these techniques, ensuring that readers can apply their theoretical knowledge to real-world data analysis. By the end of this chapter, readers will have a robust understanding of multiple linear regression, equipped with the skills necessary to build and evaluate regression models effectively.