This chapter provides a comprehensive overview of regression analysis, designed for readers who are familiar with basic statistical concepts and are looking to deepen their understanding of regression techniques. The field of regression analysis is a cornerstone of predictive analytics, enabling researchers and practitioners to model relationships between variables and make data-driven predictions. The chapter starts with an introduction to regression and its various types, followed by an in-depth exploration of simple linear regression (SLR). It covers the evaluation of SLR models, estimation and prediction methods, and the critical assumptions underlying SLR. Subsequent sections address advanced topics such as the standard error of the estimate, the T-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 regression analysis, equipped with the skills necessary to build and evaluate regression models effectively.