The article covers Linear Regression Models in three parts. Part 1 focuses on Simple Linear Regression, which involves using one predictor to make predictions. Part 2 delves into Multiple Linear Regression, where multiple predictors are used. Lastly, Part 3 covers Nonlinear Regression, which includes advanced techniques like Neural Networks. The researchers explore different types of regression analysis and how to build accurate regression models, diagnose potential issues, and suggest solutions. They also touch on topics like autocorrelation in time series data. By using various regression methods, the study aims to help understand and predict relationships between different variables in data sets for better decision-making.