The article discusses how regression analysis, a statistical method, is applied in social sciences. It covers various types of regression like linear, polynomial, and robust regression. The focus is on analyzing relationships between different factors. For instance, it examines how multiple variables can predict outcomes and deal with issues like outliers in data. The study also delves into more advanced topics like multicollinearity and interactions between variables. In addition, it touches on longitudinal data analysis and ways to select important variables for the analysis. The researchers also include practical aspects such as matrix algebra basics and computational considerations related to regression. By using these methods, researchers in social sciences can better understand and predict relationships among different factors in their studies.