The article compares different methods for analyzing data in linear regression when there are outliers present. Ordinary least squares is the best method if assumptions are met, but outliers can make results misleading. Robust regression methods are developed to handle outliers and provide accurate results. The study aims to understand how outliers affect linear regression and compares robust regression methods through simulations to find the best approach for different scenarios.