The article discusses alternatives to basic linear regression for modeling data when the assumptions of normal distribution are not met. These alternatives include robust regression estimators that are less affected by outliers, nonparametric rank regression that focuses on the order of data rather than the actual values, and isotonic regression that enforces monotonic relationships between variables. These methods expand the applicability of regression models to real-world scenarios where traditional linear regression may not be suitable.