The article presents two new methods to improve linear regression models when dealing with outliers and multicollinearity in the data. These methods use a type of optimization to better estimate the relationships between variables, even when some data points are unusual or when variables are highly correlated. The researchers found that their models are efficient in terms of computational resources and can provide more accurate results compared to traditional methods. They tested their approach on real data sets and showed that it can effectively handle these common issues in regression analysis.