The researchers found a way to improve regression analysis when there are outliers causing problems. They used a method called Robust Ridge Regression to handle the issue of multicollinearity, which is when independent variables are closely related. By combining ordinary least squares and robust regression techniques, they were able to reduce the impact of outliers and improve the accuracy of their predictions. This approach showed significant improvements over traditional methods.