Regression is a method used to understand how different factors affect an outcome. When there are many factors involved, they can be closely related, causing a problem called multicollinearity. Outliers in the data can also affect the results. A new method called Partial Robust M-Regression (PRM) has been developed to handle both multicollinearity and outliers at the same time. In a comparison with other methods like Ridge Regression and Principal Component Regression, PRM was found to be the most effective in dealing with these issues. This means that PRM can provide more accurate predictions when there are multiple factors influencing the outcome.