The article compares different methods for estimating parameters in linear models when there are outliers in the data. Ordinary least squares (OLS) estimators are usually the best, but outliers can affect their accuracy. Robust regression methods are used as an alternative to improve the model fit and parameter estimates. The focus is on outliers in the response direction. The methods were compared based on efficiency and breakdown point through a simulation study and applied to real data.