The article discusses a new method to identify outliers in data used for regression analysis. Outliers are data points that don't fit the overall pattern and can skew results. The researchers propose a sequential outlier test that compares different estimates of the data to detect outliers. By removing extreme data points one by one, the test can pinpoint outliers without being influenced by other data. This method was tested on real and simulated data and showed good performance in identifying outliers accurately.