Least absolute deviations

Least Absolute Deviations (LAD) is a statistical method used to find the best-fit line or model for a set of data points. Unlike traditional methods that minimize the squared differences between the data and the model (which can be affected by very large errors), LAD minimizes the sum of the absolute differences. This approach makes it more robust to outliers or unusual data points, as it treats all deviations equally regardless of their size. Essentially, LAD seeks the line that minimizes the total distance from all data points, providing a more resistant and reliable fit in certain scenarios.