Least Absolute Deviations (LAD) is a statistical method used for regression analysis that minimizes the sum of the absolute differences between observed and predicted values, making it robust to outliers compared to the least squares method. It is particularly useful when the data contains outliers or when the error distribution is not normal, as it focuses on the median of the residuals rather than the mean.