Robust regression methods

Robust regression methods are statistical tools designed to analyze data while minimizing the impact of outliers—unusual data points that can distort results. Unlike standard regression, which can be heavily affected by these anomalies, robust methods focus on accurately modeling the main pattern of the data, even when some points are off. This makes the analysis more reliable in real-world situations where messy or imperfect data is common. Essentially, robust regression helps us understand true relationships without being misled by data errors or extreme values.