What is the difference between robust regression and linear regression?

What is the difference between robust regression and linear regression?

Linear regression assumes that the relationship between variables is linear and is sensitive to outliers. Robust regression, on the other hand, is designed to be less affected by outliers and provides a more reliable estimate in the presence of such data.

Can you give me an example of when to use robust regression?

Sure! If you have a dataset with significant outliers, like income data where a few individuals earn much more than the rest, robust regression would give you a better fit than linear regression.

What are some methods used in robust regression?

Common methods include M-estimators, R-estimators, and S-estimators, which adjust the influence of outliers on the regression model.

Is robust regression always better than linear regression?

Not necessarily. Robust regression is better when outliers are present, but if your data is clean and normally distributed, linear regression may perform just as well or better.