How do I handle outliers in regression analysis?

How do I handle outliers in regression analysis?

Handling outliers in regression can be crucial for accurate predictions. You can either remove them, transform your data, or use robust regression techniques.

What are some methods to identify outliers?

Common methods include visualizations like box plots and scatter plots, as well as statistical tests like the Z-score or IQR method.

Should I always remove outliers?

Not necessarily. It depends on the context. If outliers are due to data entry errors, they should be removed. If they represent valid variations, consider using robust methods.

What is robust regression?

Robust regression is a technique that is less sensitive to outliers, providing a better fit for data with anomalies.

Can you give an example of robust regression?

Sure! One common method is the Huber regression, which combines the least squares and least absolute deviations approaches.