What is the difference between outliers and leverage points?

What is the difference between outliers and leverage points?

Outliers are data points that differ significantly from other observations, while leverage points are data points that have a disproportionate influence on the regression model due to their extreme values in the predictor variables.

Can you give me an example of each?

Sure! An outlier might be a test score of 0 when most scores are between 60 and 100. A leverage point could be a student who scored 100 but also has an unusually high number of study hours compared to others.

How do they affect regression analysis?

Outliers can skew the results and affect the accuracy of predictions, while leverage points can disproportionately influence the slope of the regression line, potentially leading to misleading conclusions.

How can I identify them in my dataset?

You can use statistical methods like the Z-score for outliers and Cook's distance for leverage points to identify them in your dataset.