How to determine whether to remove a generated outlier or not in stepwise regression?
Dear Friends, I have developed a stepwise regression model. I am getting a outlier. I need to know the effective weight of the outlier so that I came to understand weather I need to remove it or my model will play good without removing it. In summary, I want to calculate adjusted R2 value with and without outlier.Can you please help
Neeta Dsouza answered .
2025-11-20
Determining whether to remove an outlier in stepwise regression involves several considerations:
1. Impact on Model Performance: Evaluate how the outlier affects the model's performance metrics, such as R-squared, adjusted R-squared, and prediction accuracy. Removing the outlier might improve these metrics.
2. Influence on Coefficients: Check if the outlier significantly alters the regression coefficients. If the coefficients change drastically with the outlier included, it might be worth considering its removal.
3. Statistical Tests: Use statistical tests like Cook's Distance or leverage values to identify influential outliers. These tests help determine the impact of each data point on the regression model.
4. Domain Knowledge: Consider the context and domain knowledge. Sometimes outliers represent important, valid data points that should not be removed without careful consideration.
5. Sensitivity Analysis: Perform a sensitivity analysis by running the regression with and without the outlier to see how results differ.