Why is normality important in regression analysis?

Why is normality important in regression analysis?

Normality is crucial in regression analysis because it ensures that the residuals are normally distributed, which validates the assumptions of the model.

What happens if the residuals are not normally distributed?

If the residuals are not normally distributed, it can lead to biased estimates, unreliable hypothesis tests, and invalid confidence intervals.

How can I check for normality in my regression model?

You can check for normality using visual methods like Q-Q plots or statistical tests like the Shapiro-Wilk test.

What can I do if my residuals are not normal?

You can try transforming your dependent variable, using robust regression techniques, or applying non-parametric methods.