Robust regression refers to methods that are less sensitive to outliers, heteroskedasticity, and violations of normality of residuals. These techniques aim to produce reliable estimates even when assumptions like homoskedasticity or normal error distribution...
When the dependent variable in a linear regression is not normally distributed, it can violate key assumptions, particularly those affecting inference (like p-values and confidence intervals). Here are remedies depending on the severity and...
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