What happens when the assumptions about residuals in linear regression are violated?
- The interpretation of the model changes
- The model becomes invalid
- The model becomes underfit
- The standard errors, confidence intervals, and hypothesis tests may not be valid
Violations of the assumptions about residuals in linear regression can lead to inefficient and biased estimates, and standard errors, confidence intervals, and hypothesis tests may not be valid. This can lead to incorrect inferences and predictions.
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