Why is the assumption of independently and identically distributed (IID) residuals important in linear regression?
- It ensures that the model is not overfitting
- It ensures that the model is not underfitting
- It ensures that the parameter estimates are unbiased
- It ensures the correctness of standard errors and hypothesis tests
The assumption of IID residuals is important because it ensures that standard errors, confidence intervals, and hypothesis tests are valid. If this assumption is violated, these statistics may be incorrect, leading to misleading results.
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