What are the consequences of violating the homoscedasticity assumption in multiple linear regression?
- The R-squared value becomes negative
- The estimated regression coefficients are biased
- The regression line is not straight
- The standard errors are no longer valid
Violating the assumption of homoscedasticity (constant variance of the errors) can lead to inefficient and invalid standard errors, which can result in incorrect inferences about the regression coefficients. The regression coefficients themselves remain unbiased.
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