What is the impact of heteroscedasticity on a multiple linear regression model?
- It affects the linearity of the model
- It affects the normality of the residuals
- It causes multicollinearity
- It invalidates the statistical inferences that could be made from the model
Heteroscedasticity, or non-constant variance of the error term, can invalidate statistical inferences that could be made from the model because it violates one of the assumptions of multiple linear regression. This could lead to inefficient estimation of the regression coefficients and incorrect standard errors, which in turn affects confidence intervals and hypothesis tests.
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