You have fitted a Simple Linear Regression model and discovered heteroscedasticity in the residuals. What impact could this have, and how might you correct it?
- Always Leads to Overfitting, No Correction Possible
- Biased Estimates, Increase Sample Size
- Inefficiency in Estimates, Transform the Dependent Variable
- No Impact, No Correction Required
Heteroscedasticity could lead to inefficiency in the estimates, making them less reliable. Transforming the dependent variable or using weighted least squares can help correct this issue.
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