The correct answer is A. linear regression with varying error terms.
Heteroscedasticity is a violation of the assumption of homoskedasticity, which states that the variance of the error terms is constant. In other words, heteroscedasticity occurs when the variance of the error terms is not constant, but instead varies across the values of the independent variables.
This can be a problem in linear regression because it can lead to biased estimates of the regression coefficients. To test for heteroscedasticity, you can use the Breusch-Pagan test or the White test. If either of these tests indicates that there is heteroscedasticity, you can try to correct for it by using a weighted least squares regression.
Here is a brief explanation of each option:
- A. linear regression with varying error terms: This is the correct answer. Heteroskedasticity is a violation of the assumption of homoskedasticity, which states that the variance of the error terms is constant. In other words, heteroscedasticity occurs when the variance of the error terms is not constant, but instead varies across the values of the independent variables.
- B. linear regression with constant error terms: This is the assumption of homoskedasticity. If this assumption is violated, it can lead to biased estimates of the regression coefficients.
- C. linear regression with zero error terms: This is not possible. The error terms in a linear regression model are always non-negative.
- D. none of these: This is not an option.