The least squares method requires that the variance of the error variable$\varepsilon$ is a constant no matter what the value of x is. When this requirement is violated, the condition is called:
A) heteroscedasticity.
B) homoscedasticity.
C) influential observation.
D) non-independence of $\varepsilon$.

Question

The least squares method requires that the variance  of the error variable$\varepsilon$ is a constant no matter what the value of x is. When this requirement is violated, the condition is called:
A) heteroscedasticity. 
B) homoscedasticity. 
C) influential observation. 
D) non-independence of $\varepsilon$.

Quizplus · Accepted Answer

Heteroscedasticity is the violation of the requirement that the variance of the error variable is constant for all values of the independent variable. In other words, the variance of the error term changes as the value of the independent variable changes. This can have an impact on the reliability of the regression results, and may require using alternative methods to account for the heteroscedasticity. Homoscedasticity, on the other hand, means that the variance of the error term is the same for all values of the independent variable. Influential observation refers to a data point that has a strong impact on the regression results, while non-independence of the error term means that the errors are correlated with each other, which can also affect the accuracy of the regression results.

The Least Squares Method Requires That the Variance of the Error