The least squares method requires that the variance $\sigma _ { \varepsilon } ^ { 2 }$ 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)multicollinearity.
B)heteroscedasticity.
C)homoscedasticity.
D)autocorrelation.

Question

Quizplus · Accepted Answer

Heteroscedasticity occurs when the variance of the error terms in a regression model is not constant across observations, violating the assumption of constant variance (homoscedasticity) in the errors.

The Least Squares Method Requires That the Variance σε2\sigma _ { \varepsilon } ^ { 2 }σε2​

The Least Squares Method Requires That the Variance $\sigma _ { \varepsilon } ^ { 2 }$