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

Short Answer

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: $\begin{array}{|l|l|}\hline\text { A. } & \text {multicollinearity. }\\\hline \text { B. } & \text {heteroscedasticity. } \\\hline \text { C. } &\text { homoscedasticity.}\\\hline \text { D. } &\text {autocorrelation. }\\\hline\end{array}$