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

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

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

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