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

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Heteroscedasticity refers to the violation of the assumption of constant variance of error terms. When the variance of error terms is not constant across different values of X, it is called heteroscedasticity. The least squares method assumes homoscedasticity, which means that the variance of error terms is constant for all values of X. Multicollinearity refers to the situation when the independent variables in a regression model are highly correlated with each other. Autocorrelation refers to the correlation between error terms in a regression model.

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

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