The variance of $Y _ { i }$ is given by
A) $\beta _ { 0 } ^ { 2 } + \beta _ { 1 } ^ { 2 } \operatorname { var } \left( X _ { i } \right) + \operatorname { var } \left( u _ { i } \right)$
B) $\text { the variance of } u _ { i }$
C) $\beta _ { 1 } ^ { 2 } \operatorname { var } \left( X _ { i } \right) + \operatorname { var } \left( u _ { i } \right)$
D) the variance of the residuals.

Question

The variance of  $Y _ { i }$ is given by
A) $\beta _ { 0 } ^ { 2 } + \beta _ { 1 } ^ { 2 } \operatorname { var } \left( X _ { i } \right) + \operatorname { var } \left( u _ { i } \right)$
B) $\text { the variance of } u _ { i }$
C) $\beta _ { 1 } ^ { 2 } \operatorname { var } \left( X _ { i } \right) + \operatorname { var } \left( u _ { i } \right)$
D) the variance of the residuals.

Quizplus · Accepted Answer

The variance of $Y_i$ in a simple linear regression model is given by $\beta_1^2 \operatorname{var}(X_i) + \operatorname{var}(u_i)$, where $\beta_1$ is the coefficient of $X_i$ and $u_i$ is the error term.

The Variance Of YiY _ { i }Yi​ Is Given by A)

The Variance Of $Y _ { i }$ Is Given by
A)