In the linear regression model, $Y _ { i } = \beta _ { 0 } + \beta _ { 1 } X _ { i } + u _ { i } , \beta _ { 0 } + \beta _ { 1 } X _ { i }$
is referred to as
A) the population regression function.
B) the sample regression function.
C) exogenous variation.
D) the right-hand variable or regressor.

Question

Quizplus · Accepted Answer

The expression $\beta _ { 0 } + \beta _ { 1 } X _ { i }$ represents the population regression function, which describes the expected value of $Y$ given $X$ in the population.

In the Linear Regression Model Yi=β0+β1Xi+ui,β0+β1XiY _ { i } = \beta _ { 0 } + \beta _ { 1 } X _ { i } + u _ { i } , \beta _ { 0 } + \beta _ { 1 } X _ { i }Yi​=β0​+β1​Xi​+ui​,β0​+β1​Xi​

In the Linear Regression Model $Y _ { i } = \beta _ { 0 } + \beta _ { 1 } X _ { i } + u _ { i } , \beta _ { 0 } + \beta _ { 1 } X _ { i }$