The model $E ( y ) = \beta _ { 0 } + \beta _ { 1 } x _ { 1 } + \beta _ { 2 } x _ { 2 }$ was fit to a set of data.
A partial printout for the analysis follows:

Interpret the value of the residual when $x _ { 1 } = 7,781$ and $x _ { 2 } = 644$.
A) The predicted $\hat { y }$ exceeds the observed value of $y$ by $8.468$.
B) Since the residual is not 0 , the model is not useful for predicting $y$.
C) The predicted $\hat { y }$ is $8.468$ less than the observed value of $y$.
D) Since the residual is negative, there is evidence of a negative linear relationship between $y$ and at least one of the two independent variables.

Question

The model $E ( y ) = \beta _ { 0 } + \beta _ { 1 } x _ { 1 } + \beta _ { 2 } x _ { 2 }$ was fit to a set of data.
A partial printout for the analysis follows:

Interpret the value of the residual when $x _ { 1 } = 7,781$ and $x _ { 2 } = 644$.
A) The predicted $\hat { y }$ exceeds the observed value of $y$ by $8.468$. 
B) Since the residual is not 0 , the model is not useful for predicting $y$. 
C) The predicted $\hat { y }$ is $8.468$ less than the observed value of $y$. 
D) Since the residual is negative, there is evidence of a negative linear relationship between $y$ and at least one of the two independent variables.

Quizplus · Accepted Answer

The Answer of The model $E ( y ) = \beta _ { 0 } + \beta _ { 1 } x _ { 1 } + \beta _ { 2 } x _ { 2 }$ was fit to a set of data.
A partial printout for the analysis follows:

Interpret the value of the residual when $x _ { 1 } = 7,781$ and $x _ { 2 } = 644$.
A) The predicted $\hat { y }$ exceeds the observed value of $y$ by $8.468$. 
B) Since the residual is not 0 , the model is not useful for predicting $y$. 
C) The predicted $\hat { y }$ is $8.468$ less than the observed value of $y$. 
D) Since the residual is negative, there is evidence of a negative linear relationship between $y$ and at least one of the two independent variables.

The Model E(y)=β0+β1x1+β2x2E ( y ) = \beta _ { 0 } + \beta _ { 1 } x _ { 1 } + \beta _ { 2 } x _ { 2 }E(y)=β0​+β1​x1​+β2​x2​

The Model $E ( y ) = \beta _ { 0 } + \beta _ { 1 } x _ { 1 } + \beta _ { 2 } x _ { 2 }$