The following model $y = \beta _ { 0 } + \beta _ { 1 } x _ { 1 } + \beta _ { 2 } x _ { 2 }$ + $\varepsilon$ is used whenever the statistician believes that, on average, y is linearly related to:
A) $x _ { 1 }$ , and the predictor variables do not interact.
B) $x _ { 2 }$ , and the predictor variables do not interact.
C) $x _ { 1 }$ and the predictor variables do not interact or to $x _ { 2 }$ , and the predictor variables do not interact.
D) $x _ { 1 }$ and the predictor variables do not interact and to $x _ { 2 }$ , and the predictor variables do not interact.

Question

Quizplus · Accepted Answer

The model assumes that y is linearly related to both $x_1$ and $x_2$ independently, without interaction between the predictor variables.

The Following Model y=β0+β1x1+β2x2y = \beta _ { 0 } + \beta _ { 1 } x _ { 1 } + \beta _ { 2 } x _ { 2 }y=β0​+β1​x1​+β2​x2​

The Following Model $y = \beta _ { 0 } + \beta _ { 1 } x _ { 1 } + \beta _ { 2 } x _ { 2 }$