The estimated regression equation for a model involving two independent variables and 10 observations follows.
$\hat{y} = 27.1370 + 0.5206x_1 + 0.4910x_2$
(a) Interpret $b_1$ in this estimated regression equation.
$b_1 = 0.5206$ is an estimate of the change in y corresponding to a 1 unit change in $x_1$ when $x_2$ is held constant.
$b_1 = 0.4910$ is an estimate of the change in y corresponding to a 1 unit change in $x_1$ when $x_2$ is held constant.
$b_1 = 0.4910$ is an estimate of the change in y corresponding to a 1 unit change in $x_2$ when $x_1$ is held constant.
$b_1 = 27.1370$ is an estimate of the change in y corresponding to a 1 unit change in $x_1$ when $x_2$ is held constant.
$b_1 = 0.5206$ is an estimate of the change in y corresponding to a 1 unit change in $x_2$ when $x_1$ is held constant.
Interpret $b_2$ in this estimated regression equation.
$b_2 = 0.5206$ is an estimate of the change in y corresponding to a 1 unit change in $x_1$ when $x_2$ is held constant.
$b_2 = 0.4910$ is an estimate of the change in y corresponding to a 1 unit change in $x_1$ when $x_2$ is held constant.
$b_2 = 0.4910$ is an estimate of the change in y corresponding to a 1 unit change in $x_2$ when $x_1$ is held constant.
$b_2 = 0.5206$ is an estimate of the change in y corresponding to a 1 unit change in $x_2$ when $x_1$ is held constant.
$b_2 = 27.1370$ is an estimate of the change in y corresponding to a 1 unit change in $x_1$ when $x_2$ is held constant.
(b) Predict y when $x_1 = 160$ and $x_2 = 340$.