Question

using the weights (lb) and highway fuel consumption amounts (mi/gal) of the 48 cars listed in the accompanying data set, one gets this regression equation: \\(\hat{y} = 58.9 - 0.00749x\\), where \\(x\\) represents weight. complete parts (a) through (d). click the icon to view the car data. ... \\(\bigcirc\\) b. the slope is 0.00749 and the y-intercept is 58.9. \\(\bigcirc\\) c. the slope is 58.9 and the y-intercept is \\(-0.00749\\). \\(\bigcirc\\) d. the slope is 58.9 and the y-intercept is 0.007499. c. what is the predictor variable? \\(\bigcirc\\) a. the predictor variable is highway fuel consumption, which is represented by \\(x\\). \\(\bigcirc\\) b. the predictor variable is highway fuel consumption, which is represented by \\(y\\). \\(\bigcirc\\) c. the predictor variable is weight, which is represented by \\(x\\). \\(\bigcirc\\) d. the predictor variable is weight, which is represented by \\(y\\).

Explanation:

In a regression equation \(\hat{y} = b_0 + b_1x\), \(x\) is the predictor (independent) variable used to predict \(y\) (the response variable). Here, the equation is \(\hat{y}=58.9 - 0.00749x\), where \(x\) represents weight. So the predictor variable is weight, represented by \(x\).

Answer:

C. The predictor variable is weight, which is represented by x.