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The regression line predicted highway $mpg=4.62+1.109$(city mpg).

A regression line's slope is the indirect relationship between the $x$- and $y$-variables.

$\text{Slope}=1.109$

In each of the two variables, $x$(city mpg) and $y$(highway mpg) represents the increase in the $y-$variable.

Note: mpg =miles per gallon

The regression line predicted highway $mpg=4.62+1.109$(city mpg).

Statistics have meaning only when a value is in line with reality.

In these circumstances, the car will get $0$miles per gallon in the city since the intercept is$4.62$highway mpg.

If the explanatory variable is $0$, the y-intercept is the value of the response variable.

The regression line predicted highway $mpg=4.62+1.109$(city mpg).

$$\begin{gathered} Highwaympg=4.62+1.109 \\ Highwaympg=4.62+1.109 \\ Highwaympg=4.62+17.74 \\ =22.36 \end{gathered}$$(city mpg)

Using the regression equation for the regression line, we can calculate the predicted highway mileage given the city mileage of the vehicle.

$$\begin{gathered} Highwaympg=4.62+1.109 \\ Highway\mathit{}mpg=4.62+1.109\left(28\right) \\ Highway\mathit{}mpg=4.62+31.05=35.67 \end{gathered}$$ (city mpg)