Pedro Became Interested in Vehicle Fuel Efficiency, So He Performed$\begin{array} { l l r l } r ^ { 2 } 0.711 & \text { n } 93 \\ r - 0.843 & k 1 \\ \text { Std. Error } & 3.038 & \text { Dep. Var. CityMPG } \end{array}$

The Answer of Pedro Became Interested in Vehicle Fuel Efficiency, So He Performed$\begin{array} { l l r l } r ^ { 2 } 0.711 & \text { n } 93 \\ r - 0.843 & k 1 \\ \text { Std. Error } & 3.038 & \text { Dep. Var. CityMPG } \end{array}$

Pedro Became Interested in Vehicle Fuel Efficiency, So He Performed$\begin{array} { l l r l } r ^ { 2 } 0.711 & \text { n } 93 \\ r - 0.843 & k 1 \\ \text { Std. Error } & 3.038 & \text { Dep. Var. CityMPG } \end{array}$

The Answer of Pedro Became Interested in Vehicle Fuel Efficiency, So He Performed$\begin{array} { l l r l } r ^ { 2 } 0.711 & \text { n } 93 \\ r - 0.843 & k 1 \\ \text { Std. Error } & 3.038 & \text { Dep. Var. CityMPG } \end{array}$

Solved

Pedro Became Interested in Vehicle Fuel Efficiency, So He Performed

question 62

Essay

Pedro became interested in vehicle fuel efficiency, so he performed a simple regression using 93 cars to estimate the model CityMPG = β₀ + β₁ Weight, where Weight is the weight of the vehicle in pounds. His results are shown below. Write a brief analysis of these results, using what you have learned in this chapter. Is the intercept meaningful in this regression? Make a prediction of CityMPG when Weight = 3000 and also when Weight = 4000. Do these predictions seem believable? If you could make a car 1000 pounds lighter, what change would you predict in its CityMPG?
Regression Analysis
$\begin{array} { l l r l } r ^ { 2 } 0.711 & \text { n } 93 \\ r - 0.843 & k 1 \\ \text { Std. Error } & 3.038 & \text { Dep. Var. CityMPG } \end{array}$

$\text { ANOVA table }$
$\begin{array}{lrrrrr}\text { Source } & \text { SS } & \text { df } & \text { MS } & F & p \text {-value } \\\hline \text { Regression } & 2,065.5191 & 1 & 2,065.5191 & 223.75 & 2.97 \mathrm{E}-26 \\\text { Residual } & 840.0508 & 91 & 9.2313 & & \\\hline \text { Total } & 2,905.5699 & 92 & & & \\\hline\end{array}$

$\text { Rearession outout }$ $\quad$ $\quad$ $\quad$ $\quad$ $\quad$ $\quad$ $\quad$ $\quad$ $\quad$ $\quad$ $\quad$ $\quad$ $\quad$ $\quad$ $\quad$ $\quad$ $\quad$ $\quad$ $\quad$ $\quad$ $\quad$ $\quad$ $\text { confidence interval }$
$\begin{array}{lrrrrrr}\hline \text { variables } & \text { coefficients } & \text { std. error } & t(d f=91) & p \text {-value } & 95 \% \text { lower } & 95 \% \text { upper } \\\text { Intercept } & 47.0484 & 1.6799 & 28.006 & 1.63 \mathrm{E}-46 & 43.7114 & 50.3853 \\\text { Weight } & -0.0080 & 0.0005 & -14.958 & 2.97 \mathrm{E}-26 & -0.0091 & -0.0070 \\\hline\end{array}$

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