SUMMARY OUTPUT $\text { Figure } 4.1$ -Suppose You Are Given the Excel Output in You Would

SUMMARY OUTPUT
$\begin{array}{lc}\hline \text { Regression Statistics } & \\\hline \text { Multiple R } & 0.216543528 \\\text { R square } & 0.046891099 \\\text { Adjusted R Square } & 0.042101608 \\\text { Standard Error } & 1155621.367 \\\text { Observations } & 201 \\\hline\end{array}$

$\begin{array}{l}\text { ANOVA }\\\begin{array}{lccccc}\hline & \text { of } & S S & \text { MS } & \text { F } & \text { Significance } F \\\hline \text { Regression } & 1 & 1.30747 E+13 & 1.30747 E+13 & 9.790411975 & 0.00201776 \\\text { Residual } & 199 & 2.65757 E+14 & 1.33546 E+12 & \\\text { Total } & 200 & 2.78831 E+14 & & \\\hline\end{array}\end{array}$

$\begin{array}{lcccccc}\hline & \text { Coefficients } & \text { Standard Error } & \text { t Stat } & \text { P-value } & \text { Lower 95\% } & \text { Upper 95\% } \\\hline \text { Intercept } & -7,773,135.558 & 2,867,254.773 & -2.711002745 & 0.007294364 & -13,427,237,240 & -2,119,033.877 \\\text { Yards Per Drive } & 30,737.523 & 9,823.548 & 3.128963403 & 0.00201776 & 11,365.913 & 50,109.132 \\\hline\end{array}$

$\text { Figure } 4.1$
-Suppose you are given the Excel output in You would conclude that the estimated sample regression function explains

A) 21.65 percent of the total variation in annual earnings.
B) 4.69 percent of the total variation in annual earnings.
C) 4.21 percent of the total variation in annual earnings.
D) 0.02 percent of the total variation in annual earnings.