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

question 11

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SUMMARY OUTPUT
 Regression Statistics  Multiple R 0.216543528 R square 0.046891099 Adjusted R Square 0.042101608 Standard Error 1155621.367 Observations 201\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}

 ANOVA  of SS MS  F  Significance F Regression 11.30747E+131.30747E+139.7904119750.00201776 Residual 1992.65757E+141.33546E+12 Total 2002.78831E+14\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}

 Coefficients  Standard Error  t Stat  P-value  Lower 95%  Upper 95%  Intercept 7,773,135.5582,867,254.7732.7110027450.00729436413,427,237,2402,119,033.877 Yards Per Drive 30,737.5239,823.5483.1289634030.0020177611,365.91350,109.132\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}

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


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