Suppose you regress the number of days that individuals report having hunted in the past year on years of education and that you get the results in Figure 4.3. SUMMARY OUTPUT

\begin{array}{lc}\hline \text { Regression Statistics } & \\\hline \text { Multiple R } & 0.069296763 \\\text { R Square } & 0.004802041 \\\text { Adjusted R Square } & 0.004749178 \\\text { Standard Error } & 9.950755489 \\\text { Observations } & 18828 \\\hline\end{array}

\begin{array}{l}\text { ANOVA }\\\begin{array}{lccccc}\hline & d f & S S & M S & F & \text { Significance } F \\\hline \text { Regression } & 1 & 8994.697853 & 8994.697853 & 90.83944446 & 1.74217 \mathrm{E}-21 \\\text { Residual } & 18826 & 1864104.11 & 99.0175348 & & \\\text { Total } & 18827 & 1873098.808 & & & \\\hline\end{array}\end{array}

\begin{array}{lcccccc}\hline & \text { Caefficients } & \text { Standard Error } & t \text { Stat } & \text { P-value } & \text { Lower } 95 \% & \text { Upper } 95 \% \\\hline \text { Intercept } & 6.8799 & 0.4320 & 15.9260 & 0.0000 & 6.0331 & 7.7266 \\\text { Years of Education } & -0.2969 & 0.0312 & -9.5310 & 0.0000 & -0.3580 & -0.2358 \\\hline\end{array}

Figure

4.3

How would you interpret these results? What would you say about the Goodness-of-Fit of the regression? Explain.

What would be the predicted number of hunting days for an individual with 12 years of education? 16 years of education? Show your work.

The Explained Sum of Squares Is

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