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Suppose That You Regress the Total Number of Medals That

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Suppose that you regress the total number of medals that a country won in the 2008 Olympics on GDP per capita and that you get the results in Figure 5.4. SUMMARY OUTPUT

 Regression Statistics  Multiple R 0.193666659 R. Square 0.037506775 Adjusted R Square 0.027881842 Standard Error 17.8035921 Observations 102\begin{array}{lc}\hline \text { Regression Statistics } & \\\hline \text { Multiple R } & 0.193666659 \\\text { R. Square } & 0.037506775 \\\text { Adjusted R Square } & 0.027881842 \\\text { Standard Error } & 17.8035921 \\\text { Observations } & 102 \\\hline\end{array}


 ANOVA  of SSMSF Significance F Regression 11235.1716331235.1716333.8968351880.05113579 Residual 10031696.78915316.9678915 Total 10132931.96078\begin{array}{l}\text { ANOVA }\\\begin{array}{lccccc}\hline & \text { of } & S S & M S & F & \text { Significance } F \\\hline \text { Regression } & 1 & 1235.171633 & 1235.171633 & 3.896835188 & 0.05113579 \\\text { Residual } & 100 & 31696.78915 & 316.9678915 & & \\\text { Total } & 101 & 32931.96078 & & & \\\hline\end{array}\end{array}

 Coefficients  Standard Emor t Stat  P-value  Lower 95% Upper 95%  Intercept 6.07922.30792.63410.00981.500510.6580 GDP Per Capita (Thousands) 0.15530.07871.97400.05110.00080.3113\begin{array}{lcccccc}\hline & \text { Coefficients } & \text { Standard Emor } & t \text { Stat } & \text { P-value } & \text { Lower } 95 \% & \text { Upper 95\% } \\\hline \text { Intercept } & 6.0792 & 2.3079 & 2.6341 & 0.0098 & 1.5005 & 10.6580 \\\text { GDP Per Capita (Thousands) } & 0.1553 & 0.0787 & 1.9740 & 0.0511 & -0.0008 & 0.3113 \\\hline\end{array}

 Figure 5.4\text { Figure } 5.4

What would you conclude about the statistical significance of the estimated sample regression function? The estimated intercept? The estimated slope coefficient? Explain.


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