I have been asked to complete the following question using a F

Application of F-test: Test of overall significance of a regression y = Bo + B1xi1 + 32xi2 + ... + Bkxik + u Ho : B1 = B2 = ... = Bk = 0 The null hypothesis states that all the explanatory variables are not useful at all in explaining the y = Botur dependent variable Restricted model (regression on constant) F = (SSRr - SS Rur) / q R2 / k SS Rur/ (n - k -1) (1 - R2) / (n - k - 1) ~ Fk,n-k-1 The test of overall significance is reported in most regression packages; the null hypothesis is usually overwhelmingly rejected5. (v) Now re-estimate the equation in (iv) by using the log of each variable. That is, estimate the model: log(Cases) = Bo + B1 log(GDP) + B2 log(POP) + u Report your regressions results in a sample regression function. What is the estimated elasticity of Cases with respect to GDP? Test whether the model overall is statistically significant at 1% level. log(Cases) = -19.314 + 1.093 log(GDP) + 1.066 log(POP) + u The coefficient of log(cases) 0.1093 means that as the cases go up by 1% the GDP goes up by 0.1093%.SUMMARY OUTPUT Regression Statistics Multiple R 0.80445748 R Square 0.64715184 Adjusted R Sq 0.64265696 Standard Erro 1.53022963 Observations 160 ANOVA of 5S MS F Significance F Regression 2 674.265911 337.132956 143.975301 3.0595E-36 Residual 157 367.631627 2.34160272 Total 159 1041.89754 Coefficients itandard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0% Intercept 19.314086 1.6231197 -11.89936 1.113E-23 -22.520054 -16.108117 -22.520054 -16.108117 Log(GDP) 1.0934907 0.10020081 10.9129928 5.4648E-21 0.89557515 1.29140626 0.89557515 1.29140626 Log(POP) 1.06606178 0.07402663 14.4010587 1.6548E-30 0.91984519 1.21227837 0.91984519 1.21227837