In this scenario we have to determine whether or not one of the two variables in this multiple regression model is significant. We will be utilizing a f-test versus a f-test as the f-test tests whether or not one or more variables are significant, and a f-test would determine if a particular variable is significant. The null hypothesis in this F-test is HO: 1=82=0, which means that the slopes for Bl and B2 are 0. The multiple regression model that is being used is y = 35.1191 -3.2990 x1 - 0.0324 x2 and we must remember that a multiple regression model follows the formula Y = 30 + BIX1 + B2X2 . The alternative hypothesis in this F-test is Ha: At least on Bi# 0 for i=1,2 . This means that the slopes for Bl and B2 will not = 0. The level of significance for this is at 5% or a=0.05 . The F-statistic from the OLS regression results are the value 66.76. The Prob (F-statistic) is 3.54e-11 or 0.000000000035. Running the F-test we can see that the p-value is less than the level of significance or 0.000000000035 It [0.025 0.975] Intercept 35.1191 21.860 31.82 WE -3.2998 0.611 -5.398 e.eee 4.55 -2.045 hp 0.0324 0.098 -3.969 0.0ee -0.049 -0.016 Omnibus : 8. 716 urbin-Watson 1.664 Prob (Omnibus ) : 9.013 Jarque-Bera (JB) : 7.093 Skew: 0.998 Prob(JB) : . 0288 Kurtosis: 4.299 Cond. No. 657. Warnings: 1] Standard Errors assume that the covariance matrix of the errors is correctly specified. The slope coefficient for the horsepower variable is -0.0308. or $2=-0 . 0308 . Performing an individual F-test for the weight variable and using a level of significance for this is at 5% or a=0.05 , we can see that the p-value is less than the level of significance or 0.002 It), for weight in Python output. Recall that this is the individual t-test for the beta parameter.) See Step 5 in the Python script. 3. What is the slope coefficient for the horsepower variable? Is this coefficient significant at 5% level of significance (alpha=0.05)? (Hint: Check the P-value, P>Itl, for horsepower in Python output. Recall that this is the individual t-test for the beta parameter.) See Step 5 in the Python script. 4. What is the purpose of performing individual t-tests after carrying out the overall F-test? What are the differences in the interpretation of the two tests? 5. What is the coefficient of determination of your multiple regression model from Module Six? Provide appropriate interpretation of this statistic