Example: Recall the margarine and divorce dataset. Use the Excel data for the following: a. Determine the estimate simple linear regression (E[Divorce.rate] = o + BMarg.consumed) of the model: Divorce.rate = Bo+B1(Marg. consumed) + b. What is the coefficient of determination? Interpret this. c. Interestingly, the regression model seems good but what issue remains? Is there really a causal relationship? Is the direction correct? Which variables are significant (statistically) predictors of Divorce rate: d. What is the estimate of B? What is the interpretation of this estimate in the context of this model? e. What is the 95% confidence interval estimate for B? What does this say about the hypothesis that B = 0 f. At a 0.05 significance level, use Excel output from the coefficients table to test Ho: B = 0 against Ha: 0 g. At a 0.05 significance level, use the ANOVA table to test Ho: = 0 against Ha: 0 SUMMARY OUTPUT Regression Statistics Multiple R 0.931603186 R Square 0.867884496 Adjusted R 0.851370059 Standard E 0.138646333 Observatic 10 ANOVA MS F gnificance F 1.010217554 1.010218 52.55308 8.81E-05 0.153782446 0.019223 1.164 df SS Regression 1 Residual 8 Total 9 0.032513497 t Stat P-value Lower 95% Upper 95% 17.1207 1.38E-07 2.662243 3.491034 7.24935 8.81E-05 0.160725 0.310678 Coefficients Standard Error Intercept Margarine 3.076638803 0.235701716 0.179702893