I want question e solve by R, I added part d because is based on part d thank you I will like your comment

that's part d that helps answer e

Region 4.9 35 4.8 3.9 1 3 2 3 3 3 3 3 3 4.8 5.5 Clarity Aroma, Body, Flavor, Oakiness, Quality * X2 X3 44 3.3 1 2.8 3.1 4.1 9.8 3.9 12.6 3.9 5.3 4.7 119 1 3.9 2.6 3.1 3.6 11.1 1 5.6 5.1 5.5 5.1 13.3 4.6 4.7 5 12.8 4.8 4.8 4.8 3.3 12.8 5.3 4.5 4.3 5.2 12 4.3 3.9 2.9 13.6 4.3 4.7 3.9 13.9 5.1 4.3 45 3.6 14,4 0.5 3.3 3.4 4.3 3.6 12.3 0.8 5.9 5.7 7 4.1 16.1 0.7 7.7 6,6 6.7 3.7 16.1 1 7.1 4.4 5.8 4.1 15.5 0.9 5.5 5.6 5.6 4.4 15.5 1 6.3 5.4 4.6 13.8 1 5 5.5 4.1 13.8 1 4.6 4.1 43 3.1 11.3 0.9 3.4 5 3.4 3.4 7.9 0.9 6.4 5.4 6.6 4.8 15.1 1 5.5 5.3 5.3 3.8 13.5 0.7 4.7 5 3.7 10.8 0.7 4.1 4 9.5 1 5.4 5.7 4.7 12.7 1 4.3 4.6 4.7 4.9 11.6 1 4 5.1 5.1 11.7 1 5.1 4.9 5 5.1 11.9 1 4.4 5 3.9 10.8 1 4.5 3.7 29 3.9 8.5 1 5.2 4.3 3 6 10.7 0.8 4.2 3.8 4.7 9.1 1 3.3 3.5 4.3 4.5 12.1 1 14.9 5 0.8 5 5.7 55 13.5 4.8 0.8 3.5 4.7 4.2 3.3 12.2 0.8 5.5 3.5 10.3 5.2 4.8 5.7 3.5 13.2 *The wine here is Pinot Noir. Region refers to distinct geographic regions 1 2 3 3.9 2 3 2 1 2 2 2 2. 1 5.2 6.8 3 1 5.8 0.8 aroma and flavor. Discuss your results. e. Find a 95% CI for the regression coefficient for flavor for both models in part d. Discuss any differences. d) For the current model of. R = 0.7206 Ad). R = 0.6769 Lets create another model with only aroma and flavour as independent variables. R code: aroma=c(3.3,4,4,3.9,3.9,5.6.4.6.4.8.5.3,4,3,4,3,5.1,3.3,5.9.7.7.7.1,5.5,6,3,5,4,6,3,4,6,4,5.5,4.7.4.1,6. 4.3,3.9,5.1,3,9,4.5,5 2,4.2,3,3,6,8,5,3,5,4,3,5,2) flavour=c(3.1.3.5,4.8.3.1,5,5,5,4,8,4 3,3 9,4.7.4.5,4,3,7,6,7,5-8,5,6,4.8,5.5,4,3,3,4,6,6,5-3,5,4,1,5,7,4. 7,5.1,5,5.2.9.5,3.4.3,6,5.5.4.2.3.5,5.7) quality=c(9.8.12.6.11.9.11.1.13.3.12.8, 12.8,12,13.6,13.9.14.4,123,16.1,16.1.15.5 15.5.13.8.13.8.11. 3,7.9.15.1.13.5, 10.8,9.5,12.7.11.6,117,119,10,8,8.5,10.7.9.1,12.1.14.9,13.5, 12.2 10.3,132) summary(Imlauality aromat flavou)) Call: Im(formula= quality-aromAtlavour) Residuals: Min 10 Median 30 Max -2.19048 -0.60300 -0.03203 0.66039 2.46287 Coefficients: Estimate Std. Error Laluan Pr> Intercept) 3.9969 2.2318 1.791 0.028775 aroma 0.5180 0.2759 1.877 0.068849 flavour 1.1702 0.2905 4.027 0.00028 Signif. Codes: 0_**** 0.001 + 0.01 0.05 0.11 Residual standard error: 1.229 on 35 degree of freedom Multiple R-squared: 0.6586 adjusted R-squared: 0.639 F-statistic: 33.75 on 2 and 35 DF p-value: 6.811-09 For new model 0.6586 Ad). R0619 Output: Both and Adlare Migher for the model with independent rex Cold del Merce that model is better than the new model with only 2 independent variables aroma=c(3.3.4.4,3.9,3.9,5.6,4,6,4.8,5.3.4.3,4.3,5.1,3.3,5.9,7.7,7.1,5.5,6.3,5,4.6,3.4,6.4,5.5,4.7.4.1,6, 4.3,3.9,5.1.3.9,4.5,5.2.4.2.3.3,6.8,5,3.5,4.3,5.2) flavour=c(3.1,3.5,4.8,3.1,5.5,5,4.8,4.3,3.9,4.7,4.5,4.3,7,6.7,5.8,5.6,4.8,5.5,4.3, 3.4,6.6,5.3,5,4.1,5.7,4. 7,5.1,5,5,2.9,5,3,4,3,6,5.5,4.2,3.5,5.7) quality=c(9.8,12.6,11.9,11.1,13.3,12.8,12.8,12,13.6,13.9,14.4,12 3,16.1,16.1,15.5 15.5 13.8,13.8.11. 3,7.9.15.1.13.5,10.8,9.5,12.7.11.6,117,11.9,10.8,8.5,10.7,9.1,12.1.14.9,13.5,12.2,10.3,13.2) summary(Im(quality-aromatflavour)) Call: Im(formula= quality-aroma+flavour) Residuals: Min 1Q Median 30 Max -2.19048 -0.60300 -0.03203 0.66039 2.46287 Coefficients: Estimate Std. Error [value Pre> It) (Intercept) 3.9969 2.2318 1.791 0.028775 *** aroma 0.5180 0.2759 1.877 0.068849 flavour 1.1702 0.2905 4.027 0.000288 *** Signif. Codes: 0_**** 0.001 0.01 *** 0.05 0.1''1 Residual standard error: 1.229 on 35 degree of freedom Multiple R-squared: 0.6586. adjusted R-squared: 0.639 F-statistic: 33.75 on 2 and 35 DF. p-value: 6.8110-09 For new model R' = 0.6586 Ad). R = 0.639 Output: I. her fald model Hence