Please use R or Rstudio for this exercise andshow everything, including the R output. Payattention in everything in Bold, please.

\" The quality of Pinot Noir wine is thought to be related to theproperties of clarity, aroma, body, flavor, and oakiness. Data for38 wines are given in stat5_prob1.

(a) Fit a multiple linear regression modelrelating wine quality to these regressors.

(b) Construct the ANOVA table.

(c) Test for the significance of the regressionin a 0.05 significance level. What conclu- sions can you draw?

(d) Use the t tests to assess the individualcontribution of each regressor to the model in a 0.05significance level. Discuss your findings.

(e) What is the contribution of the set of clarity andaroma to the model, given that all of the other regressorsare included? Perform this hypothesis test using 0.05 significancelevel.

(f) Find a 95% confidence interval for the regressioncoefficient for flavor.

(g) Calculate R^2 and R^2 adj for this model.Compare these values to the R^2 and R^2 adj forthe regression model relating wine quality to aroma and flavor.Discuss your results.

***Here is the data for the 38wines***

# quality is y
# clarity is x1, aroma is x2, body is x3, flavor is x4, oakiness isx5.

y=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, 11.7, 11.9, 10.8, 8.5, 10.7, 9.1,12.1, 14.9, 13.5, 12.2, 10.3, 13.2)

x1=c(1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0.5, 0.8, 0.7, 1, 0.9,1, 1, 1, 0.9, 0.9, 1, 0.7, 0.7, 1, 1, 1, 1, 1, 1, 1, 0.8, 1, 1,0.8, 0.8, 0.8, 0.8)

x2=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)

x3=c(2.8, 4.9, 5.3, 2.6, 5.1, 4.7, 4.8, 4.5, 4.3, 3.9, 4.3,5.4, 5.7, 6.6, 4.4, 5.6, 5.4, 5.5, 4.1, 5, 5.4, 5.3, 4.1, 4, 5.4,4.6, 4, 4.9, 4.4, 3.7, 4.3, 3.8, 3.5, 5, 5.7, 4.7, 5.5,4.8)

x4=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)

x5=c(4.1, 3.9, 4.7, 3.6, 5.1, 4.1, 3.3, 5.2, 2.9, 3.9, 3.6,3.6, 4.1, 3.7, 4.1, 4.4, 4.6, 4.1, 3.1, 3.4, 4.8, 3.8, 3.7, 4, 4.7,4.9, 5.1, 5.1, 4.4, 3.9, 6, 4.7, 4.5, 5.2, 4.8, 3.3, 5.8, 3.5).\"