2.1Import/read the externalCorporateBonds.csvdata file into a new variable namedbondsin RStudio using theread.table()function. Remember to specify the arguments for theheaderandseparguments correctly in the function. Then use thetail()function to show thelast 5 rowsof variablebonds. (5 points)

2.2First, you can develop abaseline simple linear regression (SLR) modelwhich uses a single independent variableYears ()to predict the dependent variableYield (). Store the built estimated baseline SLR equation's regression results into a new variable calledbonds.slr.fit. Then apply thesummary()function onbonds.slr.fitto show the regression report for this estimated baseline equation. How much percentage of variability inis explained by the estimated baseline equation according to? (10 points)

2.3Use theplot()function onbonds.slr.fitto get the diagnostic plots. Among the 4 diagnostic plots, show the diagnostic plot of Residuals vs Fitted here and use it to explain if the linearity assumption is violated or not. (5 points)

2.4Assume the baseline model violates the linearity assumption, you may now consider using aquadraticregression model to capture the remaining quadratic pattern in the residual plot. Develop a quadratic regression model with two independent variablesYears()andYears Squared ()to predict the dependent variableYield (). Store the built estimated quadratic regression equation's results into a new variable calledbonds.quad.fit. Then apply thesummary()function onbonds.quad.fitto show the regression report for this estimated quadratic regression equation. (10 points)

2.5According to the regression report for the estimated quadratic regression equation from part 2.4, is the overall quadratic regression model significant at the 5% significance level, and which number you use to make the conclusion? Are the individual coefficient estimates ofYearsandYears Squaredsignificant at the 5% level, and which numbers you use to make the conclusions? Per the providedvalue, how much percentage of variability in y is explained by the estimated quadratic regression equation? Do you see any improvement in terms ofby this quadratic regression equation, compared to the previous baseline SLR equation? (10 points)

2.6Use theplot()function onbonds.quad.fitto get the diagnostic plots. Show the diagnostic plot of Residuals vs Fitted here and use it to explain if the linearity assumption is severely violated or not. As a result, do you think the quadratic regression model is better than the baseline SLR model in terms of the linearity assumption and? Why or why not? (10 points)

A B C 1 Company Tic Years Yield 2 GE 0.767 MS 1 1.816 4 WFC 1.25 0.797 5 TOTAL 1.75 1.378 6 TOTAL 3.25 1.748 7 GS 3.75 3.558 MS 4 4.413 JPM 4.25 2.31 10 C 4.75 3.332 11 RABO BK 4.75 2.805 12 TOTAL 5 2.069 13 MS 5 4.739 14 AXP 5 2.181 15 MINA 5 4.366 16 BAC 3.699 17 VOD 5 1.855 18 SHBASS 5 2.861 19 AIG 5 3.452 20 HCN 7 4.184 21 IMS 9.25 5.798 22 GS 9.25 5.365 23 GE 9.5 3.778 24 GS 9.75 5.367 25 C 9.75 4.414 26 BAC 9.75 4.949 27 RABO BK 9.75 4.203 28 WFC 10 3.682 29 TOTAL 10 3.27 30 MINA 10 6.046 31 LNC 10 4.163 32 FOX 10 4.03 33 NEM 10 3.866 34 PAA 10.25 3.856 35 HSBC 12 4.079 36 GS 25.5 6.913 37 C 25.75 8.204 38 GE 26 5.13 39 GE 26.75 5.138 40 T 28.5 4.93 41 BAC 29.75 5.903 42