A large international bank is reviewing its compensation policy for its senior executives. They want to closely examine the relationship between senior executives salaries and the growth in their business portfolio. The dataset contains last years annual compensation of the bank's senior executives (in thousands of dollars), including performance pay, and the annual rate of growth of their respective business portfolios (as a percentage). These variables are called Salary_raw and ROG_raw, respectively.

a.) Use statistical software to estimate the model below and report your results.

b. Create the corresponding residual plot (residuals against predicted values) and normal probability plot of the residuals. Looking at these two plots, does the estimated model appear satisfactory?

c. Use statistical software to estimate a new model, this time by using the adjusted variables Salary_adj and ROG_adj which exclude all the data points for which the salary of the senior executive appears extraordinarily large considering the ROG of their business portfolio. Report your results.

I will really appreciate it if the questions are answered in a way that I would be able to understand. Providing steps and ways to solve the questions will be appreciated extremely.

A D F min SRE. 00 OU UN N 000 A 1 Obs 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 B Salary_raw ROG_raw 219 14.1 200.2 10.9 224.4 23.5 115.6 5.9 273.6 13.8 229 20.0 215.6 16.4 8 218.8 16.3 247.4 10.5 10 166.6 26.3 11 113.4 25.9 12 186.6 26.8 13 267.8 14.8 14 187.4 22.3 15 402.2 56.3 16 317 12.6 17 181 20.4 211.6 1.9 19 184.4 19.9 20 244 15.4 21 204.4 38.7 151.8 16.4 23 282.8 24.4 24 208.2 15.6 25 337.6 14.4 26 596.6 19.0 27 232 16.1 28 768.8 12.1 29 95.2 16.2 30 298.4 18.4 31 204.8 14.2 E G Obs_adj Salary_adi ROG_adj 1 219 14.1 2 200.2 10.9 3 224.4 23.5 4 115.6 5.9 273.6 13.8 229 20.0 215.6 16.4 8 218.8 16.3 9 247.4 10.5 10 166.6 26.3 11 113.4 25.9 12 186.6 26.8 13 267.8 14.8 14 187.4 22.3 15 402.2 56.3 16 317 12.6 17 181 20.4 18 211.6 1.9 19 184.4 19.9 20 244 15.4 21 204.4 38.7 22 151.8 16.4 23 282.8 24.4 24 208.2 15.6 25 337.6 14.4 27 232 16.1 29 95.2 16.2 30 298.4 18.4 31 204.8 14.2 32 318.6 14.9 33 85.4 12.4 18 22 w Salary_raw; = Bo + B1 * ROG_raw; + i + & = A D F min SRE. 00 OU UN N 000 A 1 Obs 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 B Salary_raw ROG_raw 219 14.1 200.2 10.9 224.4 23.5 115.6 5.9 273.6 13.8 229 20.0 215.6 16.4 8 218.8 16.3 247.4 10.5 10 166.6 26.3 11 113.4 25.9 12 186.6 26.8 13 267.8 14.8 14 187.4 22.3 15 402.2 56.3 16 317 12.6 17 181 20.4 211.6 1.9 19 184.4 19.9 20 244 15.4 21 204.4 38.7 151.8 16.4 23 282.8 24.4 24 208.2 15.6 25 337.6 14.4 26 596.6 19.0 27 232 16.1 28 768.8 12.1 29 95.2 16.2 30 298.4 18.4 31 204.8 14.2 E G Obs_adj Salary_adi ROG_adj 1 219 14.1 2 200.2 10.9 3 224.4 23.5 4 115.6 5.9 273.6 13.8 229 20.0 215.6 16.4 8 218.8 16.3 9 247.4 10.5 10 166.6 26.3 11 113.4 25.9 12 186.6 26.8 13 267.8 14.8 14 187.4 22.3 15 402.2 56.3 16 317 12.6 17 181 20.4 18 211.6 1.9 19 184.4 19.9 20 244 15.4 21 204.4 38.7 22 151.8 16.4 23 282.8 24.4 24 208.2 15.6 25 337.6 14.4 27 232 16.1 29 95.2 16.2 30 298.4 18.4 31 204.8 14.2 32 318.6 14.9 33 85.4 12.4 18 22 w Salary_raw; = Bo + B1 * ROG_raw; + i + & =