review the assignment below then complete the task below. Thank you

Assignment Instructions This week's case study is based on Chapter 2 in the book and the attached case study. "Gallagher is a consulting firm that has hired you as an analyst to analyze CEO compensation." Use the data from class to develop an analysis that you can present to your boss. reg = smf. ols('salary~sales', data=ceosall) jail account, results = reg. fit() print (results. summary ( )) OLS Regression Results Dep. Variable: alary 0.014 Model: R-squared: OLS Adj. R-squared: 0.010 Method : Least Squares F-statistic: 3.018 Date: Tue, 14 Oct 2025 Prob (F-statistic) : 0.0838 Time : 04: 28:52 Log-Likelihood: 1804.4 No. Observations : 209 AIC: 3613. of Residuals: 207 BIC: 3620. of Model: Covariance Type: nonrobust coef std err P It| [0.025 0.975] . . . . .- Intercept 1174.0049 112.813 10.407 0.00 951.596 1396.414 sales 0.0155 600'8 1.737 0.084 zee'0- 0.033 Omni bus : 315.565 Durbin-Watson: Prob (Omnibus) : 0.000 Jarque-Bera (JB) : 33496.452 Skew: 7.076 Prob( JB) : 0.00 Kurtosis: 63. 384 Cond. No. 1.51e+04 Notes : [1] Standard Errors assume that the covariance matrix of the errors is correctly specified. [2] The condition number is large, 1.51e+04. This might indicate that there are strong multicollinearity or other numerical problems. reg - smf . ols( ' salary~pcsalary', data-ceosall) results = reg. fit( ) print (results. summary ()) OLS Regression Results Dep. Variable: salary R-squared: 0.000 Model: OLS Adj. R- squared -0.005 Method: Least Squares F-statistic: e.01557 Wed, 15 Oct 2025 Prob (F-statistic): Time: 01:10:06 Log-Likelihood: 1805.9 No. Observations: 209 AIC: 3616. Of Residuals: 207 BIC: 3623. of Model: Covariance Type: nonrobust coet std err pIt| [0.025 0.975] Intercept 1276.2757 102.767 12.419 0.00 1073.671 1478. 880 pcsalary 0.3647 2.923 0.901 -5.397 6.127 omnibus: 309.635 Durbin-Watson: 2.068 Prob (Omnibus) : Jarque-Bera (JB): 30538.407 Skew: 6.860 Prob ( JB) 0.00 Kurtosis : 60.607 Cond. No 38.0 Notes : [1] Standard Errors assume that the covariance matrix of the errors is correctly specified. reg = smf . ols( ' salary~roe' , data=ceosall) results = reg. fit() print(results. summary ( ) ) OLS Regression Results Dep. Variable: salary R-squared: 0.013 Model: OLS Adj. R-squared: Method Least Squares F-statistic: 2:767 Date. ed, 15 Oct 2025 Prob (F-statistic): Time: 01:10:05 Log-Likelihood: 1804.5 No. Observations : 3613. Of Residuals: 207 3620. of Model: Covariance Type: nonrobust coef std err P It] 0.025 0.975] Intercept 963.1913 213.240 roe 18.5012 13 123 4.517 0.080 542.790 383.592 1.663 0.098 -3.428 40. 431 Omnibus : 311.096 Durbin-Watson: 2.105 Prob (Omnibus) : 0.000 Jarque-Bera (JB) : 1120.902 Skew: 6.915 Prob(JB) : 0.60 Kurtosis : 61.158 Cond. No. 43.3 Notes : [1] Standard Errors assume that the covariance matrix of the errors is correctly specified. Your solutions and recommendations need to be based on the coefficients of your regression model results. Your job is to think about how that variable relates back to real life and what you could recommend changing based on it