Consider a regression model that links a CEO’s compensation (in $ millions) with the total assets of the firm (in $ millions) and the firm’s industry. Dummy variables are used to represent four industries: Manufacturing Technology d₁, Manufacturing Other d₂, Financial Services d₃, and Nonfinancial Services d₄. A portion of the data for the 455 CEOs is shown in the accompanying table. 

a. Estimate the model: y = β₀ + β₁x + β₂d₁ + β₃d₂ + β₄d₃ + ɛ, where y and x denote compensation and assets, respectively. Here the reference category is the nonfinancial services industry. 

b. Interpret the estimated coefficients.

c. Use a 5% level of significance to determine which industries, relative to the nonfinancial services industry, have different executive compensation. 

d. Reformulate the model to determine, at the 5% significance level, if compensation is higher in Manufacturing Other than in Manufacturing Technology. Your model must account for total assets and all industry types.

CEO 1 2 1 450 16.58 26.92 2.30 X 20917.5 32659.5 44875.0 d, dz d3 d4 1 0 0 0 1 0 # 0 0 0 I 0 0 1 *** 0 *** 0