Evaluate the regression output of your regression model

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Reference no: EM13752046

a) Construct and run a regression model to predict CEO compensation as a function of the independent variables.

b) Evaluate the regression output of your regression model. Is the output in good shape? Are there any modifications you would like to do? Is there any evidence of multicollinearity problem?

c) Try constructing a smaller regression that uses fewer independent variables. You might need to experiment with several different regression models using different combinations of independent variables. Evaluate your best regression model by looking at the regression R-square, significance-F value, p-values, and confidence intervals. Are you satisfied with your regression model?

d) Which are the critical factors that are good predictors of CEO compensation? In particular, does having an MBA have an effect on CEO compensation? Why or Why not?

e) Suppose that you would like to use your regression model to predict the compensation of a particular CEO. She has been a CEO for the last 5 years, her company's stock price increased 15% over the last year, her company's sales increased 30% over the last year, and she has an MBA degree from Suffolk University. What would your regression model predict for her CEO compensation package?

Reference no: EM13752046

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