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Question 1 A statistician was interested in studying the determinants of the salaries of Chief Executive Officers (CEO) of companies. Data was collected from a sample of 209 firms. The model, the list of variables and the basic output are shown below. log(salary) = β₀ + β₁*log(sales) + β₂*roe + β₃*finance + ε List of variables: salary: the annual salary of the CEO in small dollars sales: the annual sales of the company in small dollars roe: return on equity finance: a dummy variable that equals one if the company is in the financial industry and zero for other sectors industrial: a dummy variable that equals one if the company is in industrial firm and zero for other firms Model output: [Table of regression output showing coefficients, standard errors, t-values, and p-values for the intercept, log(sales), roe, and finance variables] [Additional statistical output including R-squared, adjusted R-squared, F-statistic, and residual standard error] [Breusch-Pagan test results] [Variance Inflation Factors (VIF) results] [Cameron Trivedi's decomposition of IM-test results] Which statements are correct? (You may tick more than one of the tickboxes below, provided they are correct) □ At the significance level of 1%, the intercept is significant but the model suffers from heteroskedasticity □ At the significance level of 5%, the finance variable and intercept are the only statistically significant variables □ At the significance level of 1%, the White's test indicates that the model suffers from heteroskedasticity □ The White test shows there is heteroskedasticity at the significance level of 5%