# Example 2.8, CEO Salary and Firm Sales # Data set: ceosal1 load("ceosal1.Rdata") # Recap the estimated model cat("The example uses the data set of CEO salaries that was used in Example 2.3", "\nModel to estimate: lsalary = beta0 + beta1 * roe + u", "\nwhere lsalary is ", paste(desc[desc[,1]=="lsalary",2]), "\nand lsales is ", paste(desc[desc[,1]=="lsales",2]), " (sales: ", paste(desc[desc[,1]=="sales",2]), ")", sep="") summary(data$lsalary) summary(data$lsales) # Estimate and show results model=lm(lsalary~lsales, data=data) summary(model) cat("The estimated regression line is\n", "lsalaryhat = ", round(model$coefficients[1],digits=3), " + ", round(model$coefficients[2],digits=3), " * lsales\n", "n = ", nrow(data), ", R^2 = ", round(summary(model)$r.squared,digits=3), sep="") # Interpretation cat("The model is a constant elasticity model. The estimate of the slope coefficient indicates that when sales increase by 1%, salary is predicted to increase by ", round(model$coefficients[2],digits=3), "%. The coefficient is the estimated elasticity of salary with respect to sales", sep="")