The file "Fees15.csv" includes data about audit fees paid by public companies for fiscal year 2015 (Note: This could include companies with fiscal year ends up to March 31, 2016 or even a little later). This file also incudes data about CIK, SIC code, ASSETS, auditor type (Big 4 or not), and Fiscal year end of the companies.

fin <- subset(Fees15, SIC < 7000 & SIC > 5999)

LN(FEES) = + 1*LN(ASSETS) + 2*BIG4

ff <- fin$FEES af <- fin$ASSETS LFF <- log(ff) LFA <- log(af) B4F <-factor(fin$BIG4) resultsf <-lm(LFF~LFA+B4F) summary(resultsf) Assignment: What is the predicted audit fees for a bank with $500 million in assets? How does this number compare with the predicted fee if we used the full dataset (i.e., the entire sample of 4426 observations without deleting the non-financial companies)? (Recall that we did this earlier, and you have the regression results from the full sample.) Bonus question: How does the predicted audit fee for a bank with $500 million in assets compare with the predicted audit fees for a non-financial firm with $500 million in assets? What are the lessons to learn from such a comparison?