Question #17

of 139 Consider the following graph of residuals and the regression line from a time-series regression: These residuals exhibit the regression problem of: A) autocorrelation. B) homoskedasticity. C) heteroskedasticity. Using a recent analysis of salaries (in $1,000) of financial analysts, Timbadia runs a regression of salaries on education, experience, and gender. (Gender equals one for men and zero for women.) The regression results from a sample of 230 financial analysts are presented below, with t-statistics in parenthesis. Salary = 34.98 + 1.2 Education + 0.5 Experience + 6.3 Gender (29.11) (8.93) (2.98) (1.58) Timbadia also runs a multiple regression to gain a better understanding of the relationship between lumber sales, housing starts, and commercial construction. The regression uses a large data set of lumber sales as the dependent variable with housing starts and commercial construction as the independent variables. The results of the regression are: Coefficient Standard Error t-statistics Intercept 5.337 1.71 3.14 Housing starts 0.76 0.09 8.44 Commercial construction 1.25 0.33 3.78 Finally, Timbadia runs a regression between the returns on a stock and its industry index with the following results: Coefficient Standard Error Intercept 2.1 2.01 Industry index 1.9 0.31 Standard error of estimate = 15.1 Correlation coefficient = 0.849