1. Answer all parts (a)-(e) of this question. (a) [10 marks] Define the following terms for a multiple regression model: population re- gression function, disturbance, sample regression function, residual. (b) [12 marks] List the classical linear regression model assumptions and briefly explain each one. (c) [12 marks] For each of the assumptions listed in (b) above, give an example of a scenario in which the assumption fails to hold. (d) [8 marks] Describe the consequences for the ordinary least squares estimator of mul- tiple linear regression coefficients if the disturbance term is heteroskedastic. You may abbreviate heteroskedasticity by using the letter ' #' in your answer. (e) [8 marks] What is meant by a regressor being endogenous? Describe the consequences of endogeneity for the ordinary least squares estimator of multiple linear regression co- efficients. Name a solution to this problem. (No maths required, verbal explanations will suffice.)