You are deciding whether to pursue an advanced degree after college (which takes five years) or work during these five years and gain some job experience. To make an informed decision you construct a prediction model that uses data from the Current Population Survey (CPS) in the US. The survey asks a large number of individuals about their earnings and their demographic characteristics. Your data contains four dummy variables that indicate the highest educational attainment of each individual: hsg (high school graduate), cg (college graduate), ad (advanced degree), and oe (other education levels, which is omitted here). lnw is the log of the hourly wage, female is a dummy variable for women, exp1 denotes job experience and exp2 is job experience squared divided by 100. Suppose that your objective is to maximize your wage in 5 years.

1. Write the simplest possible regression model that would allow you to inform your decision. Carefully specify the model equation and describe its specific elements. How many parameters would your model estimate?
2. How would you set up a prediction model that allows for the relationship between experience and wage to depend on the levels of experience? Would you still be able to use the method of OLS to estimate your model? Justify your answer