1.For the population of people in the workforce in 1976, WAGE1 data was used to analyze how a person's wage is associated with observed education. The table 1 list summarize statistics of the variables in the dataset. Table 2 reports the regression results from four different models, where column (1) is simple linear model with dependent variable wage, while column (2) -(4)use log wage as dependent variable.

How do you use the table to answer these questions and form the correct equations?

a. Make an estimated equation using column (1) information. Interpret the coefficient of education (educ). Is it significant?

b. Column (2) is different from column (1) in that we take the natural logarithm of wage

now. How do we interpret the coefficient of education now?

c.A person's working experience matters for his/her wage income. Therefore, we add

experience (exper) variable to the existing model: lwage = 0 + 1*EDUC + 2*Exper + u

Using this model to describe OLS methodology. Specifically, what are the decision variables? What is the objective function?

d. Column (3) reports the OLS results of the above model. Does experience have a significant impact on wage? If so, by how much?

e. Write the estimated equation using column (4) information. Using this estimated equation to calculate the impact of one more year working experience on wage.

f.Based on the regression results of four models in Table 2, which model do you prefer? Explain.

A Table 1: Summarize Statistics Statistic N Mean St. Dev. Min Pot 1 (25) Pct1 (75) Max wage 526 5. 896 3. 693 0.530 3. 330 6. 880 24.980 educ 526 12.563 2. 769 14 exper 6 17. 017 13.572 26 51 tenure 526 5 .105 7. 224 44 0. 304 DOOOOWITH nonwhite 526 0.103 DOOOOHOL female 526 0.479 0. 500 NHHOW married 526 0. 608 0. 489 numdep 16 1. 044 1. 262 Table 2: Regression results Dependent variable: wage 1 wage (1) (2) (3) (4) educ 0.54* * * 0 . 08 * * * 0. 10* * * 0. 09* * * (0. 05) (0. 01) (0. 01) (0. 01) ex per 0. 01 * * * 0. 04 * * * (0. 002) (0. 01) exper squ -0. 001 * * * (0. 0001) Constant -0. 90 0.58 * * * 0. 22 * * 0.13 (0. 68) (0.10) (0.11) (0. 11) Observations 526 526 526 526 R2 0. 16 0.19 0. 25 0. 30 Adjusted R2 0. 16 0.18 0. 25 0. 30 F Statistic 103. 36 * * * 119.58 * * * 86. 86* * * 74.67**