This is all one question.

The questions descriptions is in pt.2 and the other images are tables that are to be used for the question.

Thank you!

4. (30 points) For the population of people in the workforce in 1976, WAGEl 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. Answer the following questions using information from Table l and Table 2 below. Table 1: Summarize Statistics Etatistic N Mean St. Dev. Min_ Pctl(25) Ectl(?5)_ Max wage 526 5.396 3.693 0.530 3.330 6.330 24.930 educ 526 12.563 2.?69 0 12 14 13 exper 526 1?".01?Ir 13.5?2 1 5 26 51 tenure 526 5.105 1224 0 0 F" 44 nonwhite 526 0.103 0.304 0 0 0 1 female 526 0.4?9 0.500 0 0 1 1 married 526 0.603 0.439 0 0 1 1 nurndep 526 1.044 1.262 0 0 2 6 a. Write 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 as below: Iwage = Bo + B1*EDUC + 32*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 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.Table 2: Regression results Dependent variable: wage 1wage (1) (2 ) (3) (4) educ 0. 54* * * 0. 08* *# 0. 10# # # 0. 09* *# (0. 05) (0. 01) (0. 01) (0. 01) exper 0. 01* *# 0. 04* * * (0.002) (0. 01) expersqu -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* * Note: *p