**Assignment Task**

**Task**

**Data Description:**

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Get Help Now!You will use a subset consisting of 500 observations of Educational Attainment and Wage Equations (EAWE) data set to answer the questions.

**Questions:**

**1. **Fit a wage equation by regressing EARNINGS on EXP, and perform t tests on the intercept and slope coefficient. Then, perform an F test on the explanatory power of the model and explain the relationship between this F test and the t test on the slope coefficient.

**2. **Fit another wage equation by regressing EARNINGS on S and EXP, and interpret all the parameter estimates (with attention paid to their significance). Compare the slope coefficient of EXP in this equation with the one obtained in Question 1 and interpret the difference.

**3.** Perform an F test of the explanatory power of the equation you obtained in Question 2. Calculate the F statistic using R2 of the fitted regression and verify it is the same as the F statistic in your Stata output.

**4. **Regress the logarithm of EARNINGS on S and EXP. Carefully interpret the regression results, perform t tests on the coefficients and F test of the explanatory

power of the model.

**5. **Use the Box and Cox procedure (Steps 1–3) described on p. 211 of the textbook to evaluate whether the dependent variable of a wage regression of EARNINGS on S and EXP should be linear (like in Question 2) or logarithmic (like in Question 4).

**6.** Regress S on ASVABC, MALE, SM, SF, ETHHISP and ETHBLACK. Use your results to answer the question: Does ethnicity affect educational attainment, and if yes, how?

**7.** Redo Question 6 making ETHBLACK the reference category. What are the impacts of change of reference on the interpretation of the coefficients and the statistical tests (t tests of the coefficients and F test of the model)?

**8. **Define a slope dummy variable as the product of MALE and ASVABC. Regress the S on ASVABC, MALE, SM, SF, ETHHISP, ETHBLACK, and the slope dummy variable. First, explain what this regression with slope dummy variable can be useful for. Second, what are your findings based on your regression results?

**9.** Fit a wage equation by regressing the logarithm of EARNINGS on S, EXP and MALE. Perform a Goldfeld-Quandt test for heteroskedasticity in the S dimension. Why heteroskedasticity is a concern when we conduct the regression analysis?

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