6. As you see in the introduction, MARR & KID are two binary variables about personal lives of the workers. Provide a proper interpretation of the estimated coefficient of MARR. Does the estimated coefficient for MARR make sense? Explain. Provide a proper interpretation of the estimated coefficient of KID. Does the estimated coefficient for KID make sense? Explain.

7. a. Are the coefficients for MARR and KID in Part 05 jointly significant at 5% level? Should we keep both MARR and KID in our analysis or not? Show the steps involved in your justification.

b. If you were to remove both MARR and KID variables from the estimated model in Part 05 above, what would be the new estimated model? Write down the new estimated model in the form of Equation 5.8 of the textbook.

8. By working with the regression equation in Part 05 above, repeat Part 04 again to check the significance of AGE in affecting RST. Compare the outcome you obtained in here with the results of part 04 above.

9. Another important factor that could affect the rest time is EXPER. Run the following regression model in GRETL and verify if the OLS can be estimated in this case:

???? = ?0 + ?1??????? + ?2????? + ?3???? + ?4????? + ?5???? + ?6?????? + ??

Do you get the estimated value of ?6? Why or why not? Explain your answer.

10. a. To be able to review the impact of the variable EXPER, using GRETL, construct the model:

???? = ?0 + ?1??????? + ?2????? + ?3???? + ?4?????? + ?5???????? + ??

In which EXPERSQ is the square of the variable EXPER. How did you overcome the issue in Part 09 above in here? Explain. b. Based on your estimated outcome in here, discuss about the shape of the graph relating EXPER and RST (i.e. U-curve, inverted U, etc.). Explain.

ANSWERS FOR 6-8

Model 2: OLS, using observations 1-706 Dependent variable: RST Omitted due to exact collinearity: EXPER coefficient std. error t-ratio p-value const 3923.49 137. 456 28.54 1. 90e-119 *** TOTWRK -0. 192587 0. 0192292 -10.02 3.72e-22 * * AGE 3. 26586 1. 75034 1. 866 0. 0625 * EDUC -16.3153 6. 78161 -2. 406 0. 0164 MARR 10.6595 48. 0436 0. 2219 0. 8245 KID -26. 0354 57.9069 -0. 4496 0. 6531 Mean dependent var 3438. 295 S.D. dependent var 520. 4321 Sum squared resid 1. 63e+08 S.E. of regression 482. 9180 R-squared 0. 145076 Adjusted R-squared 0. 138969 F (5, 700) 23.75721 P-value (F) 4. 19e-22 Log-likelihood 5361. 730 Akaike criterion 10735. 46 Schwarz criterion 10762.82 Hannan-Quinn 10746.03