Hw4 - Greg Smith 10/18/2009 Dr. Cuellar Econ 317 Problem...

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Unformatted text preview: Greg Smith 10/18/2009 Dr. Cuellar Econ 317 Problem Set 4 1. Use the data set Wage1.dta to answer the following questions. Estimate regression equation wage = 0 + 1Education + U i. . reg wage educ Source | SS df MS Number of obs = 526-------------+------------------------------ F( 1, 524) = 103.36 Model | 1179.73204 1 1179.73204 Prob > F = 0.0000 Residual | 5980.68225 524 11.4135158 R-squared = 0.1648-------------+------------------------------ Adj R-squared = 0.1632 Total | 7160.41429 525 13.6388844 Root MSE = 3.3784------------------------------------------------------------------------------ wage | Coef. Std. Err. t P>|t| [95% Conf. Interval]-------------+---------------------------------------------------------------- educ | .5413593 .053248 10.17 0.000 .4367534 .6459651 _cons | -.9048516 .6849678 -1.32 0.187 -2.250472 .4407687 ii. Use the R2 and F-test to test for overall significance of the estimate regression. Explain each. a. Adj R-squared = 0.1632 which means that only 16 % of the variation in wage is explained by Education. b. . display invFtail(1, 523, .05) 3.8593004 F( 1, 524) = 103.36 which is larger than our critical F. iii. Interpret each of the coefficients. a. 1 is the change in wage divided by the change in education. iv. Are the coefficients statistically significant? Explain. a. . . display invttail(524, .05/2) 1.9645015 = Crit T Sample T = 10.17 so we reject the null hypothesis and hold education coefficient as statistically significant. Estimate regression equation wage = 0 + 1Education + 2Experience + U . reg wage educ exper Source | SS df MS Number of obs = 526-------------+------------------------------ F( 2, 523) = 75.99 Model | 1612.2545 2 806.127251 Prob > F = 0.0000 Residual | 5548.15979 523 10.6083361 R-squared = 0.2252-------------+------------------------------ Adj R-squared = 0.2222 Total | 7160.41429 525 13.6388844 Root MSE = 3.257------------------------------------------------------------------------------ wage | Coef. Std. Err. t P>|t| [95% Conf. Interval]-------------+---------------------------------------------------------------- educ | .6442721 .0538061 11.97 0.000 .5385695 .7499747 exper | .0700954 .0109776 6.39 0.000 .0485297 .0916611 _cons | -3.390539 .7665661 -4.42 0.000 -4.896466 -1.884613------------------------------------------------------------------------------ v. Use the R2 and F-test to test for overall significance of the estimate regression. Explain each. a. Adj R-squared = 0.2222 which means that 22 % of the variation in wage is explained by experience and education. b. . display invFtail(2, 523, .05) 3.0129575 sample F is F( 2, 523) = 75.99 which is bigger than our critical F value of 3.0129575....
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This note was uploaded on 03/08/2011 for the course ECON 304 taught by Professor Eyler during the Spring '07 term at Sonoma.

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Hw4 - Greg Smith 10/18/2009 Dr. Cuellar Econ 317 Problem...

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