Problem 2: By using stata, we estimate the equation log(wage)-80 + 1 educ + -exp
ID: 3359942 • Letter: P
Question
Problem 2: By using stata, we estimate the equation log(wage)-80 + 1 educ + -exper + 3tenure +u, and obtain the following results. Interpret the numbers in the red frame , reg logwage educ exper tenure Source df MS 526 = 80.39 0 , 0000 -0.3160 Number of obs F( 3, 522) 3 15.6247259 Model Residual 46.8741776 101.455574 522194359337 R-squared Total 148.329751 525. 28253286 Root MSE 44086 logwage Coef Std. Err [95 Conf. Interval] educ exper tenure cons 092029 0041211 0220672 .2843595 0073299 0017233 0030936 .1041904 12.56 0.000 2.39 0.017 7.13 0. 000 2.73 0.007 0776292 0007357 0159897 0796756 .1064288 0075065 0281448 .4890435Explanation / Answer
In the ANOVA table, the treatment sum of squares is 46.87 and sum of squares of the residual error is 101.46.
The F statistic is equal to 80.39. The distribution is F(3, 522), and the probability of observing a value greater than or equal to 80.39 is approximately 0.000. There is strong evidence that slope is not equal to zero.
The r² term is equal to 0.3160, indicating that 31.6% of the variability in the response (log wage) is explained by the explanatory variable.
The p-values for all variables is less than 0.05 indicate that all explanatory variables are significant predictor of response variable (log wage).
As education increases by 1 year, wage increases by 1.09 unit.
As experience increases by 1 year, wage increases by 1.12 unit.
As tenure increases by 1 unit, wage increases by 1.02 unit.
As _cons increases by 1 unit, wage increases by 1.33 unit.
Coef. anti log educ 0.092029 1.096397 exper 0.114121 1.120888 tenure 0.022067 1.022312 _cons 0.28436 1.328911Related Questions
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