Attached below is the Stata information of WAGE1.raw reg lwage female educ exper tenure Source SS df MS Number of obs 526 11 1I F(4, 521) 84.07 Model 58 . 1853046 14.5463261 Prob > F 3. 0090 Residual 90 . 1444572 521 . 173021991 R-squared 0 . 3923 Adj R-squared 0 . 3876 Total 148.329762 525 . 28253288 Root MSE . 41596 lwage Coefficient Std. err. t P>It| [95% conf. interval] female -. 3011459 . 0372456 -8. 09 0.000 -. 3743158 -. 2279759 educ . 0874623 . 0069389 12 . 60 0.000 0738307 . 101094 exper 0046294 0016271 2 . 85 0. 005 0014328 007826 tenure 017367 . 0029762 5.84 0. 000 .0115201 0232138 cons . 5013479 . 1019024 4.92 0.000 3011579 . 701538 Variables: wage: hourly wage educ: years of educ exper: years of labor market experience tenure: years with current employer female (=1 if female) married (=if married) Note: Assume that experience have a non-linear effect on wage QUESTION: Suppose the wage model, Iwage or log(wage) as dependent variable with educ, exper and tenure as explanatory variables. Given this information, interpret the resulting slope coefficients