5) Consider a regression of the log (infant mortality rate) in each state against the variables listed (10 points) : Variable Meaning linfmort Log (Infant mortality rate) 1pcinc Log (Per capita income) 1physic Log (Doctors per 100, 000 population} 1popul Log (population in 1000s) DC =1 for Washington DC linfmort | Coef. Std. Err. t P>Itl [95% Conf. Interval] 1pcinc - . 2472401 . 0928288 -2.66 0 . 009 -. 4314795 -. 0630006 1physic -. 1756327 . 0809773 -2.17 0.033 -. 3363502 -. 0149152 1popul . 0628281 . 0142942 4. 40 0. 000 . 034458 . 0911981 DC 1 . 136104 . 1305253 8. 70 0. 000 . 8770479 1. 395161 cons 5. 047641 . 704294 7.17 0. 000 3. 649812 6. 445469 Here is the variance covariance matrix from this regression: e (V) I 1pcinc 1physic 1popul DC cons lpcinc | . 00861719 1physic | - . 00476487 . 00655732 1popul | . 00008304 - . 0004003 . 00020432 DC | . 00303542 - . 00681149 . 00076755 . 01703684 cons | - . 05907059 . 01496 - . 00034546 - . 00001664 . 49602999 Calculate the t-statistic that you would use to test the hypothesis that the elasticity of infant mortality with respect to per capita income is the same as its elasticity with respect to the number of physicians per population. Interpret the results