Time Left:0:55:57 Madison Williams: Attempt 1 You use the above variables to regress in_poverty on a function of age, education, female and nonwhite, and you find the following results (note the quadratic terms and dummy variable interactions in the model) : . reg in_poverty age agesq female nonwhite nonw_female educ educsq if age>18 & age F 0.0800 Residual 5700. 60627 56,546 . 106813608 R-squared 0. 0551 Adj R-squared 0. 0550 11 Total 6033. 18296 56, 553 .106681926 Root MSE .31751 in_poverty Coef. Std. Err. t Poltl [95% Conf. Interval] age -. 0102299 .0007482 -13.67 0.080 -0116963 -. 0087635 agesq 0601037 8.80e-06 11.79 0.0co 0060865 060121 female .0397521 .0032806 12 . 12 0. 080 033322 0461821 nonwhite 0374338 0041774 8.96 0.080 029246 0456215 nonw_female 025706 . 8056767 4.53 0.080 0145796 0368323 educ 0840194 0016982 -2.37 0. 018 10073479 .0806909 educsq .0806778 0080686 -9 . 88 0.000 .0008122 -.6065433 _cons 4989025 . 0187705 26.58 0.080 4621122 5356928 How do you describe the impact of education on the propensity to have income below the poverty level? At low levels of education an increase in education increases the propensity to be in poverty: and increases in education at high levels cause a decrease in the propensity to be in poverty Education only impacts poverty for nonwhite females. Higher levels of education always increase the propensity to be in poverty. Higher levels of education always decrease the propensity to be in poverty. Question 18 (10 points) 1) Listen Using the same regression model, . reg in_poverty age agesq female nonwhite nonw_female educ educsq if age 18 & age