You are interested in the factors that affect hourly wages. You review within 5 tables below several factors including experience ("exper") measured in number of years, the age of the person ("age") measured in number of years, if the person is a woman ("female" is an indicator variable equal to 1 if the person is a woman, 0 if it is a man), marital status ("married" is an indicator variable equal to 1 if the person is married, 0 otherwise). The term "female_married" is the interaction term between "female" and "married".

Tableau 1 Source SS df MS Number of obs 3, 003 F (2, 3000) II 332 . 37 Model 37674310. 1 2 18837155.1 Prob > F 0 . 0000 Residual 170026110 3, 000 56675.3699 R-squared 0 . 1814 Adj R-squared 0 . 1808 Total 207700420 3, 002 69187 .3484 Root MSE = 238. 07 wage Coef. Std. Err. t P>/t| [95% Conf. Interval] exper -29 . 96673 1. 623029 -18.46 0 . 000 -33 . 14909 -26. 78437 age 55 . 14512 2. 144062 25.72 0. 000 50 . 94114 59 . 3491 cons -708. 019 50. 38585 -14 .05 0 . 000 -806 . 8133 -609. 2246 Tableau 2 Source SS df MS Number of obs 3, 003 F (4, 2998) 186 . 49 Model 41383221.2 10345805.3 Prob > F 0 . 0000 Residual 166317199 2,998 55476 . 0503 R-squared II 0 . 1992 Adj R-squared 0 . 1982 Total 207700420 3, 002 69187 . 3484 Root MSE 235.53 wage Coef. Std. Err. t P>It| [95% Conf. Interval] exper -30. 89045 1. 609811 -19. 19 0 . 000 -34 . 04689 -27 . 734 age 53 . 46494 2. 131259 25 . 09 0. 000 49 . 28607 57 . 64382 female -5 . 200696 8 . 600253 -0 . 60 0 . 545 -22 . 06369 11. 6623 married -17 . 45669 2 . 14122 -8 . 15 0 . 000 -21 . 6551 -13 . 25828 _cons -610 . 4099 51 . 35612 -11. 89 0 . 000 -711 . 1067 -509. 7131Tableau 3 Source SS df MS Number of obs 3, 003 11 1I F (5, 2997) 149 . 17 Model 41389884.8 5 8277976 . 96 Prob > F 0 . 0000 1III Residual 166310535 2,997 55492 . 3374 R-squared 0 . 1993 Adj R-squared II 0 . 1979 Total 207700420 3, 002 69187.3484 Root MSE 235. 57 wage Coef. Std. Err. t P>|t| [95% Conf. Interval] exper -30 . 88798 1. 610063 -19. 18 0. 000 -34. 04492 -27 . 73104 age 53 . 48228 2. 132158 25 . 08 0 . 000 49 . 30164 57 . 66292 female -9. 783546 15 . 77618 -0. 62 0. 535 -40 . 71679 21 . 14969 married -16. 7874 2. 883839 -5. 82 0 . 000 -22 . 44191 -11 . 1329 female_married 6 . 423353 18.53631 0 . 35 0. 729 -29. 92183 42 . 76853 _cons -612 . 4383 51 . 69612 -11 . 85 0. 000 -713 . 8018 -511 . 0748 Tableau 4 Source SS df MS Number of obs = 3, 003 F (3, 2999) 221. 66 Model 37695934.9 3 12565311. 6 Prob > F 11 11 1I 0 . 0000 Residual 170004485 2, 999 56687 . 0574 R-squared 0 . 1815 Adj R-squared 0 . 1807 Total 207700420 3, 002 69187.3484 Root MSE 238 . 09 wage Coef. Std. Err. t P>It| [95% Conf. Interval] exper -29 . 96227 1. 623212 -18 . 46 0 . 000 -33 . 14499 -26. 77954 age 55 . 14635 2 . 144284 25 .72 0 . 000 50. 94193 59 . 35077 female -5. 369485 8. 69359 -0 . 62 0 . 537 -22 . 41549 11 . 67652 _cons -705 . 488 50. 55738 -13.95 0 . 000 -804 . 6187 -606.3574 Tableau 5Source SS df MS Number of obs = 3 , 003 F (3, 2999) 248.59 1 Model 41362934.8 3 13787644.9 Prob > F 0 . 0000 Residual 166337485 2, 999 55464.3165 R-squared 0 . 1991 Adj R-squared = 0 . 1983 Total 207700420 3, 002 69187. 3484 Root MSE = 235. 51 wage Coef. Std. Err. t P> |t| [95% Conf. Interval] exper -30 . 89494 1 . 609624 -19 . 19 0 . 000 -34 . 05102 -27 . 73886 age 53 . 46345 2. 131032 25 . 09 0 . 000 49 . 28502 57 . 64188 married -17 . 45981 2. 140987 -8 . 16 0 . 000 -21 . 65776 -13 . 26186 _cons -612 . 8442 51 . 19268 -11. 97 0 . 000 -713 . 2206 -512 . 4679