I will upload the rest of the questions if I could have the work shown as well.

1. Consider the following tables that report regression results from three different models. The dependent variable is earnings, which is measured in dollars. The explanatory variables are height, measured in inches, age, which is measured in years, and educ, which is the number of years of education. Model 1 Source | SS df MS Number of obs 17 , 870 F (1, 17868) 196.46 Model | 1.4086e+11 1 1. 4086e+11 Prob > F 0. 0000 Residual | 1. 2812e+13 17, 868 717020563 R-squared 0. 0109 Adj R-aquared 0. 0108 Total | 1.2953e+13 17,869 724863544 Root NSE 26777 earnings | Coefficient Std. err. P>Itl [95% conf. interval] height 707. 6716 50. 48922 14. 02 0. 900 608 . 7078 806 - 6353 cons -512.7336 3386.856 -0.15 0.880 -7151.299 6125 . 832 Model 2 Source SS df Number of obs 17 , 870 F (2, 17867) 200. 31 Model 2.8406+11 2 1.42032+11 Prob > F 0. 0000 Residual 1. 2669e+13 17,867 709046170 R-squared 0. 0219 Adj R-squared 0. 0218 LOWELL 1. 2953e+13 17, 869 724863544 Root MSE 26628 ruings | Coefficient Sed. ers P>It [95% conf. interval] 739. 6672 50 . 25814 14. 72 0.000 641 . 1564 838 . 178 282. 3089 19. 86539 14 . 21 0.000 CORS -14207.22 3503 - 118 -4 . 06 0.000 -21073.67 -7340. 766n 100% 2 1 2 TT Model 3 Source | df MS Number of obs 17 ,870 F (3, 17866) = 1215.15 Model | 2. 1950e+12 3 7.3167e+11 Prob > F 0.0000 Residual | 1.0758e+13 17,866 602125581 R-squared 0. 1695 Adj R-squared 0. 1693 Total | 1.2953e+13 17, 869 724863544 Root MSE 24538 earnings | Coefficient Std. err. t Poltl [95% conf. interval] height | 441. 5084 46. 61546 9.47 0.000 350. 1376 532.8792 age 333. 2802 18.32877 18. 18 0. 000 297 . 3541 369. 2064 educ 3946. 646 70. 03849 _cons -49740.73 3289 . 248 -15. 12 0. 000 -56187.97 -43293. 48 a. In Model 1, interpret the effect of height on earnings. Is this effect statistically different from ( at the 5% level? Justify your answer. b. In Model 2, interpret the effect of age on earnings. Is this effect statistically different from ( at the 5% level? Justify your answer. c. In Model 2, construct the 95% confidence interval for age. d. In Model 3. construct the 99%% confidence interval for educ, e. In Model 3, interpret the effect of educ on earnings. Is this effect statistically different from 0 at the 176 level? Justify your answer. f. Which of three regression models would you use to evaluate t e effect of height on earnings! Explain