I need help with all of these

Use the STATA output for Model 1 at the end of this handout to answer the following questions: a. Interpret the regression coefcients in model 1. b. How much would you expect someone to make if they have 10 years of education? C. If someone in your sample has 10 years of education and has a wage = 5, what does their residual equal? Give me one possible reason why a person might have such a residual. (10 pts) Is the slope coefficient in model 1 statistically signicant at the 5 percent level? Explain using the pvalue. (For this problem, you do NOT have to spell out the distribution of the test statistic, etc.) Also be explicit about what we are actually testing- in both the language of hypothesis testing and in everyday English. (9 pts) What is the formula for 31? Based on the available information, what is the variance of education? Given this information, what must the covariance between education and wages be? (9 pts) Given the answer in (e), nd the correlation between education and wages. Then, square it. What does it equal? (Give both the value it is equal to and the name of what it equals.) (10 pts) Give two formulas for the coefficient of determination. Explain the intuitive meaning of each and then show with values from STATA output that they do indeed give you the correct R 2 value. What is 331? What is our estimate of this in our sample? Supplemental Info desc wage educ exper storage display value variable name type format label variable label wage float $9. 0g hourly wage educ byte $8 . 0g years of schooling exper byte $8 . 0g years of workforce experience sum wage educ exper Variable | Obs Mean Std. Dev. Min Max wage 1260 6. 30669 4. 660639 1 . 02 77.72 educ 1260 12. 56349 2. 624489 17 exper 1260 18.20635 11. 96349 0 48 REGRESSION MODEL 1: reg wage educ Source SS df MS Number of obs = 1260 F( 1, 1258) 59 . 40 Model 1232 . 96548 1 1232 . 96548 Prob > F = 0 . 0000 Residual 26114. 4737 1258 20. 7587231 R-squared = 0 . 0451 Adj R-squared = 0 . 0443 Total | 27347 . 4392 1259 21 . 7215561 Root MSE = 4 . 5562 wage Coef. Std. Err. t P>It/ [95% Conf. Interval] educ . 3770664 0489263 7. 71 0 . 000 2810802 4730526 cons 1. 56942 6279439 2 . 50 0 . 013 3374873 2. 801353 REGRESSION MODEL 2 : reg wage exper Source | SS df MS Number of obs = 1260 F( 1, 1258) = 73.29 Model 1505.53875 1 1505.53875 Prob > F = 0 . 0000 Residual 25841 . 9004 1258 20 .5420512 R-squared = 0 . 0551 Adj R-squared = 0 . 0543 Total | 27347 . 4392 1259 21 . 7215561 Root MSE = 4.5323 wage Coef. Std. Err. t P>It| [95% Conf. Interval] exper 0914061 0106771 8. 56 0 . 000 . 0704594 1123529 cons 4 . 642518 . 2325741 19.96 0 . 000 4. 186242 5. 098794