In Table 1, we report the estimated model where Yt is the wageer Cox Xt is the age of them and X2 is the qued value of XL. The most is estimated using Views 1) Watch Video about Quadratie Terms in Extra Videos 2) Do you think the regression is depression to the R2.Fritic, and 3) Calculate the impact of the age on the ways of CEOK Dcum the quadratic elect 4) In Table 2. we recitimate the made without the quadratic term but considering the dependent variable transformed using a parim. Aner watching the Video LegVited in Extra Video comment on the rows. Tefine the marginal effect and the elasticity in the wenge point considering in adition to information you have in Table that the mean of X 58 Dependent YT Metod Last Sum Date10120 Time 01 S2001 2000 C XT 12T R- Add SE ofre Sum Logo Fate Prati Coltu. Ele 2.79047722404 02134 136106 002 0000 -0.092 400E- 696000000 0.1990 Men de 14 De 910143 102 he were 9.7030 1783212 Schwerterton 7598 BOX 482 Harman Quinn 3723627 838482 Duben 1832110 0.000000 DeputeYT Method: Date: 12 Time Sample 2000 Included2 RU A SE of region Susid Logo Prati -1.6. 204.5 0.4504006 0.750 0.003256 17:28 20 Manche 104 1 SD pendant 910143 5098.187 and 2009610 S80 Swat 2013000 1927 221 Harman 2003 221.2100 Durama 0.000000 Table 2 EXERCISE 2 In Table 3 we report the estimated model where GPA is explained by the SAT and the dummy variable Attendance. 1) Watch Video about Dummy variables in Extra Videos 2) Comment if the estimated model is a good model to explain the data. 3) Which kind of dummy we have here? Write the model in case of Attendance = 1 and in case of Attendance = 0, 4) Are residuals normally distributed? Explain. 5) Do you think there is a multicollinearity issue? Dep. Variable GPA R-squared 0.585 Model: OLS Adj. Resguared: 0.555 Method: Least Square F-statistie: 52.70 Date: Fri 11 May 20 Prob (F-statistie): 2.196.15 Time: 16.08 28 Log-Likelihood: 25 798 No. Observations: 84 AIC: 45.60 Df Residuals: 81 BIC: -3830 Of Model: 2 Covariance Type: nonrobust coef stderr + P 0.025 0.976) const 0,0430 0.368 1797 0.070.0.000 1.357 SAT 0.0014 0 000 7.141 0.000 0.001 0.002 Attendance 0 2220 0041 5.451 0.000 0141 0.304 Omnibus: 10.580 Durbin-Watson: 1.000 ProbiOmnibuss: 0.000 Jarque-Bera (JB): 27 180 Skew: -1,028 Prob(JB): 1250-08 Kurtosis: 4801 Cond. No. 335.04 Table 3 In Table 1, we report the estimated model where Yt is the wageer Cox Xt is the age of them and X2 is the qued value of XL. The most is estimated using Views 1) Watch Video about Quadratie Terms in Extra Videos 2) Do you think the regression is depression to the R2.Fritic, and 3) Calculate the impact of the age on the ways of CEOK Dcum the quadratic elect 4) In Table 2. we recitimate the made without the quadratic term but considering the dependent variable transformed using a parim. Aner watching the Video LegVited in Extra Video comment on the rows. Tefine the marginal effect and the elasticity in the wenge point considering in adition to information you have in Table that the mean of X 58 Dependent YT Metod Last Sum Date10120 Time 01 S2001 2000 C XT 12T R- Add SE ofre Sum Logo Fate Prati Coltu. Ele 2.79047722404 02134 136106 002 0000 -0.092 400E- 696000000 0.1990 Men de 14 De 910143 102 he were 9.7030 1783212 Schwerterton 7598 BOX 482 Harman Quinn 3723627 838482 Duben 1832110 0.000000 DeputeYT Method: Date: 12 Time Sample 2000 Included2 RU A SE of region Susid Logo Prati -1.6. 204.5 0.4504006 0.750 0.003256 17:28 20 Manche 104 1 SD pendant 910143 5098.187 and 2009610 S80 Swat 2013000 1927 221 Harman 2003 221.2100 Durama 0.000000 Table 2 EXERCISE 2 In Table 3 we report the estimated model where GPA is explained by the SAT and the dummy variable Attendance. 1) Watch Video about Dummy variables in Extra Videos 2) Comment if the estimated model is a good model to explain the data. 3) Which kind of dummy we have here? Write the model in case of Attendance = 1 and in case of Attendance = 0, 4) Are residuals normally distributed? Explain. 5) Do you think there is a multicollinearity issue? Dep. Variable GPA R-squared 0.585 Model: OLS Adj. Resguared: 0.555 Method: Least Square F-statistie: 52.70 Date: Fri 11 May 20 Prob (F-statistie): 2.196.15 Time: 16.08 28 Log-Likelihood: 25 798 No. Observations: 84 AIC: 45.60 Df Residuals: 81 BIC: -3830 Of Model: 2 Covariance Type: nonrobust coef stderr + P 0.025 0.976) const 0,0430 0.368 1797 0.070.0.000 1.357 SAT 0.0014 0 000 7.141 0.000 0.001 0.002 Attendance 0 2220 0041 5.451 0.000 0141 0.304 Omnibus: 10.580 Durbin-Watson: 1.000 ProbiOmnibuss: 0.000 Jarque-Bera (JB): 27 180 Skew: -1,028 Prob(JB): 1250-08 Kurtosis: 4801 Cond. No. 335.04 Table 3