7. To examine the demand for coke in the United States we estimate the following three models: Model (1) QB + B InPi+ U. InQB + BP + U Model (2) InQB+B InP,+ U .... Model (3) Where i indexes individuals, Q is the number bottles of coke consumed per week, P is the price of coke, measured in dollars per bottle, and U is a random disturbance term. The results of this regression are presented below: Model (1) Model (2) R Square Observations 0.706 R Square Observations 0.668 32 32 Explained Sum of Squares 1330 0.366 Residual Sum of Squares Explained Sum of Squares Residual Sum of Squares 554 0.182 Coefficients St. Error Coefficients St. Error Intercept 89.21 4.01 Intercept 4.54 0.07 In P -30.1 3.55 P -0.17 0.02 Model (3) R Square Observations 0.702 32 Explained Sum of Squares 0.384 Residual Sum of Squares 0.163 Coefficients St. Error Intercept 4.58 0.07 In P -9.95 0.07 a. Using the information of all three models interpret , in all models b. using AIC & SIC method, which model would you use? slap 8. A linear earning function was estimated using 1000 observations on wage per hour (S) and gender of worker (if female = 1, if male =0); R 0.0014, RSS-44.95 wage per hour-91.4-2.29 female +1.5 edu-2.5 female.edu, (2.85) (3.6) (1.09) (0.5) a. if all else equal, what is the difference between female wage and male wage, of the estimated relation b. test if a is significant at a = 5%. What is the conclusion? c. test if BB + 1 or not at the 5% significance level where the Residual Sum of Squares of this test= 53 7. To examine the demand for coke in the United States we estimate the following three models: Model (1) QB + B InPi+ U. InQB + BP + U Model (2) InQB+B InP,+ U .... Model (3) Where i indexes individuals, Q is the number bottles of coke consumed per week, P is the price of coke, measured in dollars per bottle, and U is a random disturbance term. The results of this regression are presented below: Model (1) Model (2) R Square Observations 0.706 R Square Observations 0.668 32 32 Explained Sum of Squares 1330 0.366 Residual Sum of Squares Explained Sum of Squares Residual Sum of Squares 554 0.182 Coefficients St. Error Coefficients St. Error Intercept 89.21 4.01 Intercept 4.54 0.07 In P -30.1 3.55 P -0.17 0.02 Model (3) R Square Observations 0.702 32 Explained Sum of Squares 0.384 Residual Sum of Squares 0.163 Coefficients St. Error Intercept 4.58 0.07 In P -9.95 0.07 a. Using the information of all three models interpret , in all models b. using AIC & SIC method, which model would you use? slap 8. A linear earning function was estimated using 1000 observations on wage per hour (S) and gender of worker (if female = 1, if male =0); R 0.0014, RSS-44.95 wage per hour-91.4-2.29 female +1.5 edu-2.5 female.edu, (2.85) (3.6) (1.09) (0.5) a. if all else equal, what is the difference between female wage and male wage, of the estimated relation b. test if a is significant at a = 5%. What is the conclusion? c. test if BB + 1 or not at the 5% significance level where the Residual Sum of Squares of this test= 53