NAME: Don't forget your name! WARNING: Be careful when you enter the letter answers. Any extra character in the cell, including a blank space, will cause your answer to not match the key and Excel will enter a zero as the score for that question. 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 1 Consider the following regression functions: I II III E(PRICE) = ( + ) + ( + )SQFT E(PRICE) = + ( + )SQFT E(PRICE) = + SQFT The regression equation E(PRICE) = ( + ) + ( + )SQFT contains: a a slope indicator variable but not a intercept dummy variable b an intercept dummy variable but not an slope indicator variable c both an intercept indicator variable and a slope dummy variable d neither a slope dummy variable nor a intercept dummy variable 2 The reference group of the regression model defines a the categories that do not exhaust the sample space b the group with all dummy variables equal to zero c the group including all indicator variables, thereby avoiding the dummy variable trap (DVT) d the category of all continuous variables included in the regression model 3 When you have a multiple regression model with a binary dependent variable, the model is a, a dichotomous model b Bernoulli model c Binary model d Linear Probability model A large company is accused of gender discrimination in wages. The following model has been estimated from the company's human resources information ln(WAGE) = + EDUC + EXPER + MALE where WAGE is hourly wage, EDUC is years of education, EXPER is years of relevant experience, and MALE is the gender. The estimated regression coefficients are: (These numbers are pre-calculated) Intercept EDUC EXPER MALE b_j 1.4534 0.0842 0.0517 0.1951 4 Let EDUC = 16 EXPER = 10 Find the predicted wage of a male employee with the above characteristics. a 36.2904 b 35.3715 c 33.5617 d 32.8855 e 31.5701 5 Find the predicted wage of a female employee with the same years of education and experience. a 31.0777 b 30.1725 c 29.2937 d 28.4405 e 27.6121 6 Find the percent difference in the wage of a male employee over the wage of a female employee. a 22.6191% b 21.5471% c 20.6853% d 19.8578% e 18.6664% 7 Find the percent difference in the wage of a male employee with 11 years of experience over the wage of a female employee with 10 years of experience. a 27.9979% b 28.8379% c 29.7030% d e 30.5941% 31.5119% Using the data in the WAGE tab, run the following regression model: E(ln(WAGE)] = + EDUC + EXPER + EXPER + FEMALE + BLACK + MARRIED + SOUTH + FULLTIME + METRO EDUC EXPER FEMALE BLACK MARRIED SOUTH FULLTIME METRO 8 Find the estimated coefficients of the regression equation. Intercept EDUC EXPER EXPER FEMALE BLACK MARRIED SOUTH FULLTIME METRO 9 years of schooling years of experience = 1, if female, 0 if male = 1, if black, 0 otherwise = 1 if married, 0 if single = 1, lives in SOUTH, 0 otherwise =1, if fulltime, 0, otherwise =1, if lives in metropolitan area, 0 otherwise a b 0.95748 0.95513 0.09432 0.09047 0.03007 0.03019 -0.00044 -0.00044 -0.21911 -0.21693 -0.07436 -0.08500 0.07634 0.07467 -0.00474 0.00056 0.24395 0.24843 0.13269 0.14243 c 0.92721 0.08686 0.03049 -0.00044 -0.21609 -0.08639 0.06922 -0.01136 0.24855 0.15488 d 0.92552 0.08905 0.03167 -0.00047 -0.21761 -0.09267 0.06847 -0.00298 0.23633 0.15526 e 0.90701 0.08827 0.02534 -0.00046 -0.19803 -0.09174 0.07052 -0.00307 0.24342 0.15992 Find the predicted wage of a person with the following characteristics: The person is a single white male who works fulltime and lives in a metropolitan area outside the South, and has 16 years of education with 10 years of experience. a 22.2007 b 21.1435 c 20.3303 d 19.7204 e 18.7344 10 Find the predicted wage of a person with the following characteristics: The person is a single black male who works fulltime and lives in a metropolitan area in the South, and has 16 years of education with 10 years of experience. a 14.8853 b 15.8355 c 16.8462 d 17.9215 e 18.4758 11 Find the percent difference in the wage of the person in question 10 relative to the wage of the person in question 9. a -8.097% b -9.122% c -10.034% d -11.038% e -12.141% The data set INCINERATOR was used to study the effect that a new garbage incinerator had on housing values in North Andover, MA. The rumor that a new incinerator would be built in North Andover began after 1978, and construction began in 1981. The data show the price of houses that sold in 1978, before the rumor began, and those sold in 1981, when the construction started. The hypothesis is that the price of houses located near the incinerator would fall compared to the price of more distant houses. The data represent PRICE NEAR YEAR81 price of the house 1, house is near the incinerator; 0, otherwise 1981 = 1; 1978 = 0 The objective is to determine how the average price of houses near the incinerator changed between 1978 and 1981. 12 Find the pre-rumor (1978) average price of houses away from the incinerator. a 82093 b 83406 c 84741 d 86097 e 87474 13 Find the pre-rumor (1978) average price of houses near the incinerator. a 76378 b 75175 c 74431 d 73694 e 72965 14 Find the 1981 average price of houses away from the incinerator. a 100450 b 99455 c 98470 d 97486 e 96511 15 Find the 1981 average price of houses near the incinerator. a 74180 b 75293 c 76046 d e 76806 77574 16 Find the treatment effect (the impact of building the incinerator on the price of houses near the facility). a -18058 b -17036 c -16071 d -15162 e -14555 17 Run the regression and show the estimated coefficient from the regression output. a b c d e 83406.5 82093.0 84741.0 86096.8 87474.4 -10503.8 -9128.5 -8874.1 -7864.2 -8021.5 17698.4 16377.4 17010.8 16377.4 13897.8 -14555.2 -15161.6 -16071.3 -17035.6 -18057.8 WAGE 19.77 20 8.7 15 20 9 13.75 6.67 29.76 20.45 22.13 14.6 10 18.24 48.08 22 22.3 33 22.22 24.05 37.22 20.38 7.29 33.5 9 11.53 21.15 10 16 17 14.43 10 29.75 20 12.5 16.5 23.08 24.03 12.5 41.21 9.5 64.11 46.63 12 15 13 16.24 14.25 8.65 30.45 EDUC EXPER 21 16 12 16 14 16 16 3 13 12 16 12 12 21 14 11 16 14 13 16 14 16 14 12 12 16 18 16 13 13 12 12 12 16 12 16 13 16 13 16 13 16 18 10 16 13 14 12 16 16 EXPER 33 25 44 6 24 4 11 23 41 23 12 28 11 8 32 27 24 36 21 27 9 39 47 13 60 17 8 23 17 9 36 42 22 10 15 33 22 35 7 36 53 32 28 28 28 8 37 35 14 13 1089 625 1936 36 576 16 121 529 1681 529 144 784 121 64 1024 729 576 1296 441 729 81 1521 2209 169 3600 289 64 529 289 81 1296 1764 484 100 225 1089 484 1225 49 1296 2809 1024 784 784 784 64 1369 1225 196 169 FEMALE BLACK 0 1 1 1 0 1 0 0 0 0 0 1 0 1 0 0 1 0 0 0 0 0 1 0 1 1 0 0 0 0 1 1 0 1 0 1 1 0 0 0 1 0 0 0 1 1 0 0 0 0 MARRIED 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 SOUTH 1 1 1 0 0 0 1 0 1 1 0 0 0 0 1 1 0 1 0 1 0 1 0 1 0 0 0 0 1 0 1 1 0 1 1 1 1 1 0 1 1 1 1 1 1 0 1 0 1 1 FULLTIME 1 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 1 1 0 0 1 0 0 0 0 0 1 0 0 0 0 0 1 0 0 1 0 1 0 1 0 1 0 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 0 1 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 0 1 1 26.07 30 18 12.38 13 15.38 42.72 16.18 22.48 15.04 13.88 42.73 14 16.25 14.03 8 17.19 21 9.1 20.5 29.45 12.25 16.25 10 20.02 24.04 16.35 20.18 10 28.38 2.69 50 9.5 9.62 30 40 16.08 23.84 14.5 9.54 16 18.75 16 45 14.88 15 26.25 9 38.51 9.23 9.33 12 16 12 12 12 12 14 16 13 16 3 21 12 16 12 12 18 14 13 16 14 13 12 12 18 21 16 18 16 12 12 13 13 13 12 13 18 12 11 13 12 16 18 13 13 12 16 12 16 18 13 42 16 30 43 28 32 34 24 33 13 64 30 29 33 50 8 47 19 16 16 11 6 10 8 9 16 13 38 32 35 7 18 6 10 40 17 36 43 39 21 17 17 16 45 13 29 12 9 22 49 52 1764 256 900 1849 784 1024 1156 576 1089 169 4096 900 841 1089 2500 64 2209 361 256 256 121 36 100 64 81 256 169 1444 1024 1225 49 324 36 100 1600 289 1296 1849 1521 441 289 289 256 2025 169 841 144 81 484 2401 2704 0 1 1 1 1 1 1 1 0 0 0 1 1 1 1 0 0 0 1 0 0 1 0 0 0 0 0 1 1 1 1 1 1 0 0 0 0 1 0 1 0 0 1 0 1 1 0 0 0 1 1 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 1 0 0 0 0 0 0 0 0 1 1 1 0 0 1 1 1 0 0 1 0 1 0 1 0 1 1 1 0 0 0 1 1 1 0 1 1 0 1 0 0 1 0 0 0 1 0 1 1 1 0 1 0 0 0 1 0 0 1 1 0 0 1 0 0 0 0 0 1 1 0 0 1 0 1 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 1 0 0 0 0 0 1 1 0 0 0 0 0 1 1 1 1 1 1 1 0 1 1 0 1 1 1 0 1 0 1 0 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 0 1 1 1 0 1 1 1 1 1 1 0 18.28 13.45 33.51 14.38 12.5 30.77 6.77 27 7.5 15.75 20.83 7.57 22.5 13 8.25 52.88 8 32.68 21.63 9.24 19.1 35 9.75 6.75 14 16 19.22 28.85 25 9 14 10.49 20.04 14.16 10.5 2.83 15.38 25 24.05 23.69 7.25 19.75 5.71 17.5 12 25.48 10.3 16.73 11.85 20 28 14 12 16 14 16 21 12 12 13 14 18 13 12 12 12 18 14 14 16 16 12 16 10 13 13 11 12 16 12 13 12 13 13 12 13 16 21 12 16 21 11 16 12 12 12 21 12 16 12 13 12 34 14 15 18 22 29 23 33 44 18 24 6 34 42 27 45 18 40 12 12 39 22 25 5 48 25 41 6 34 54 11 25 30 6 41 4 6 39 20 21 12 18 25 11 30 17 42 7 65 31 30 1156 196 225 324 484 841 529 1089 1936 324 576 36 1156 1764 729 2025 324 1600 144 144 1521 484 625 25 2304 625 1681 36 1156 2916 121 625 900 36 1681 16 36 1521 400 441 144 324 625 121 900 289 1764 49 4225 961 900 1 1 1 0 1 0 1 0 0 0 1 1 1 0 0 0 1 0 1 1 0 1 1 1 1 1 1 0 0 1 0 1 0 0 0 0 1 0 0 1 1 1 0 0 1 0 1 1 1 0 0 1 1 0 0 0 0 0 1 1 1 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 1 0 1 0 0 0 0 1 0 1 1 1 1 1 1 1 1 1 1 1 1 0 0 1 1 1 1 0 1 0 0 0 1 0 0 1 1 1 0 0 0 1 0 1 1 0 1 1 1 1 1 0 1 0 0 0 1 1 1 1 0 0 0 0 0 0 1 1 0 0 1 1 0 0 1 1 0 1 0 1 1 1 1 1 0 0 0 1 0 0 0 0 1 0 1 1 1 0 0 0 0 0 1 0 1 0 1 1 0 1 1 1 1 0 1 0 1 1 1 0 0 1 1 1 1 1 1 1 1 1 0 1 0 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 0 0 1 1 1 1 1 1 1 1 1 14 25 22.5 12 12.83 24.53 12.98 13 23.55 9.85 21.25 21.16 24.15 20.18 34.63 28.85 13 76.39 28.85 25 17.3 37.5 15.48 38.27 16.83 17.5 41.03 9.7 100 25 21.29 27 61.11 12.75 25.63 14.91 21.25 12.05 21.15 27.78 7.7 16 14 15 15 6.94 35.08 15 14 38.45 16.88 18 14 12 12 12 13 13 14 16 16 8 14 13 12 12 16 13 14 12 12 12 16 14 21 16 12 13 13 18 16 12 18 18 10 12 16 14 12 14 16 12 12 12 13 13 12 16 12 12 16 16 38 34 22 31 33 30 31 27 14 16 22 27 24 39 37 27 30 20 19 30 11 30 17 22 13 34 38 8 46 34 38 32 22 31 23 38 10 15 21 38 38 38 30 25 22 13 8 14 18 37 5 1444 1156 484 961 1089 900 961 729 196 256 484 729 576 1521 1369 729 900 400 361 900 121 900 289 484 169 1156 1444 64 2116 1156 1444 1024 484 961 529 1444 100 225 441 1444 1444 1444 900 625 484 169 64 196 324 1369 25 1 0 0 1 0 0 1 1 0 1 0 0 1 1 1 1 1 1 1 0 0 1 1 0 1 0 0 0 0 0 1 1 1 0 1 1 1 0 0 0 1 0 0 1 1 1 1 0 1 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 0 1 1 1 1 0 1 0 1 1 0 1 1 1 1 0 1 1 0 0 0 1 0 1 1 1 0 1 0 0 1 0 1 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 1 0 0 0 1 0 0 1 1 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 0 1 1 0 1 1 1 1 1 1 0 1 1 1 1 1 1 1 0 1 0 1 1 1 1 1 39.9 20 12 15.71 7 8.25 7 9.15 27.5 24.05 7.4 27.88 8.5 14.08 17.49 28.85 15.65 10.5 35.17 8.48 20.68 45.33 9.5 14.29 50 13.45 18.06 64.11 31.25 16.16 33.65 10.53 16 41.02 15 12.87 4.17 12.5 14.78 13.97 11.25 29.4 24.79 14.43 40.88 10.58 13.45 25.63 17.1 12.7 18.75 14 16 13 12 12 14 6 14 14 18 8 18 14 13 16 14 13 12 18 13 12 13 12 16 16 12 12 18 18 12 14 12 12 16 21 12 11 14 16 14 13 21 12 13 16 6 12 18 13 14 8 28 19 37 30 15 15 35 17 32 16 16 32 4 24 29 10 39 21 26 23 16 24 31 4 28 33 35 21 19 22 25 15 12 34 18 48 21 32 26 40 10 31 32 9 14 17 10 40 43 29 22 784 361 1369 900 225 225 1225 289 1024 256 256 1024 16 576 841 100 1521 441 676 529 256 576 961 16 784 1089 1225 441 361 484 625 225 144 1156 324 2304 441 1024 676 1600 100 961 1024 81 196 289 100 1600 1849 841 484 1 0 1 1 1 1 0 1 1 0 0 1 1 1 0 0 0 1 1 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 1 0 1 1 1 0 1 0 0 1 0 1 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 1 1 1 0 1 0 1 1 0 1 0 0 1 1 1 0 1 1 1 1 1 1 0 1 0 1 0 0 1 1 0 1 1 1 1 1 1 0 1 1 1 1 1 1 1 0 1 1 1 1 0 0 0 0 0 1 0 0 0 1 1 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0 1 1 1 0 0 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 1 1 1 1 1 1 0 0 0 1 1 1 0 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 0 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 17.7 24.05 23.95 7.5 15.11 13.33 7.5 12.5 20 10.49 23.75 21.63 20.68 27.83 4.79 12.5 20 17.3 13.58 9 10.52 21.63 8.75 12.5 10 36.67 8.6 8.95 19.17 28 34.63 25.63 36.06 8.81 11.75 10.5 8.52 10 11.5 14 10.94 12.5 20.33 29.91 10 16 6.5 36.55 18 9 8 16 16 18 14 13 13 13 12 18 14 18 16 16 14 12 12 12 16 16 18 13 13 16 12 13 18 12 12 12 16 14 14 21 12 12 13 12 12 13 13 16 13 14 16 13 13 10 14 10 13 13 20 38 30 17 35 13 6 39 5 14 9 42 19 6 43 42 12 10 28 43 7 43 35 8 22 25 26 29 57 17 19 24 32 26 14 11 28 31 45 13 9 13 41 42 10 17 63 29 44 10 11 400 1444 900 289 1225 169 36 1521 25 196 81 1764 361 36 1849 1764 144 100 784 1849 49 1849 1225 64 484 625 676 841 3249 289 361 576 1024 676 196 121 784 961 2025 169 81 169 1681 1764 100 289 3969 841 1936 100 121 0 0 0 1 0 0 1 0 0 0 1 1 0 0 0 0 0 0 1 1 0 1 0 0 1 1 0 1 1 0 1 0 1 1 1 1 1 1 1 0 1 1 1 0 1 1 1 1 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 1 0 0 0 0 1 1 1 0 1 0 0 0 0 1 1 0 1 1 0 1 1 0 0 0 0 0 0 1 1 0 1 1 1 0 0 1 1 1 1 1 0 0 0 0 1 1 1 1 0 1 0 1 1 1 0 0 0 0 0 0 1 0 0 1 0 0 1 0 0 0 0 1 1 1 1 0 0 1 0 1 0 0 0 1 0 1 0 0 0 0 0 0 1 1 0 1 0 0 1 0 1 1 1 1 0 0 1 1 1 1 1 1 0 1 1 1 1 1 1 1 0 1 1 1 1 1 0 1 1 1 1 1 1 1 0 1 1 1 1 1 0 1 0 1 1 1 0 1 0 1 0 1 0 1 1 1 0 31.25 35.23 17 16.83 18.24 29.9 20.96 31.25 11 19.88 19.57 9.5 26.78 12.26 41.67 24.05 16.24 21.15 20 19.23 16 20.98 21.63 28.83 21.63 24.05 15.38 8.03 11.55 15.13 32.7 16.25 8 31.73 13.75 19.25 9.5 20.53 9 10 11 72.13 5.97 8 7.5 42.3 47.13 11 12.82 9.5 19.23 12 16 13 13 18 14 21 12 10 16 16 13 16 16 16 18 16 16 13 12 16 16 12 16 18 18 16 16 21 12 18 12 12 18 12 16 12 14 18 18 12 16 12 13 13 12 16 12 12 12 12 39 42 5 12 11 23 21 21 34 27 13 35 5 23 15 15 36 14 42 21 23 18 39 19 19 30 34 8 17 32 34 42 8 29 32 9 30 38 44 56 8 27 43 21 7 19 17 6 21 8 41 1521 1764 25 144 121 529 441 441 1156 729 169 1225 25 529 225 225 1296 196 1764 441 529 324 1521 361 361 900 1156 64 289 1024 1156 1764 64 841 1024 81 900 1444 1936 3136 64 729 1849 441 49 361 289 36 441 64 1681 0 0 1 0 1 0 0 0 1 1 1 0 1 1 0 1 0 0 0 0 1 0 1 1 1 1 1 0 0 0 0 1 1 1 0 1 1 1 0 0 1 0 1 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 1 1 0 1 0 0 1 1 0 1 1 0 0 1 1 1 1 0 1 1 0 1 0 1 1 0 1 0 1 0 1 0 0 1 1 1 1 1 0 1 0 1 0 0 0 1 1 0 0 0 0 1 0 0 1 0 1 0 0 0 0 0 1 1 0 0 0 0 1 0 1 1 0 0 0 0 1 1 0 0 0 0 0 0 1 1 0 0 0 0 0 1 0 1 0 1 0 1 1 0 0 0 1 1 1 1 1 1 1 1 1 1 0 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 1 1 1 1 10.25 28 22.7 15.38 8.53 10.5 43.8 21.63 10.5 9.35 23.08 12.5 11.25 41.35 2.52 26.1 9.28 8 8 7.7 9 10.67 15.25 25 10 11.27 17.3 14 28.83 20.68 57.68 9 17.5 15 21.63 20 11.5 17.3 9.13 9.5 50.03 13.75 23 7.5 7 17.75 38.45 3 14.63 34.63 7.5 6 14 18 13 12 12 21 18 12 13 12 12 12 18 12 12 12 14 13 3 12 12 14 18 12 12 16 12 16 13 16 12 16 13 18 13 12 12 14 13 13 12 13 13 11 12 16 8 14 18 16 40 27 29 16 17 27 33 32 41 42 23 37 32 44 22 10 46 33 5 19 33 26 20 20 23 12 12 26 33 32 13 9 24 21 40 41 37 19 14 46 39 23 37 47 34 22 10 64 25 39 4 1600 729 841 256 289 729 1089 1024 1681 1764 529 1369 1024 1936 484 100 2116 1089 25 361 1089 676 400 400 529 144 144 676 1089 1024 169 81 576 441 1600 1681 1369 361 196 2116 1521 529 1369 2209 1156 484 100 4096 625 1521 16 0 0 0 0 1 0 0 0 1 1 0 1 0 1 1 0 1 1 1 0 1 1 1 1 1 0 0 0 1 0 0 0 0 0 1 1 1 0 0 1 1 0 1 0 1 0 0 1 1 0 1 0 0 0 0 0 0 0 0 1 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 1 0 0 0 1 1 1 1 1 1 1 1 1 1 0 1 1 0 0 0 1 1 1 1 0 0 0 1 1 0 0 0 0 0 1 1 1 0 1 0 0 0 0 0 1 0 0 0 1 1 1 1 0 0 0 0 0 1 1 0 0 0 0 1 0 0 0 0 0 1 0 0 0 1 0 0 1 0 0 0 0 0 1 1 0 0 0 0 1 1 0 0 0 1 0 1 0 0 1 1 1 1 1 1 1 0 1 1 1 0 1 1 1 1 1 0 1 0 0 0 1 0 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 0 0 1 1 0 1 1 1 28.85 8 7.54 10.71 17.49 15.39 11.6 12.08 18.75 12.66 17.57 25 14.29 10 8.66 14.8 21.52 24.58 16.75 14.86 20.2 43.25 36.52 47 26 9.63 14.87 35.49 50.95 10 13.73 16 28.62 9 9 9 173 47.06 38.64 39.5 14.38 15.63 16.93 24.5 34.96 30.33 12.86 17 14.5 10.76 12 13 13 14 12 14 12 6 12 14 16 13 18 12 13 13 12 18 10 13 18 18 18 16 16 14 12 12 18 18 18 13 14 21 12 14 13 14 14 16 16 12 12 16 12 16 18 12 13 14 16 13 48 5 4 23 16 41 33 24 14 24 50 34 36 30 28 45 22 36 23 3 12 8 30 27 29 31 27 9 22 11 26 4 25 44 19 8 38 27 20 20 32 31 8 10 8 34 34 15 22 14 36 2304 25 16 529 256 1681 1089 576 196 576 2500 1156 1296 900 784 2025 484 1296 529 9 144 64 900 729 841 961 729 81 484 121 676 16 625 1936 361 64 1444 729 400 400 1024 961 64 100 64 1156 1156 225 484 196 1296 0 0 1 0 0 0 1 0 1 1 0 1 0 1 1 0 0 0 0 1 0 1 0 0 0 1 1 0 0 1 0 0 0 0 1 0 0 1 1 1 1 0 1 1 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 1 1 1 1 1 0 1 1 1 0 1 0 1 1 0 0 0 1 0 0 1 1 1 1 0 1 0 1 0 1 1 1 0 1 1 1 1 0 0 0 1 0 1 1 0 1 0 1 0 0 0 1 1 1 1 0 0 0 0 0 0 0 1 0 0 0 1 0 1 1 1 0 0 0 0 0 0 1 0 1 1 1 0 0 0 0 0 1 0 1 0 0 0 0 0 0 1 1 0 1 0 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 0 0 1 1 1 1 1 0 1 1 1 1 1 1 1 8.59 16 7 13.33 15 20 6.25 48.08 19.23 30.76 18.75 25.48 18.14 2.55 11.05 8.2 26.45 9 17 10.2 51.9 35.58 12 29.08 17.88 24.17 38.45 13.23 19.23 10.38 20.27 10 20 32 17.86 18.5 20.63 19.23 8 13.5 19.23 14.25 6.35 9 27.88 23.51 13.5 42.11 11 52.7 30.9 10 14 12 13 14 14 21 21 12 16 12 14 16 21 14 12 16 14 13 12 18 21 14 13 16 16 18 16 12 13 16 14 13 11 12 13 13 13 13 14 16 12 12 11 18 16 12 16 12 13 16 45 17 32 45 20 13 11 14 33 19 29 18 13 20 19 37 12 8 28 26 43 30 24 26 15 23 28 12 53 24 7 29 38 36 45 37 34 17 29 11 37 46 17 29 23 27 7 44 28 35 35 2025 289 1024 2025 400 169 121 196 1089 361 841 324 169 400 361 1369 144 64 784 676 1849 900 576 676 225 529 784 144 2809 576 49 841 1444 1296 2025 1369 1156 289 841 121 1369 2116 289 841 529 729 49 1936 784 1225 1225 1 0 1 1 0 0 1 0 1 1 0 1 0 1 0 1 0 1 1 0 0 0 0 1 1 0 1 1 0 1 0 1 0 0 0 0 1 1 1 1 1 0 1 1 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 1 1 0 1 1 1 1 0 1 1 1 1 0 1 1 1 1 1 1 1 1 1 0 1 0 0 0 1 1 0 0 0 0 0 1 1 1 0 0 1 1 1 1 1 0 0 1 1 0 1 0 0 0 1 0 0 0 1 0 0 0 1 0 0 0 1 0 1 0 1 0 0 0 1 0 1 1 1 0 1 1 0 0 0 1 0 0 1 0 0 0 1 1 0 0 1 0 1 0 1 1 0 1 1 0 1 1 1 1 1 0 1 1 1 0 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 5.33 12.83 17 17 10.5 38.08 22.5 21.76 33.65 17.3 45.5 15.88 16.25 10.76 19.24 12 9.58 10 27.7 7.81 14.69 19.88 41.67 11.2 17 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12 12 9 13 12 13 12 12 13 13 14 12 13 18 16 12 14 12 13 12 13 10 33 23 21 39 38 50 44 11 13 34 15 48 9 37 24 42 28 26 6 35 18 39 12 17 5 16 51 23 19 26 49 54 13 45 42 34 20 12 48 27 20 26 22 19 22 40 28 17 38 31 100 1089 529 441 1521 1444 2500 1936 121 169 1156 225 2304 81 1369 576 1764 784 676 36 1225 324 1521 144 289 25 256 2601 529 361 676 2401 2916 169 2025 1764 1156 400 144 2304 729 400 676 484 361 484 1600 784 289 1444 961 0 0 0 0 0 1 1 1 0 1 0 1 1 0 0 0 1 1 1 0 1 0 1 1 0 0 0 1 0 1 1 1 1 1 1 0 0 0 0 0 1 1 0 0 1 1 1 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 1 1 0 1 0 0 0 0 0 0 1 0 1 0 1 1 1 1 0 1 1 1 1 1 1 1 1 0 0 0 1 1 1 1 1 1 1 0 1 0 0 0 0 0 0 1 1 1 0 0 1 1 0 0 0 0 1 0 0 0 1 0 0 0 1 0 0 0 0 1 1 0 0 0 0 1 0 0 0 1 1 0 0 0 0 1 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 1 1 1 0 0 0 0 1 1 1 1 1 1 0 1 0 1 1 1 0 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 0 1 1 0 1 0 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 8.25 57.69 11.05 12.25 30.3 10 16.83 17.28 25.76 12.68 13 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1 1 0 0 0 1 1 0 1 1 1 1 1 1 0 1 1 0 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 0 1 0 1 1 1 1 1 1 1 1 1 0 1 1 0 1 1 1 0 1 1 1 1 1 1 1 1 0 1 1 0 0 1 1 0 1 1 1 1 1 1 1 0 1 1 1 0 0 1 0 1 1 1 1 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 0 0 0 1 1 1 1 1 1 1 0 1 1 0 1 0 1 1 1 1 0 1 1 1 1 1 0 0 1 1 1 1 1 1 1 1 1 1 0 0 1 0 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 0 0 1 1 0 1 1 1 1 1 1 1 1 1 1 0 1 1 1 0 1 0 1 1 0 1 1 1 1 0 0 0 1 0 1 1 1 1 1 1 1 1 1 0 1 0 0 1 0 1 1 1 1 1 0 1 0 1 1 0 1 1 1 0 1 1 1 1 1 1 0 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 0 1 1 1 0 0 1 1 1 1 1 0 1 1 1 0 0 1 1 1 1 1 0 1 1 1 1 1 1 1 0 0 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 0 1 1 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 0 1 1 0 1 1 1 1 1 1 0 0 1 1 0 1 1 1 1 0 1 0 1 1 1 1 1 0 1 1 1 1 1 1 1 0 1 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 0 0 1 1 1 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 1 1 1 1 0 1 1 0 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 0 1 1 1 0 1 0 1 1 1 1 1 1 1 1 1 1 0 0 1 1 1 1 1 1 0 1 1 1 1 0 1 1 1 1 1 1 1 1 0 1 0 1 1 1 1 1 1 0 0 1 1 0 1 1 1 0 0 1 1 0 0 1 1 1 1 1 1 0 1 1 1 1 1 1 1 0 1 1 1 1 0 1 1 1 0 1 1 0 1 1 1 1 1 0 1 1 1 0 1 0 1 1 1 1 1 0 1 1 1 0 1 1 0 0 0 1 1 1 1 1 1 0 1 1 1 0 1 1 0 1 0 1 0 1 0 1 1 1 1 1 1 0 1 0 1 1 1 1 1 1 0 1 1 0 1 0 0 1 1 PRICE 84300 134408.6 89912 92165.9 51075.27 46000 52000 37634.41 49000 71500 83717.36 65000 52227.34 76804.91 84408.6 52227.34 75268.82 85900 73000 74500.77 156743.5 109063 40629.8 94000 110215.1 99769.59 95000 145819.5 106500 94376 78341.02 59000 51059.14 70400 53763.44 90629.8 41000 185000 95000 50000 26000 37500 70500 90500 95920 134408.6 31490.02 NEAR 0 0 0 0 1 1 0 1 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 1 0 1 0 0 0 0 0 0 1 YEAR81 0 1 0 1 1 0 0 1 0 0 1 0 1 1 1 1 1 0 0 1 1 1 1 0 1 1 0 1 0 0 1 0 1 0 1 1 0 0 0 0 0 0 0 0 0 1 1 66000 56000 55683.56 75192.02 47619.05 60000 62400 77500 129032.3 121351.8 58000 87900 86000 49923.2 63400 53763.44 34000 41474.66 48000 134792.6 122500 123000 120506.9 82500 54000 110599.1 300000 65284.18 122811.1 56835.64 86000 92089.09 78400 107526.9 120000 136524.6 86000 91500 134408.6 87173.58 79800.3 84211 101382.5 103686.6 119047.6 66100 92933.95 56900 0 1 1 1 1 0 0 0 0 0 0 0 0 1 0 1 1 1 1 0 0 0 0 0 1 1 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 1 1 1 0 0 0 1 1 0 0 0 1 0 1 0 1 0 1 0 0 1 0 0 1 0 1 1 1 0 1 0 1 0 1 0 0 1 1 1 0 1 1 1 0 1 0 75500 49923.2 124424 61827.96 85900 90000 53500 95622.12 120000 142500 42000 53763.44 63000 106500 80900 62980.03 84485.41 85000 65284.18 60500 84900 90245.77 82872.5 84900 69124.42 40000 46082.95 74308.76 85900 139857.9 61443.93 99846.39 76804.91 83000 103686.6 73900 50000 207373.3 83000 95900 64000 46082.95 90400 75000 73500 121000 73000 96006.14 0 0 0 0 0 0 0 1 0 0 1 1 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 1 0 0 1 1 1 0 0 0 0 1 1 0 0 0 1 0 0 0 0 0 0 1 1 1 0 0 0 1 0 0 0 1 0 0 0 1 1 0 1 0 0 1 1 0 1 0 1 1 0 1 1 1 1 0 1 0 0 1 0 0 0 1 0 0 0 0 0 1 55000 97900 125000 110983.1 100000 82000 73000 72964.67 90000 90629.8 63900 56900 100230.4 75900 44900 81413.21 111367.1 90500 75000 88325.66 98000 61443.93 117511.5 34000 119431.6 91858.68 49000 104500 64900 61900 84900 148000 168970.8 38402.46 31000 56067.59 48000 43000 57000 50900 76900 65284.18 52227.34 77000 66052.23 104377.9 92165.9 69124.42 1 0 1 0 0 0 0 0 0 1 1 1 0 0 0 1 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 1 0 1 1 0 1 1 0 1 0 0 1 0 1 0 0 0 0 0 0 1 0 0 0 1 0 1 0 0 1 0 0 1 1 0 0 1 0 1 1 0 1 1 0 0 0 0 0 0 1 1 0 1 0 0 0 0 0 1 1 0 1 1 1 1 77649.77 60500 122887.9 103686.6 120199.7 115207.4 120000 131500 94900 74500 165898.6 59000 108294.9 128993.9 153609.8 132795.7 48000 133256.5 91400 88000 146319.5 67500 120000 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 1 1 0 1 1 1 1 0 0 0 0 1 0 1 1 1 1 0 1 0 0 1 0 0 NAME: Don't forget your name! WARNING: Be careful when you enter the letter answers. Any extra character in the cell, including a blank space, will cause your answer to not match the key and Excel will enter a zero as the score for that question. 1 C 2 C 3 C 4 C 5 E 6 B 7 A 8 D 9 C 10 E 11 B 12 A 13 E 14 C 15 A 16 D 17 B 1 Consider the following regression functions: I II III E(PRICE) = ( + ) + ( + )SQFT E(PRICE) = + ( + )SQFT E(PRICE) = + SQFT The regression equation E(PRICE) = ( + ) + ( + )SQFT contains: a a slope indicator variable but not a intercept dummy variable b an intercept dummy variable but not an slope indicator variable c both an intercept indicator variable and a slope dummy variable d neither a slope dummy variable nor a intercept dummy variable 2 The reference group of the regression model defines a the categories that do not exhaust the sample space b the group with all dummy variables equal to zero c the group including all indicator variables, thereby avoiding the dummy variable trap (DVT) d the category of all continuous variables included in the regression model 3 When you have a multiple regression model with a binary dependent variable, the model is a, a dichotomous model b Bernoulli model c Binary model d Linear Probability model A large company is accused of gender discrimination in wages. The following model has been estimated from the company's human resources information ln(WAGE) = + EDUC + EXPER + MALE where WAGE is hourly wage, EDUC is years of education, EXPER is years of relevant experience, and MALE is the gender. The estimated regression coefficients are: (These numbers are pre-calculated) Intercept EDUC EXPER MALE b_j 1.4534 0.0842 0.0517 0.1951 4 Let EDUC = 16 EXPER = 10 Find the predicted wage of a male employee with the above characteristics. a 36.2904 b 35.3715 c 33.5617 d 32.8855 e 31.5701 5 Find the predicted wage of a female employee with the same years of education and experience. a 31.0777 b 30.1725 c 29.2937 d 28.4405 e 27.6121 6 Find the percent difference in the wage of a male employee over the wage of a female employee. a 22.6191% b 21.5471% c 20.6853% d 19.8578% e 18.6664% 7 Find the percent difference in the wage of a male employee with 11 years of experience over the wage of a female employee with 10 years of experience. a 27.9979% b 28.8379% c 29.7030% d e 30.5941% 31.5119% Using the data in the WAGE tab, run the following regression model: E(ln(WAGE)] = + EDUC + EXPER + EXPER + FEMALE + BLACK + MARRIED + SOUTH + FULLTIME + METRO EDUC EXPER FEMALE BLACK MARRIED SOUTH FULLTIME METRO 8 Find the estimated coefficients of the regression equation. Intercept EDUC EXPER EXPER FEMALE BLACK MARRIED SOUTH FULLTIME METRO 9 years of schooling years of experience = 1, if female, 0 if male = 1, if black, 0 otherwise = 1 if married, 0 if single = 1, lives in SOUTH, 0 otherwise =1, if fulltime, 0, otherwise =1, if lives in metropolitan area, 0 otherwise a b 0.95748 0.95513 0.09432 0.09047 0.03007 0.03019 -0.00044 -0.00044 -0.21911 -0.21693 -0.07436 -0.08500 0.07634 0.07467 -0.00474 0.00056 0.24395 0.24843 0.13269 0.14243 c 0.92721 0.08686 0.0304