Wage Industry 19388 49898 28219 83601 29736 50235 45976 33411 21716 37664 26820 29977 33959 11780 10997 17626 22133 21994 29390 32138 30006 68573 17694 26795 19981 14476 19452 28168 19306 13318 25166 18121 13162 32094 16667 50171 31691 36178 15234 16817 22485 30308 11702 11186 12285 Occupation 1 2 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 1 0 1 1 0 0 0 1 0 0 0 0 0 0 0 0 Education 0 0 3 5 4 0 2 2 5 5 5 4 5 2 4 3 5 1 0 4 3 5 4 0 4 5 4 0 5 0 4 3 0 3 3 5 0 3 1 3 3 4 2 0 1 South 6 12 12 17 8 16 12 12 12 18 18 16 17 11 14 12 16 12 13 14 16 16 8 7 4 12 13 13 9 11 12 12 12 12 12 12 12 12 12 12 12 12 14 12 12 Nonwh 1 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 1 1 0 0 0 1 1 0 0 0 1 1 0 0 1 0 0 Hisp 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 Female 0 0 0 1 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 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 1 1 0 1 1 1 0 0 1 0 1 0 0 1 0 0 1 1 1 1 0 1 0 0 0 1 1 1 0 0 1 0 1 19284 11451 57623 25670 83443 49974 46646 31702 13312 44543 15013 33389 60626 24509 20852 30133 31799 16796 20793 29407 29191 15957 34484 35185 26614 41780 55777 15160 66738 33351 33498 29809 15193 23027 75165 18752 83569 32235 20852 13787 34746 17690 52762 60152 33461 13481 1 1 0 0 0 1 2 0 0 0 0 0 0 0 1 2 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 4 0 1 3 5 1 0 3 4 2 4 1 5 5 0 0 3 4 0 4 0 2 3 3 0 0 1 4 0 5 1 4 0 4 1 4 1 3 0 4 3 1 5 5 1 4 16 12 15 13 17 16 5 12 12 18 16 14 18 14 12 10 12 12 12 10 12 12 13 14 12 12 14 8 9 16 10 8 12 14 15 11 18 12 12 11 14 12 18 16 16 12 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 1 1 0 1 1 0 0 1 1 1 1 1 0 0 0 0 0 0 0 0 1 0 1 1 0 1 0 1 0 0 0 0 0 1 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 1 0 1 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 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 1 1 0 1 0 1 1 0 0 1 1 0 0 0 0 1 1 0 1 1 1 0 0 1 1 1 1 0 0 1 0 1 0 1 1 0 0 1 0 1 0 0 1 0 0 0 0 0 9879 16789 31304 37771 50187 39888 19227 32786 28440 0 0 0 0 0 0 0 1 0 3 3 1 5 3 3 3 0 4 12 13 16 15 12 12 12 11 12 1 1 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 1 0 1 0 1 Experience Marr 45 33 12 18 47 12 43 20 11 19 33 6 26 33 0 45 10 24 18 22 27 14 38 44 54 3 3 17 34 25 10 18 6 14 4 39 13 40 4 26 22 10 6 0 42 Age 1 1 1 1 1 1 1 1 0 1 0 1 1 1 0 1 0 1 1 1 1 1 1 1 1 1 0 0 1 1 0 1 0 1 0 1 0 1 0 0 0 1 1 0 1 Union 57 51 30 41 61 34 61 38 29 43 57 28 49 50 20 63 32 42 37 42 49 36 52 57 64 21 22 36 49 42 28 36 24 32 22 57 31 58 22 44 40 28 26 18 60 0 1 0 0 1 0 1 0 0 0 1 0 1 0 0 0 1 0 0 1 0 1 0 0 0 0 0 0 1 1 0 0 1 0 0 1 0 0 0 0 0 0 0 0 0 3 8 31 8 5 26 44 39 9 10 21 22 7 15 38 27 25 14 6 19 9 10 28 12 19 9 21 45 29 4 20 29 15 34 12 45 29 38 1 4 15 14 7 38 7 7 0 1 1 0 0 1 1 1 1 1 1 0 1 0 1 1 0 1 0 0 0 0 0 1 1 1 1 0 1 1 1 0 0 1 1 0 1 1 0 1 1 1 1 1 1 0 25 26 52 27 28 48 55 57 27 34 43 42 31 35 56 43 43 32 24 35 27 28 47 32 37 27 41 59 44 26 36 43 33 54 33 62 53 56 19 21 35 32 31 60 29 25 0 0 0 1 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 1 0 1 0 0 0 0 0 0 0 0 1 0 28 6 26 5 24 5 15 37 24 1 1 1 0 1 0 1 1 1 46 25 48 26 42 23 33 54 42 0 0 0 0 0 0 0 1 0 Refer to the Wage data in Blackboard which reports information on annual wages for a sample of 100 workers. This data set includes variables relating to industry, years of education, and the gender for each worker. Determine the regression equation using annual wage as the dependent variable and year of education, gender, years of work experience, age in years, and whether or not the worker is a union member. A.) Write out the regression equation. How much does each year of education and each year of experience add to the annual wage? (Minitab: Stat - Regression - Regression, Fit Regression Model, Responses = Wage, Continuous Predictors = Education, Female, Experience, Age, and Union) (SPSS: Analyze - Regression - Linear, Dependent = Wage, Independent(s) = Education, Female, Experience, Age, and Union) B.) Determine the value of R-squared. Provide an interpretation of the variance R-squared represents. C.) Develop a correlation matrix. Which independent variables have strong or weak correlations with the dependent variable (wage)? (Minitab: Stat - Basic Statistics - Correlation - Add the following variables: Wage, Education, Female, Experience, Age, and Union - Then uncheck display p-values) (SPSS: Analyze - Correlate - Bivariate - Variables = Wage, Education, Female, Experience, Age, and Union) Wage Industry 19388 49898 28219 83601 29736 50235 45976 33411 21716 37664 26820 29977 33959 11780 10997 17626 22133 21994 29390 32138 30006 68573 17694 26795 19981 14476 19452 28168 19306 13318 25166 18121 13162 32094 16667 50171 31691 36178 15234 16817 22485 30308 11702 11186 12285 Occupation 1 2 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 1 0 1 1 0 0 0 1 0 0 0 0 0 0 0 0 Education 0 0 3 5 4 0 2 2 5 5 5 4 5 2 4 3 5 1 0 4 3 5 4 0 4 5 4 0 5 0 4 3 0 3 3 5 0 3 1 3 3 4 2 0 1 South 6 12 12 17 8 16 12 12 12 18 18 16 17 11 14 12 16 12 13 14 16 16 8 7 4 12 13 13 9 11 12 12 12 12 12 12 12 12 12 12 12 12 14 12 12 Nonwh 1 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 1 1 0 0 0 1 1 0 0 0 1 1 0 0 1 0 0 Hisp 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 Female 0 0 0 1 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 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 1 1 0 1 1 1 0 0 1 0 1 0 0 1 0 0 1 1 1 1 0 1 0 0 0 1 1 1 0 0 1 0 1 19284 11451 57623 25670 83443 49974 46646 31702 13312 44543 15013 33389 60626 24509 20852 30133 31799 16796 20793 29407 29191 15957 34484 35185 26614 41780 55777 15160 66738 33351 33498 29809 15193 23027 75165 18752 83569 32235 20852 13787 34746 17690 52762 60152 33461 13481 1 1 0 0 0 1 2 0 0 0 0 0 0 0 1 2 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 4 0 1 3 5 1 0 3 4 2 4 1 5 5 0 0 3 4 0 4 0 2 3 3 0 0 1 4 0 5 1 4 0 4 1 4 1 3 0 4 3 1 5 5 1 4 16 12 15 13 17 16 5 12 12 18 16 14 18 14 12 10 12 12 12 10 12 12 13 14 12 12 14 8 9 16 10 8 12 14 15 11 18 12 12 11 14 12 18 16 16 12 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 1 1 0 1 1 0 0 1 1 1 1 1 0 0 0 0 0 0 0 0 1 0 1 1 0 1 0 1 0 0 0 0 0 1 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 1 0 1 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 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 1 1 0 1 0 1 1 0 0 1 1 0 0 0 0 1 1 0 1 1 1 0 0 1 1 1 1 0 0 1 0 1 0 1 1 0 0 1 0 1 0 0 1 0 0 0 0 0 9879 16789 31304 37771 50187 39888 19227 32786 28440 0 0 0 0 0 0 0 1 0 3 3 1 5 3 3 3 0 4 12 13 16 15 12 12 12 11 12 1 1 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 1 0 1 0 1 Experience Marr 45 33 12 18 47 12 43 20 11 19 33 6 26 33 0 45 10 24 18 22 27 14 38 44 54 3 3 17 34 25 10 18 6 14 4 39 13 40 4 26 22 10 6 0 42 Age 1 1 1 1 1 1 1 1 0 1 0 1 1 1 0 1 0 1 1 1 1 1 1 1 1 1 0 0 1 1 0 1 0 1 0 1 0 1 0 0 0 1 1 0 1 Union 57 51 30 41 61 34 61 38 29 43 57 28 49 50 20 63 32 42 37 42 49 36 52 57 64 21 22 36 49 42 28 36 24 32 22 57 31 58 22 44 40 28 26 18 60 0 1 0 0 1 0 1 0 0 0 1 0 1 0 0 0 1 0 0 1 0 1 0 0 0 0 0 0 1 1 0 0 1 0 0 1 0 0 0 0 0 0 0 0 0 3 8 31 8 5 26 44 39 9 10 21 22 7 15 38 27 25 14 6 19 9 10 28 12 19 9 21 45 29 4 20 29 15 34 12 45 29 38 1 4 15 14 7 38 7 7 0 1 1 0 0 1 1 1 1 1 1 0 1 0 1 1 0 1 0 0 0 0 0 1 1 1 1 0 1 1 1 0 0 1 1 0 1 1 0 1 1 1 1 1 1 0 25 26 52 27 28 48 55 57 27 34 43 42 31 35 56 43 43 32 24 35 27 28 47 32 37 27 41 59 44 26 36 43 33 54 33 62 53 56 19 21 35 32 31 60 29 25 0 0 0 1 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 1 0 1 0 0 0 0 0 0 0 0 1 0 28 6 26 5 24 5 15 37 24 1 1 1 0 1 0 1 1 1 46 25 48 26 42 23 33 54 42 0 0 0 0 0 0 0 1 0 Refer to the Wage data in Blackboard which reports information on annual wages for a sample of 100 workers. This data set includes variables relating to industry, years of education, and the gender for each worker. Determine the regression equation using annual wage as the dependent variable and year of education, gender, years of work experience, age in years, and whether or not the worker is a union member. A.) Write out the regression equation. How much does each year of education and each year of experience add to the annual wage? (Minitab: Stat - Regression - Regression, Fit Regression Model, Responses = Wage, Continuous Predictors = Education, Female, Experience, Age, and Union) (SPSS: Analyze - Regression - Linear, Dependent = Wage, Independent(s) = Education, Female, Experience, Age, and Union) B.) Determine the value of R-squared. Provide an interpretation of the variance R-squared represents. C.) Develop a correlation matrix. Which independent variables have strong or weak correlations with the dependent variable (wage)? (Minitab: Stat - Basic Statistics - Correlation - Add the following variables: Wage, Education, Female, Experience, Age, and Union - Then uncheck display p-values) (SPSS: Analyze - Correlate - Bivariate - Variables = Wage, Education, Female, Experience, Age, and Union)