You are analyzing salaries for your organization and you are trying to determine which variables are explaining differences in salaries. You are also wondering if there could possibly be any gender discrimination. The following data have been collected on every employee in this large department in the organization. The definitions of the variables are as follows: Salary: the employees’ bi weekly salary in dollars; Service: the number of months the employee has been employed by the organization; Age: age of the employee in years; Gender: whether the employee is male or female; Job; whether the job is technical (tech) or clerical (cler). The data appear below. Salary Service Age Gender Job 1769 93 42 male cler 1740 104 33 male cler 1941 104 42 male tech 2367 126 57 male tech 2467 98 30 male tech 1640 99 49 male tech 1756 94 35 male cler 1706 96 46 female tech 1767 124 56 female cler 1200 73 23 female tech 1706 110 67 female tech 1985 90 36 female tech 1555 104 53 female cler 1749 81 29 female cler 2056 106 45 male cler 1729 113 55 female tech 2186 129 46 male tech 1858 97 39 female tech 1819 101 43 male tech 1350 91 35 male tech 2030 100 40 male cler 2550 123 59 male cler 1544 88 30 female cler 1766 117 60 male tech 1937 107 45 male tech 2691 105 32 female tech 1623 86 33 female cler 1791 131 56 female tech 2001 95 30 male tech 1874 98 47 male cler 2400 100 50 male tech 1700 80 44 female cler 2900 90 48 female tech 2000 68 51 male cler 3100 75 44 female tech 1900 44 30 female cler 2200 39 35 male cler 2350 40 50 female cler 3000 60 48 female tech 3450 90 62 male tech 3300 75 59 female tech 2950 52 43 female tech 3150 68 52 male tech 2450 35 42 male cler 2150 47 39 female cler 3050 53 46 female tech 3150 70 55 male tech 2700 45 42 female tech 2900 50 50 male tech 2550 60 52 male cler 1975 35 30 female cler 2200 40 28 female cler Your specific tasks are as follows: Determine the multiple regression equation that best explains employees’ salaries. Analyze all the important properties of your final regression equation. This includes the decision to include the variables in your best model.
- You are analyzing salaries for your organization and you are trying to determine which variables are explaining differences in salaries. You are also wondering if there could possibly be any gender discrimination. The following data have been collected on every employee in this large department in the organization. The definitions of the variables are as follows: Salary: the employees’ bi weekly salary in dollars; Service: the number of months the employee has been employed by the organization; Age: age of the employee in years; Gender: whether the employee is male or female; Job; whether the job is technical (tech) or clerical (cler). The data appear below.
Salary Service Age Gender Job
1769 93 42 male cler
1740 104 33 male cler
1941 104 42 male tech
2367 126 57 male tech
2467 98 30 male tech
1640 99 49 male tech
1756 94 35 male cler
1706 96 46 female tech
1767 124 56 female cler
1200 73 23 female tech
1706 110 67 female tech
1985 90 36 female tech
1555 104 53 female cler
1749 81 29 female cler
2056 106 45 male cler
1729 113 55 female tech
2186 129 46 male tech
1858 97 39 female tech
1819 101 43 male tech
1350 91 35 male tech
2030 100 40 male cler
2550 123 59 male cler
1544 88 30 female cler
1766 117 60 male tech
1937 107 45 male tech
2691 105 32 female tech
1623 86 33 female cler
1791 131 56 female tech
2001 95 30 male tech
1874 98 47 male cler
2400 100 50 male tech
1700 80 44 female cler
2900 90 48 female tech
2000 68 51 male cler
3100 75 44 female tech
1900 44 30 female cler
2200 39 35 male cler
2350 40 50 female cler
3000 60 48 female tech
3450 90 62 male tech
3300 75 59 female tech
2950 52 43 female tech
3150 68 52 male tech
2450 35 42 male cler
2150 47 39 female cler
3050 53 46 female tech
3150 70 55 male tech
2700 45 42 female tech
2900 50 50 male tech
2550 60 52 male cler
1975 35 30 female cler
2200 40 28 female cler
Your specific tasks are as follows:
- Determine the multiple regression equation that best explains employees’ salaries.
- Analyze all the important properties of your final regression equation. This includes the decision to include the variables in your best model.
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