Wage Wage EDUC EXPER AGE Gender 37.85 11 2 40 1 21.72 4 1 39 0 14.34 4 2 38 0 21
ID: 3356734 • Letter: W
Question
Wage
Wage
EDUC EXPER AGE Gender 37.85 11 2 40 1 21.72 4 1 39 0 14.34 4 2 38 0 21.26 5 9 53 1 24.65 6 15 59 1 25.65 6 12 36 1 15.45 9 5 45 0 20.39 4 12 37 0 29.13 5 14 37 1 27.33 11 3 43 1 18.02 8 5 32 0 20.39 9 18 40 1 24.18 7 1 49 1 17.29 4 10 43 0 15.61 1 9 31 0 35.07 9 22 45 0 40.33 11 3 31 1 20.39 4 14 55 0 16.61 6 5 30 1 16.33 9 3 28 0 23.15 6 15 60 1 20.39 4 13 32 0 14.88 4 9 58 1 13.88 5 4 28 0 17.65 6 5 40 1 15.45 6 2 37 0 26.35 4 18 52 1 19.15 6 4 44 0 16.61 6 4 57 0 18.39 9 3 30 1 15.45 5 8 43 0 18.02 7 6 31 1 13.44 4 3 33 0 17.66 6 23 51 1 16.96 4 15 37 0 14.34 4 9 45 0 15.45 6 3 55 0 17.43 5 14 57 0 35.89 9 16 36 1 20.39 4 20 60 1 11.81 4 5 35 0 15.45 9 10 34 0 17.66 5 4 28 1 13.87 6 1 25 0 16.35 7 10 43 1 15.45 9 2 42 1 23.67 4 17 47 0 16.02 11 2 46 1 23.15 4 15 52 0 24.18 8 11 64 0 31. FILE Hourly_ Wage. A researcher interviews 50 employees of a large manufacturer and collects data on each worker's hourly wage (Wage), years of higher education (EDUC), experience (EXPER), and age (AGE). A portion of the data is shown in the accompanying table. AGE 40 39 EDUC EXPER Wage $37.85 21.72 4 64 24.18 8 a. Eate:Explanation / Answer
a)
The regression equation is
Wage = 8.68 + 1.23 EDUC + 0.417 EXPER - 0.0190 Age + 2.29 Gender
Predictor Coef SE Coef T P
Constant 8.685 4.093 2.12 0.039
EDUC 1.2327 0.3673 3.36 0.002
EXPER 0.4166 0.1425 2.92 0.005
Age -0.01900 0.08284 -0.23 0.820
Gender 2.290 1.676 1.37 0.179
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