Homework Sourse1 Which variables significantly affect salary at the 005 lev
1. Which variable(s) significantly affect salary at the 0.05 level?
Select one:
a. Female
b. Female, Experience
c. Experience
d. Female, Experience, Business Degree
2. Choose the correct interpretation for the coefficient for the variable Female.
Select one:
a. If Female inceases by 1 unit, the worker\'s salary increases by $8.82.
b. The predicted salary for a female worker without any experience is $12,999 LESS than a male worker without any experience.
c. For every level of experience a women\'s salary is $15,000
d. As a worker\'s experience increases the salary gap between men and women decreases.
3. What is the predicted effect of gaining one more year of experience on a worker\'s salary?
Select one:
a. We predict the worker would increase his or her salary by $4,265
b. We would predict no significant increase in salary
c. We predict the worker would increase his or her salary by $32,213
d. We predict the worker would increase his or her salary by $4,799
4. Predict the salary for a Female worker with an English degree and 4 years of experience. (Use 4 decimal places in your calculations)
Select one:
a. $39, 329
b. $32, 761
c. $34, 986
d. $39,500
| Salary | Experience | Female | Business Degree |
| 22 | 1 | 1 | 0 |
| 33 | 1 | 0 | 0 |
| 38 | 1 | 0 | 0 |
| 40 | 1 | 0 | 1 |
| 30 | 2 | 1 | 0 |
| 31 | 2 | 1 | 0 |
| 38 | 3 | 0 | 0 |
| 41 | 3 | 0 | 0 |
| 29 | 3 | 1 | 0 |
| 30 | 3 | 1 | 1 |
| 31 | 3 | 1 | 0 |
| 42 | 3 | 0 | 0 |
| 32 | 4 | 1 | 0 |
| 44 | 4 | 0 | 0 |
| 47 | 4 | 0 | 1 |
| 46 | 5 | 0 | 0 |
| 38 | 5 | 1 | 0 |
| 51 | 5 | 0 | 0 |
| 53 | 6 | 0 | 0 |
| 55 | 6 | 0 | 0 |
| 42 | 6 | 1 | 0 |
| 67 | 6 | 0 | 1 |
| 45 | 6 | 1 | 6 |
| 55 | 6 | 0 | 0 |
| 54 | 8 | 1 | 0 |
| 68 | 9 | 0 | 1 |
| 72 | 9 | 0 | 0 |
| 77 | 9 | 0 | 0 |
| 62 | 10 | 1 | 1 |
| 89 | 10 | 0 | 0 |
Solution
Here we are given that ,
response variable (Y) = salary
and there are three dependent variables as,
X1 : experience
X2 : female
X3 : business degree.
We can obtain result by using MINITAB.
steps :
STAT --> Regression --> Regression --> Response : Y --> predictors : X1,X2 and X3 --> Results : select second option --> ok
These steps give us following output,
Regression Analysis: Y versus X1, X2, X3
The regression equation is
Y = 28.8 + 4.80 X1 - 13.0 X2 + 0.271 X3
Predictor Coef SE Coef T P
Constant 28.792 1.913 15.05 0.000
X1 4.7985 0.3151 15.23 0.000
X2 -12.999 1.765 -7.36 0.000
X3 0.2714 0.7776 0.35 0.730
S = 4.59685 R-Sq = 92.6% R-Sq(adj) = 91.8%
Analysis of Variance
Source DF SS MS F P
Regression 3 6884.5 2294.8 108.60 0.000
Residual Error 26 549.4 21.1
Total 29 7433.9
From this output we can say that X1 and X2 are significantly affect salary at the 0.05 significance level.
P-value for X1 and X2 is less than 0.05.
So option b) is correct.
that is female and experience are significantly affect salary.
Choose the correct interpretation for the coefficient for the variable Female.
Here option b) is correct.
The predicted salary for a female worker without any experience is $12,999 LESS than a male worker without any experience.
