Assignment
Statistics Multiple Regression Analysis
|
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
|
Use the dataset labeled "GenderBias2.xls" to answer questions 1 through 9.
Question 1
Use the Regression function in Excel (part of the Data Analysis toolpack) to estimate the effect Experience (in years), Female (1 = female, 0 = male) and Business Degree (1 = business major) have on a worker's salary (in thousand $s).
What is the r-squared for the regression?
a. 0.96
b. 0.93
c. 0.91
d. 0.10
Question 2
What is the regression sum of squares (SSR)?
a. 7434
b. 549
c. 6884
d. 2294
Question 3
When testing whether the regression equation is significant at the 0.05 level, what is the test statistic?
a. 108.60
b. 0.93
c. 15.22
d. -7.36
Question 4
Is the regression equation significant at the 0.05 level?
a. Yes
b. No
c. I need more information to answer this correctly.
Question 5
Which variable(s) significantly affect salary at the 0.05 level?
a. Female
b. Female, Experience
c. Experience
d. Female, Experience, Business Degree
Question 6
Choose the correct interpretation for the coefficient for the variable Female.
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.
Question 7
What is the predicted effect of gaining one more year of experience on a worker's salary?
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
Question 8
Do business majors earn significantly higher starting salaries compared to non business majors at the 0.10 significance level?
a. No
b. Yes
Question 9
Predict the salary for a Female worker with an English degree and 4 years of experience. (Use 4 decimal places in your calculations)
a. $39, 329
b. $32, 761
c. $34, 986
d. $39,500
Question 10
What is inferential statistics (i.e., the objective of his course)?
a. Inferential statistics is all about organizing and presenting data.
b. Inferential statistics is when you don't have any data so you just infer what the data should look like.
c. Inferential statistics describes using sample data to make inferences about unknown population parameters.
d. Inferential statistics is using population datasets to test hypotheses about smaller sample datasets.