Salary | Years | Age | MBA? |
---|---|---|---|
35600 | 7.3 | 33 | 0 |
71100 | 24.1 | 61 | 0 |
42300 | 8.6 | 35 | 1 |
51200 | 11.5 | 43 | 1 |
41300 | 13.8 | 40 | 0 |
64900 | 18.1 | 55 | 0 |
54600 | 16.9 | 49 | 0 |
43800 | 9.4 | 37 | 1 |
46600 | 12.2 | 48 | 1 |
50100 | 16 | 50 | 0 |
32800 | 4.2 | 28 | 1 |
49300 | 11.5 | 49 | 0 |
38100 | 7.3 | 35 | 1 |
53500 | 14.4 | 52 | 1 |
46000 | 10.8 | 45 | 0 |
The data in the above table give the annual salary (Salary), number of years of employment (Years), employee's age (Age), and whether or not the employee has an MBA degree (1 = yes, 0 = no) for 15 workers in a particular industry and location.
What proportion of the variability in the Salaries is explained by the multiple regression model using Years, Age, and MBA? as predictor variables?
Question 7 options:
.915% |
|
3388.9 |
|
91.5% |
|
39.6% |
|
89.2 |
The statistical software output for this problem is:
From the above table,
R - squared = 0.915
Hence,
Percentage of variation explained = 91.5%
Option C is correct.
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