20. A social scientist would like to analyze the relationship between educational attainment (in years of higher education) and annual salary (in $1,000s). He collects data on 20 individuals. A portion of the data is as follows:
Salary | Education | ||||
35 | 1 | ||||
67 | 6 | ||||
⋮ | ⋮ | ||||
32 | 0 | ||||
a. Find the sample regression equation for the model: Salary = β0 + β1Education + ε. (Round answers to 2 decimal places.)
Salaryˆ=Salary^= + Education
b. Interpret the coefficient for Education.
As Education increases by 1 unit, an individual’s annual salary is predicted to increase by $8,590.
As Education increases by 1 unit, an individual’s annual salary is predicted to decrease by $8,590.
As Education increases by 1 unit, an individual’s annual salary is predicted to decrease by $5,460.
As Education increases by 1 unit, an individual’s annual salary is predicted to increase by $5,460.
c. What is the predicted salary for an individual who completed 5 years of higher education? (Round coefficient estimates to at least 4 decimal places and final answer to the nearest whole number.)
Salary | Education |
35 | 1 |
67 | 6 |
79 | 2 |
46 | 1 |
69 | 7 |
78 | 5 |
111 | 6 |
62 | 0 |
20 | 4 |
25 | 5 |
100 | 6 |
47 | 5 |
64 | 3 |
66 | 9 |
154 | 7 |
61 | 0 |
87 | 1 |
60 | 3 |
123 | 7 |
32 | 0 |
A) The sample regression equation for the model is
Salary^=48.016 +5.457 * Education
B) Since the value of β1=5.457~5.46. So,
Interpretation of coefficients is Education increases by 1 unit, an individual’s annual salary are predicted to increase by $5,460.
C) The predicted salary for an individual who completed 5 years of higher education is 75$ (approx)
Note: I have done this problem in R. So, I am attaching my R-code for your reference.
>
Salary<-c(35,67,79,46,69,78,111,62,20,25,100,47,64,66,154,61,87,60,123,32)
> Education<-c(1,6,2,1,7,5,6,0,4,5,6,5,3,9,7,0,1,3,7,0)
> length(Education
+ )
[1] 20
> length(Salary)
[1] 20
> model<-lm(Salary~Education)
> model
Call:
lm(formula = Salary ~ Education)
Coefficients:
(Intercept) Education
48.016 5.457
> pred<-predict(model,data.frame(Education=5))
> pred
1
75.30311
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