Row Education Experience Ag Salary 1 10 20 45 55139 2 10 5 23 48937 3 10 19 36 57624 4 11 15 50 58170 5 11 16 42 62202 6 11 8 30 51646 7 11 4 21 52563 8 12 10 34 49434 9 12 8 27 55153 10 12 18 38 63882 11 13 6 25 46067 12 13 10 46 60886 13 14 10 38 57190 14 14 2 22 52094 15 15 8 32 60620 16 16 5 49 59843 17 16 4 28 57288 18 17 7 33 67151 19 18 3 27 61313 20 19 3 32 64175 1. Consider the data set above. Cl is years of education, C2 is years of job experience, C3 is age, and C4 is annual salary. a. Estimate the relationship: C4 = a + b(Cl)+c(C2)+d(C3) b. Test the hypothesis that the entire model (C1, C2, and C3 combined) does not explain a significant amount of variation in the dependent variable at the 5% level of significance. c. What fraction of the variation in annual salary is explained by education, experience, and age?
Correlation
Correlation defines a relationship between two independent variables. It tells the degree to which variables move in relation to each other. When two sets of data are related to each other, there is a correlation between them.
Linear Correlation
A correlation is used to determine the relationships between numerical and categorical variables. In other words, it is an indicator of how things are connected to one another. The correlation analysis is the study of how variables are related.
Regression Analysis
Regression analysis is a statistical method in which it estimates the relationship between a dependent variable and one or more independent variable. In simple terms dependent variable is called as outcome variable and independent variable is called as predictors. Regression analysis is one of the methods to find the trends in data. The independent variable used in Regression analysis is named Predictor variable. It offers data of an associated dependent variable regarding a particular outcome.
|
Row |
Education |
Experience |
Ag |
Salary |
|
1 |
10 |
20 |
45 |
55139 |
|
2 |
10 |
5 |
23 |
48937 |
|
3 |
10 |
19 |
36 |
57624 |
|
4 |
11 |
15 |
50 |
58170 |
|
5 |
11 |
16 |
42 |
62202 |
|
6 |
11 |
8 |
30 |
51646 |
|
7 |
11 |
4 |
21 |
52563 |
|
8 |
12 |
10 |
34 |
49434 |
|
9 |
12 |
8 |
27 |
55153 |
|
10 |
12 |
18 |
38 |
63882 |
|
11 |
13 |
6 |
25 |
46067 |
|
12 |
13 |
10 |
46 |
60886 |
|
13 |
14 |
10 |
38 |
57190 |
|
14 |
14 |
2 |
22 |
52094 |
|
15 |
15 |
8 |
32 |
60620 |
|
16 |
16 |
5 |
49 |
59843 |
|
17 |
16 |
4 |
28 |
57288 |
|
18 |
17 |
7 |
33 |
67151 |
|
19 |
18 |
3 |
27 |
61313 |
|
20 |
19 |
3 |
32 |
64175 |
1. Consider the data set above. Cl is years of education, C2 is years of job experience, C3 is age, and C4 is annual salary.
a. Estimate the relationship:
C4 = a + b(Cl)+c(C2)+d(C3)
b. Test the hypothesis that the entire model (C1, C2, and C3 combined) does not explain a significant amount of variation in the dependent variable at the 5% level of significance.
c. What fraction of the variation in annual salary is explained by education, experience, and age?
d. Calculate a 90% Confidence Interval for expected income for a person with 12 years of education, 10 years of experience, and who is age 30
Trending now
This is a popular solution!
Step by step
Solved in 3 steps with 2 images
