The new manager of an Information Technology company collected data for a sample of 20 computer programmers in the organization to perform a multiple regression analysis on the structure of their salaries. The aim of this manager in this exercise is to determine if the Salary (y) of a hired computer programmer was related to the years of Experience (??) in the organization and also the Score (??) of the programmers during their first interview aptitude test scores. The years of experience, score on the aptitude test and the corresponding annual salary (in thousands of Ghana cedis) for a sample of the 20 programmers is shown in the Regression statistics table below; Experience (??) (in years) Score (??) (out of 100%) Salary (y) (GH¢ 000) 4 78 24 7 100 43 1 86 23.7 5 82 34.3 8 86 35.8 10 84 38 0 75 22.2 1 80 23.1 6 83 30 6 91 33 9 88 38 2 73 26.6 10 75 36.2 5 81 31.6 6 74 29 8 87 34 4 79 30.1 6 94 33.9 3 70 28.2 3 89 30 Use Multiple Regression Analysis to help management develop a Salary model that will be fair for all existing staff and new ones that are yet to come in, by finding the following; (a) Multiple regression equation of the above data. Regression coefficient values and their interpretations. Adjusted R squared value and its interpretation. The standard error and its interpretation.
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.
The new manager of an Information Technology company collected data for a sample of 20 computer programmers in the organization to perform a multiple
|
Experience (??) (in years) |
Score (??) (out of 100%) |
Salary (y) (GH¢ 000) |
|
4 |
78 |
24 |
|
7 |
100 |
43 |
|
1 |
86 |
23.7 |
|
5 |
82 |
34.3 |
|
8 |
86 |
35.8 |
|
10 |
84 |
38 |
|
0 |
75 |
22.2 |
|
1 |
80 |
23.1 |
|
6 |
83 |
30 |
|
6 |
91 |
33 |
|
9 |
88 |
38 |
|
2 |
73 |
26.6 |
|
10 |
75 |
36.2 |
|
5 |
81 |
31.6 |
|
6 |
74 |
29 |
|
8 |
87 |
34 |
|
4 |
79 |
30.1 |
|
6 |
94 |
33.9 |
|
3 |
70 |
28.2 |
|
3 |
89 |
30 |
Use Multiple Regression Analysis to help management develop a Salary model that will be fair for all existing staff and new ones that are yet to come in, by finding the following; (a) Multiple regression equation of the above data.
- Regression coefficient values and their interpretations.
- Adjusted R squared value and its interpretation.
The standard error and its interpretation.
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