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 statist

Glencoe Algebra 1, Student Edition, 9780079039897, 0079039898, 2018
18th Edition
ISBN:9780079039897
Author:Carter
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Chapter10: Statistics
Section10.6: Summarizing Categorical Data
Problem 10CYU
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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
4 | P a g e
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
Managerial Report 2:
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.
(b) Regression coefficient values and their interpretations.
(c) Adjusted R squared value and its interpretation.
(d) The standard error and its interpretation.

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