A scientific foundation wanted to evaluate the relation between y= salary of researcher (in thousands of dollars), x1= number of years of experience, x2= an index of publication quality, x3=sex (M=1, F=0) and x4= an index of success in obtaining grant support. A sample of 35 randomly selected researchers was used to fit the multiple regression model. Parts of the computer output appear below. The least squares line fitted to the data is: salary = 2.001 + 0.33 x1 + 0.04 x2 + 0.69 x3 + 0.30 x4 + ε salary = 17.85 + 1.10 x1 + 0.32 x2 + 1.59 x3 + 1.29 x4 + ε salary = 2.001 + 0.33 x1 + 0.04 x2 + 0.69 x3 + 0.30 x4 salary = 17.85 + 1.10 x1 + 0.32 x2 + 1.59 x3 + 1.29 x4
A scientific foundation wanted to evaluate the relation between y= salary of researcher (in thousands of dollars), x1= number of years of experience, x2= an index of publication quality, x3=sex (M=1, F=0) and x4= an index of success in obtaining grant support. A sample of 35 randomly selected researchers was used to fit the multiple regression model. Parts of the computer output appear below. The least squares line fitted to the data is: salary = 2.001 + 0.33 x1 + 0.04 x2 + 0.69 x3 + 0.30 x4 + ε salary = 17.85 + 1.10 x1 + 0.32 x2 + 1.59 x3 + 1.29 x4 + ε salary = 2.001 + 0.33 x1 + 0.04 x2 + 0.69 x3 + 0.30 x4 salary = 17.85 + 1.10 x1 + 0.32 x2 + 1.59 x3 + 1.29 x4
MATLAB: An Introduction with Applications
6th Edition
ISBN:9781119256830
Author:Amos Gilat
Publisher:Amos Gilat
Chapter1: Starting With Matlab
Section: Chapter Questions
Problem 1P
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Question
A scientific foundation wanted to evaluate the relation between y= salary of researcher (in thousands of dollars), x1= number of years of experience, x2= an index of publication quality, x3=sex (M=1, F=0) and x4= an index of success in obtaining grant support. A sample of 35 randomly selected researchers was used to fit the multiple regression model. Parts of the computer output appear below. The least squares line fitted to the data is:
salary = 2.001 + 0.33 x1 + 0.04 x2 + 0.69 x3 + 0.30 x4 + ε
salary = 17.85 + 1.10 x1 + 0.32 x2 + 1.59 x3 + 1.29 x4 + ε
salary = 2.001 + 0.33 x1 + 0.04 x2 + 0.69 x3 + 0.30 x4
salary = 17.85 + 1.10 x1 + 0.32 x2 + 1.59 x3 + 1.29 x4
Transcribed Image Text:Predictor
Coef
SE Coef
P
Constant
17.846931
2.001876
8.915
0.0001
Years
1.103130
0.359573
3.068
0.0032
Papers
0.321520
0.037109
0.0002
Sex
1.593400
0.687724
2.317
0.0083
Grants
1.288941
0.298479
4.318
0.0003
S= 1.75276
R-sq = 92.3%
adj R-sq = 91.4%
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