Public health researchers would like to evaluate whether information on age (in years) and weight (in kg) could be used to predict shoe size of students. The output of the linear regression analysis is given below. 95% confidence interval Unstandardized Beta t Lower bound Upper bound (Constant) 6.493 1.121 -5.191 18.177 Age (years) -0.462 -1.617 -1.037 0.114 Weight (kg) 0.155 7.568 0.114 0.197 Dependent variable: shoe size A) Write null hypotheses for the linear regression analysis. B) Interpret the results of the linear regression in not more than 100 words. C) Write a complete equation for the results of the linear regression analysis
Public health researchers would like to evaluate whether information on age (in years) and weight (in kg) could be used to predict shoe size of students. The output of the linear regression analysis is given below. 95% confidence interval Unstandardized Beta t Lower bound Upper bound (Constant) 6.493 1.121 -5.191 18.177 Age (years) -0.462 -1.617 -1.037 0.114 Weight (kg) 0.155 7.568 0.114 0.197 Dependent variable: shoe size A) Write null hypotheses for the linear regression analysis. B) Interpret the results of the linear regression in not more than 100 words. C) Write a complete equation for the results of the linear regression analysis
MATLAB: An Introduction with Applications
6th Edition
ISBN:9781119256830
Author:Amos Gilat
Publisher:Amos Gilat
Chapter1: Starting With Matlab
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Public health researchers would like to evaluate whether information on age (in years) and weight (in kg) could be used to predict shoe size of students.
The output of the linear
|
|
|
|
95% confidence interval |
|
|
|
Unstandardized Beta |
t |
Lower bound |
Upper bound |
|
(Constant) |
6.493 |
1.121 |
-5.191 |
18.177 |
Age (years) |
-0.462 |
-1.617 |
-1.037 |
0.114 |
|
Weight (kg) |
0.155 |
7.568 |
0.114 |
0.197 |
Dependent variable: shoe size
- A) Write null hypotheses for the linear regression analysis.
- B) Interpret the results of the linear regression in not more than 100 words.
C) Write a complete equation for the results of the linear regression analysis
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