2. Suppose in a health screening, seven people take measurement on Gender, Height, Weight and Age. One wants to investigate the impact of height on weight and get the following SAS output: Variable Intercept height DF 1 1 Parameter Estimates Parameter Estimate -592.64458 11.19127 Standard Error 81.54217 1.21780 t Value Pr > |t| -7.27 9.19 0.0008 0.0003
2. Suppose in a health screening, seven people take measurement on Gender, Height, Weight and Age. One wants to investigate the impact of height on weight and get the following SAS output: Variable Intercept height DF 1 1 Parameter Estimates Parameter Estimate -592.64458 11.19127 Standard Error 81.54217 1.21780 t Value Pr > |t| -7.27 9.19 0.0008 0.0003
MATLAB: An Introduction with Applications
6th Edition
ISBN:9781119256830
Author:Amos Gilat
Publisher:Amos Gilat
Chapter1: Starting With Matlab
Section: Chapter Questions
Problem 1P
Related questions
Question

Transcribed Image Text:2. Suppose in a health screening, seven people take measurement on
Gender, Height, Weight and Age. One wants to investigate the impact of
height on weight and get the following SAS output:
Variable
Intercept
height
Source
DF
Model
Error
Corrected Total
1
1
Parameter
Estimate
-592.64458
11.19127
Parameter Estimates
DF
The REG Procedure
Model: MODEL 1
Dependent Variable: weight
1
5
6
Number of Observations Read
Number of Observations Used
Root MSE
Dependent Mean
Coeff Var
Standard
Error
Sum of
Squares
81.54217
1.21780
Analysis of Variance
11880
703.38705
12584
11.86075
155.57143
7.62399
t Value
Mean
Square
11880
140.67741
R-Square
Adj R-Sq
-7.27
9.19
7
7
F Value
84.45
0.9441
0.9329
Pr > |t|
0.0008
0.0003
Pr > F
0.0003

Transcribed Image Text:(d) What is the coefficient of determination? Explain what this tells us.
(e) What is the correlation coefficient between the two variables?
(f) Do you think, from this data, that height is a good predictor of
weight? Conduct the appropriate hypotheses test to confirm your
answer (please include your null and alternative hypotheses, calculate
the test statistic and report the P-value. Choose a = .05.
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