Regression Analysis: BMI versus AGE Regression Equation BMI = 19.367+ 0.1823 AGE Coefficients Term Constant 19.367 AGE Model Summary Coef SE Coef T-Value P-Value VIF 20.33 9.15 0.030 0.004 1.00 0.953 0.1823 0.0199 SR-sq R-sq(adj) R-sq(pred) 66.99% 1.82143 71.13% 70.28%

MATLAB: An Introduction with Applications
6th Edition
ISBN:9781119256830
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Regression Analysis: BMI versus AGE
Regression Equation
BMI = 19.367+ 0.1823 AGE
Coefficients
Term
Constant 19.367
AGE
Model Summary
Coef SE Coef T-Value P-Value VIF
0.030
0.004 1.00
0.953
20.33
0.1823 0.0199
9.15
SR-sq R-sq(adj) R-sq(pred)
1.82143 71.13% 70.28%
66.99%
Analysis of Variance
DF Adj sS
1 277.92
Adj MS F-Value P-Value
277.918
277.918
3.318
Source
Regression
83.77
0.004
1 277.92
34 112.80
AGE
83.77
0.004
Error
Lack-of-Fit 29 98.69
5 14.11
35 390.72
3.403
1.21
0.461
Pure Error
2.822
Total
using this hypothesized output,
we can write the results as follows:
we used linear regression to assess the linear
relationship. The R-square
+ %,
which indicates that the model is
The model was significant F
and p-value =
Then the P-value%3D
for age which means it is
significant in
BMI.
Transcribed Image Text:Regression Analysis: BMI versus AGE Regression Equation BMI = 19.367+ 0.1823 AGE Coefficients Term Constant 19.367 AGE Model Summary Coef SE Coef T-Value P-Value VIF 0.030 0.004 1.00 0.953 20.33 0.1823 0.0199 9.15 SR-sq R-sq(adj) R-sq(pred) 1.82143 71.13% 70.28% 66.99% Analysis of Variance DF Adj sS 1 277.92 Adj MS F-Value P-Value 277.918 277.918 3.318 Source Regression 83.77 0.004 1 277.92 34 112.80 AGE 83.77 0.004 Error Lack-of-Fit 29 98.69 5 14.11 35 390.72 3.403 1.21 0.461 Pure Error 2.822 Total using this hypothesized output, we can write the results as follows: we used linear regression to assess the linear relationship. The R-square + %, which indicates that the model is The model was significant F and p-value = Then the P-value%3D for age which means it is significant in BMI.
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