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Regression Analysis: BMI versus AGE Regression Equation BMI = 18.367 + 0.282 AGE Coefficients Term Coef SE Coef T-Value P-Value VIF Constant 18.367 0.953 20.33 0.039 AGE 0.2824 0.0199 9.15 0.0001 1.00 Model Summary S R-sq R-sq(adj) R-sq(pred) 1.82143 70.28% 71.13% 66.99% Analysis of Variance Source DF Adj SS Adj MS F-Value P-Value Regression 1 277.92 277.918 84.77 0.0001 AGE 1 277.92 277.918 84.77 0.0001 Error 35 112.80 3.318 Lack-of-Fit 29 98.69 3.403 1.21 0.461 Pure Error 6 14.11 2.822 Total 36 390.72 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= for age, which means it is significant in BMI.

          Regression Analysis: BMI versus AGE
Regression Equation
BMI = 18.367 + 0.282 AGE
Coefficients
Term Coef SE Coef T-Value P-Value VIF
Constant 18.367 0.953 20.33 0.039
AGE 0.2824 0.0199 9.15 0.0001 1.00
Model Summary
S R-sq R-sq(adj) R-sq(pred)
1.82143 70.28% 71.13% 66.99%
Analysis of Variance
Source DF Adj SS Adj MS F-Value P-Value
Regression 1 277.92 277.918 84.77 0.0001
AGE 1 277.92 277.918 84.77 0.0001
Error 35 112.80 3.318
Lack-of-Fit 29 98.69 3.403 1.21 0.461
Pure Error 6 14.11 2.822
Total 36 390.72

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=
for age, which means it is
significant in BMI.

Regression Analysis: BMI versus AGE
Regression Equation
BMI = 18.367 + 0.282 AGE
Coefficients
Term Coef SE Coef T-Value P-Value VIF
Constant 18.367 0.953 20.33 0.039
AGE 0.2824 0.0199 9.15 0.0001 1.00
Model Summary
S R-sq R-sq(adj) R-sq(pred)
1.82143 70.28% 71.13% 66.99%
Analysis of Variance
Source DF Adj SS Adj MS F-Value P-Value
Regression 1 277.92 277.918 84.77 0.0001
AGE 1 277.92 277.918 84.77 0.0001
Error 35 112.80 3.318
Lack-of-Fit 29 98.69 3.403 1.21 0.461
Pure Error 6 14.11 2.822
Total 36 390.72

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=
for age, which means it is
significant in BMI.

Added by Jill M.

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