Time lefton 6Regression Analysis: BMI versus AGEetredRegression Equationed out ofBMI = 18.367+ 0.282 AGECoefficientslag questionTermConstant 18.367AGEModel SummaryCoef SE CoefT-Value P-Value VIF0.9530.2824 0.019920.330.0399.15 0.0001 1.00S R-sq R-sq(adj) R-sq(pred)66.99%1.82143 70.28% 71.13%Analysis of VarianceSourceDF Adj SS Adj MS F-Value P-Value277.918277.9183,3183.403Regression1 277.921 277.9235 112.80Lack-of-Fit 29 98.6984.770.0001AGE84.770.0001Error1.210.461Pure Error6 14.112.822Total36 390,72using this hypothesized putput,we can write the results as followswe used linear regression to assess the linear relationship. The R-square =* % which indicates that the model isThe model was significant F* jand p-valueThen the Pvalue=for age, which means it is significant inBMI.

The regression equation is given by: BMI = 18.367 + 0.282 AGE And the results are obtained as:…

Regression Analysis: BMI versus AGE Regression Equation of BMI = 18.367+ 0.282 AGE %3D ation Coefficients Term Constant 18.367 AGE Coef SE Coef T-Value P-Value VIF 20.33 0.953 0.039 0.2824 0.0199 9.15 0.00011.00 Model Summary SR-sq R-sq(adj) R-sq(pred) 1.82143 70.28% 71.13% 66.99% Analysis of Variance Source DF Adj SS 1 277.92 277918 Adj MS F-Value 84.77 P-Value Regression 0.0001

Regression Analysis: BMI versus AGE Regression Equation of BMI = 18.367+ 0.282 AGE %3D ation Coefficients Term Constant 18.367 AGE Coef SE Coef T-Value P-Value VIF 20.33 0.953 0.039 0.2824 0.0199 9.15 0.00011.00 Model Summary SR-sq R-sq(adj) R-sq(pred) 1.82143 70.28% 71.13% 66.99% Analysis of Variance Source DF Adj SS 1 277.92 277918 Adj MS F-Value 84.77 P-Value Regression 0.0001

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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