Results Linear Regression Model Summary - bmi-y Model R R² Adjusted R² RMSE Ho 0.000 0.000 0.000 5.655 H₁ 0.081 0.007 0.002 5.651 ANOVA Model Sum of Squares df Mean Square F P H₁ Regression 41.910 1 Residual Total 6322.327 198 41.910 1.313 0.253 31.931 6364.236 199 Note. The intercept model is omitted, as no meaningful information can be shown. Coefficients Model Unstandardized Standard Error Standardized t р Ho (Intercept) 30.634 0.400 76.608 < .001 H₁ (Intercept) Income-x 31.728 -3.638x10-7 1.035 3.175x10-7 30.653 < .001 -0.081 -1.146 0.253

Results Linear Regression Model Summary - bmi-y Model R R² Adjusted R² RMSE Ho 0.000 0.000 0.000 5.655 H₁ 0.081 0.007 0.002 5.651 ANOVA Model Sum of Squares df Mean Square F P H₁ Regression 41.910 1 Residual Total 6322.327 198 41.910 1.313 0.253 31.931 6364.236 199 Note. The intercept model is omitted, as no meaningful information can be shown. Coefficients Model Unstandardized Standard Error Standardized t р Ho (Intercept) 30.634 0.400 76.608 < .001 H₁ (Intercept) Income-x 31.728 -3.638x10-7 1.035 3.175x10-7 30.653 < .001 -0.081 -1.146 0.253

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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