Regression analysis Multiple R 0.912878 R Square Adjusted R Square 0.830615 Standard Error 35.94092 Observations 63 ANOVA df MS F P-value Regression 1 394021.5 394021.5 305.0293 2.04664E-25 Residual 61 78796.74 1291.75 Total 62 472818.3 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Intercept 19.99449 6.367757 0.002605 7.261369448 32.72760207 X 3.413824 0.195466 J 2.05E-25 N Using the method of least squares, the estimated regression will be: OA. D=19.995 + 3.414x O B. = 19.995-3.414x O C. )-3.414-19.995x O D.9=3.414 + 19.995x

Regression analysis Multiple R 0.912878 R Square Adjusted R Square 0.830615 Standard Error 35.94092 Observations 63 ANOVA df MS F P-value Regression 1 394021.5 394021.5 305.0293 2.04664E-25 Residual 61 78796.74 1291.75 Total 62 472818.3 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Intercept 19.99449 6.367757 0.002605 7.261369448 32.72760207 X 3.413824 0.195466 J 2.05E-25 N Using the method of least squares, the estimated regression will be: OA. D=19.995 + 3.414x O B. = 19.995-3.414x O C. )-3.414-19.995x O D.9=3.414 + 19.995x

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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