Regression methods were used to analyze the data from a study investigating the relationshipbetween roadway surfaee temperature (X) and pavement deflecetion (y).TemperatureDeflection700.621770.65772.10.6472.80.62378.30.66174.50.641740.63772.40.63075.20.644760.63972.70.63767.80.62776,60.65273.40.6370.50.62772.10.63171.20.641730.63172.70.63417.40.638 a) Estimate the intercept Bo and slope Bi regression coefficients Write the estimatedregression line.b) Compute the residuals.c) Compute SSE and estimate the variance.d) Find the standard error of the slope and intercept coefficients.e) Show that SST= SSR+ SSE) Compute the coefficient of determination, R Comment on the value.

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Answered: a) Estimate the intercept Bo and slope…

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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Report the equation of the regression line and interpret it in the context of the problem
a. Estimate the regression line and also write the prediction equation. y = 83.4578-5.8795 x y = 5.8795 + 83.4578 x ŷ ŷ= = -5.8795 + 83.4578 x = 83.4578 + 5.8795 x
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The data shows a systolic and a diastolic blood pressure of certain patients. Find the linear regression eqation, using the first variable (systolic) as the independent variable. Find the best predicted diastolic blood pressure for a patient with a systolic blood pressure reading of 140. Use a significance level of x Systolic Diastolic 116 82 124 88 112 72 144 95 138 96 112 76 145 88 124 76 130 90 129 94 1)Find the correlation coefficient. 2)State the appropriate critical r value to use to test the claim that the correlation between systolic blood pressure and diastolic blood pressure is significantly different from 0 3)The correlation between diastolic blood pressure and systolic blood pressure is: significant, not significant, weak, none 4)The best predicted diastolic blood pressure for a patient with a systolic blood pressure reading of 140 is closest to 1pts

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a) Estimate the intercept Bo and slope B1 regression coefficients. Write the estimated regression line. b) Compute the residuals. c) Compute SSE and estimate the variance.