The following regression output is for predicting the heart weight (in g) of cats fromtheir body weight (in kg). The coecients are estimated using a dataset of 144domestic cats.20-15-5-2.02.53.03.54.0Body weight (kg)Std. ErrorPr(>[t])0.607Estimatet value(Intercept)body wt-0.3570.692-0.5154.0340.25016.1190.000S = 1.452R = 64.66%= 64.41%Radia) Write out the linear model.y- -0.357b) Interpret the intercept.4.034poverty %Expected heart weight of cats with no body weight is 4.034 kgExpected body weight of cats with no heart weight is 4.034 gFor each additional 1 g increase in heart weight, we expect body weight todecrease by 0.357 kgFor each additional 1 g increase in heart weight, we expect body weight toincrease by 0.357 kgFor each additional 1 g increase in heart weight, we expect body weight toincrease by 4.034 kgExpected heart weight of cats with no body weight is -0.357 kgExpected body weight of cats with no heart weight is -0.357 gFor each additional 1 kg increase in body weight, we expect heart weight todecrease by 0.357 gFor each additional 1 kg increase in body weight, we expect heart weight toincrease bu 4034 aHeart weight (g) C) Interpret the slope.Expected body weight of cats with no heart weight is -0.357 gFor each additional 1 g increase in heart weight, we expect body weight toincrease by 0.357 kgExpected heart weight of cats with no body weight is -0.357 kgFor each additional 1 g increase in heart weight, we expect body weight toincrease by 4.034 kgExpected heart weight of cats with no body weight is 4.034 kgFor each additional 1 g increase in heart weight, we expect body weight todecrease by 0.357 kgFor each additional 1 kg increase in body weight, we expect heart weight todecrease by 0.357 gFor each additional 1 kg increase in body weight, we expect heart weight toincrease by 4.034 gExpected body weight of cats with no heart weight is 4.034 gFor each additional 1 kg increase in body weight, we expect heart weight toincrease by 0.357gd) Interpret .. Body weight explains 64.66% of the variability in heart weight in catsHeart weight explains 64.66% of the variability in body weight in catsHeart weight causes 64.66% of the difference in body weight in catsBody weight causes 64.66% of the difference in heart weight in catse) Calculate the correlation coefficient and round to 4 decimal places.

From the output, the slope is 4.034 and the intercept is -0.357.

Answered: The following regression output is for…

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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A regression was run to determine if there is a relationship between hours of tv watched per day (x) and number of sit-ups a person can do (y)
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