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c. Find the sum of squares of the regression and error and their corresponding mean squares. d. Test the usefulness of the regression model and the slope of the line of means. e. Find the coefficient of correlation and coefficient of determination of the regression model and interpret your results.

c. Find the sum of squares of the regression and error and their corresponding mean squares. d. Test the usefulness of the regression model and the slope of the line of means. e. Find the coefficient of correlation and coefficient of determination of the regression model and interpret your results.

MATLAB: An Introduction with Applications
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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Could you please solve the subparts d and e?

The following data represents a report about the x: carbon amount (in mg per 1g steel) and y: the ultimate tensile strength of steel (in MPa): 200 200 150 | 150 142 180 | 210 | 190 300 300 | 350 402 440 390 | 600 | 610 100 125 125 250 250 406 y 155 320 | 280 | 400 | 430 670 a. Find the least squares estimate of the regression line with assuming the simple linear regression is valid. (Find the coefficients of the model) b. Write down the probabilistic and deterministic models (equations) with the estimated coefficients. c. Find the sum of squares of the regression and error and their corresponding mean squares. d. Test the usefulness of the regression model and the slope of the line of means. e. Find the coefficient of correlation and coefficient of determination of the regression model and interpret your results.
Transcribed Image Text:The following data represents a report about the x: carbon amount (in mg per 1g steel) and y: the ultimate tensile strength of steel (in MPa): 200 200 150 | 150 142 180 | 210 | 190 300 300 | 350 402 440 390 | 600 | 610 100 125 125 250 250 406 y 155 320 | 280 | 400 | 430 670 a. Find the least squares estimate of the regression line with assuming the simple linear regression is valid. (Find the coefficients of the model) b. Write down the probabilistic and deterministic models (equations) with the estimated coefficients. c. Find the sum of squares of the regression and error and their corresponding mean squares. d. Test the usefulness of the regression model and the slope of the line of means. e. Find the coefficient of correlation and coefficient of determination of the regression model and interpret your results.
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