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* Instead of slope, intercept, and standard error, show the final fitted equation and coefficient of determination (R²) ** DO repeat the problem with x and y reversed and interpret the results.

* Instead of slope, intercept, and standard error, show the final fitted equation and coefficient of determination (R²) ** DO repeat the problem with x and y reversed and interpret the 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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17.3

* Instead of slope, intercept, and standard error, show the final fitted equation and coefficient of determination (R²) ** DO repeat the problem with x and y reversed and interpret the results.
Transcribed Image Text:* Instead of slope, intercept, and standard error, show the final fitted equation and coefficient of determination (R²) ** DO repeat the problem with x and y reversed and interpret the results.
**17.3 Use Least-Squares Regression to Fit a Straight Line** **Table of Data:** | x | 0 | 2 | 4 | 6 | 9 | 11 | 12 | 15 | 17 | 19 | |---|---|---|---|---|---|----|----|----|----|----| | y | 5 | 6 | 7 | 6 | 9 | 8 | 7 | 10 | 12 | 12 | **Instructions:** - Compute the slope and intercept of the regression line using least-squares regression. - Determine the standard error of the estimate and the correlation coefficient. - Plot the data points and the regression line on a graph. - Repeat the analysis by regressing x versus y (switch the roles of the variables). - Interpret the results of both regressions.
Transcribed Image Text:**17.3 Use Least-Squares Regression to Fit a Straight Line** **Table of Data:** | x | 0 | 2 | 4 | 6 | 9 | 11 | 12 | 15 | 17 | 19 | |---|---|---|---|---|---|----|----|----|----|----| | y | 5 | 6 | 7 | 6 | 9 | 8 | 7 | 10 | 12 | 12 | **Instructions:** - Compute the slope and intercept of the regression line using least-squares regression. - Determine the standard error of the estimate and the correlation coefficient. - Plot the data points and the regression line on a graph. - Repeat the analysis by regressing x versus y (switch the roles of the variables). - Interpret the results of both regressions.
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