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(a) (1) Use least-squares regression to fit a straight line to the following data: x 1 2 3 4 5 6 7 8 9 10 y 1.3 3.5 4.2 5.0 7.0 8.8 10.1 12.5 13.0 15.6 Along with the slope and intercept, compute the standard error of the estimate and the correlation coefficient. Plot the data and the regression line.

(a) (1) Use least-squares regression to fit a straight line to the following data: x 1 2 3 4 5 6 7 8 9 10 y 1.3 3.5 4.2 5.0 7.0 8.8 10.1 12.5 13.0 15.6 Along with the slope and intercept, compute the standard error of the estimate and the correlation coefficient. Plot the data and the regression line.

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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(a) (1) Use least-squares regression to fit a straight line to the following data: x 1 23 4 5 6 7 8 9 10 y 1.3 3.5 4.2 5.0 7.0 8.8 10.1 12.5 13.0 15.6 Along with the slope and intercept, compute the standard error of the estimate and the correlation coefficient. Plot the data and the regression line. (2) Repeat the analysis but for a straight line with a zero intercept. (3) Calculate the difference of R-values of these two regression models.
Transcribed Image Text:(a) (1) Use least-squares regression to fit a straight line to the following data: x 1 23 4 5 6 7 8 9 10 y 1.3 3.5 4.2 5.0 7.0 8.8 10.1 12.5 13.0 15.6 Along with the slope and intercept, compute the standard error of the estimate and the correlation coefficient. Plot the data and the regression line. (2) Repeat the analysis but for a straight line with a zero intercept. (3) Calculate the difference of R-values of these two regression models.
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