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What does the slope say about this relationship?       For every increase in X of 1, the corresponding average increase in Y is 1.2305   For every increase in X of 1, the corresponding average increase in Y is .4443   For every increase in X of .5, the corresponding average increase in Y is .4443   For every increase in X of .5, the corresponding average decrease in Y is .4443   For every increase in X of 1, the corresponding average decrease in Y is .4443

What does the slope say about this relationship?       For every increase in X of 1, the corresponding average increase in Y is 1.2305   For every increase in X of 1, the corresponding average increase in Y is .4443   For every increase in X of .5, the corresponding average increase in Y is .4443   For every increase in X of .5, the corresponding average decrease in Y is .4443   For every increase in X of 1, the corresponding average decrease in Y is .4443

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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What does the slope say about this relationship?
 

 

 
For every increase in X of 1, the corresponding average increase in Y is 1.2305
 
For every increase in X of 1, the corresponding average increase in Y is .4443
 
For every increase in X of .5, the corresponding average increase in Y is .4443
 
For every increase in X of .5, the corresponding average decrease in Y is .4443
 
For every increase in X of 1, the corresponding average decrease in Y is .4443
The relationship between two quantities X and Y is examined, and the association is shown in the scatterplot below. Y 8- 7- 6- 5- 4- 3+ 2+ 1- + 0.5 1 1.5 2 2.5 3 3.5 4 4.5 X If a linear model is considered, the regression analysis is as follows: Dependent variable: Y R-squared 84.7% VARIABLE COEFFICIENT = Intercept 1.2305 X .4443
Transcribed Image Text:The relationship between two quantities X and Y is examined, and the association is shown in the scatterplot below. Y 8- 7- 6- 5- 4- 3+ 2+ 1- + 0.5 1 1.5 2 2.5 3 3.5 4 4.5 X If a linear model is considered, the regression analysis is as follows: Dependent variable: Y R-squared 84.7% VARIABLE COEFFICIENT = Intercept 1.2305 X .4443
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