2. Given the following datalist of the height and weight pairs: Height 68 69 70 70 71 71 72 72 73 73 74 75 Weight 151 146 150 160 170 160 163 180 170 175 178 188 a. Compute the linear correlation coefficient for the height and weight pairs anti interpret it. b. Compute the coefficient of determination and interpret it. c. Find the least squares regression line and interpret the slope.
2. Given the following datalist of the height and weight pairs: Height 68 69 70 70 71 71 72 72 73 73 74 75 Weight 151 146 150 160 170 160 163 180 170 175 178 188 a. Compute the linear correlation coefficient for the height and weight pairs anti interpret it. b. Compute the coefficient of determination and interpret it. c. Find the least squares regression line and interpret the slope.
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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need better explanation

Transcribed Image Text:2. Given the following datalist of the height and weight pairs:
Height 68 69 70 70 71 71 72 72 73 73 74 75
Weight 151 146 150 160 170 160 163 180 170 175 178 188
a. Compute the linear correlation coefficient for the height and weight pairs
ant interpret it.
b. Compute the coefficient of determination and interpret it.
c. Find the least squares regression line and interpret the slope.
s) d. Test, at the 2% level of significance, whether the variable x is useful for
predicting y based on the information in the height and weight data set.
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