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The table below shows data collected on the length and weight of sand sharks. Length 60 62 64 66 68 70 72 Weight 105 114 124 131 139 149 158 A linear regression analysis is run on this data and results in a linear regression equation of y = 4.36x - 156.14 where x is the length of the shark in inches and y is the weight of the sharks in pounds. The correlation coefficient (r) is calculated as 0.9991. Using the correlation coefficient (r), is this linear regression model a good fit for our data? Why or why not? A. Yes, because the r-value is positive. B. No, because the r-value is close to 1. C. Yes, because the r-value is close to 1. D. No, because the r-value is not close to 0.

The table below shows data collected on the length and weight of sand sharks. Length 60 62 64 66 68 70 72 Weight 105 114 124 131 139 149 158 A linear regression analysis is run on this data and results in a linear regression equation of y = 4.36x - 156.14 where x is the length of the shark in inches and y is the weight of the sharks in pounds. The correlation coefficient (r) is calculated as 0.9991. Using the correlation coefficient (r), is this linear regression model a good fit for our data? Why or why not? A. Yes, because the r-value is positive. B. No, because the r-value is close to 1. C. Yes, because the r-value is close to 1. D. No, because the r-value is not close to 0.

Advanced Engineering Mathematics
Advanced Engineering Mathematics
10th Edition
ISBN:9780470458365
Author:Erwin Kreyszig
Publisher:Erwin Kreyszig
Chapter2: Second-order Linear Odes
Section: Chapter Questions
Problem 1RQ
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The table below shows data collected on the length and weight of sand sharks.

Length 60 62 64 66 68 70 72
Weight 105 114 124 131 139 149 158

A linear regression analysis is run on this data and results in a linear regression equation of y = 4.36x - 156.14 where x is the length of the shark in inches and y is the weight of the sharks in pounds. The correlation coefficient (r) is calculated as 0.9991.

Using the correlation coefficient (r), is this linear regression model a good fit for our data? Why or why not?

A. Yes, because the r-value is positive.

B. No, because the r-value is close to 1.

C. Yes, because the r-value is close to 1.

D. No, because the r-value is not close to 0.

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