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17. When the heights (in inches) and shoe lengths (also in inches) were measured for a large random sample of individuals, it was found that r = 0.89, and a regression equation was constructed in order to further explore the relationship between shoe length and height, with height being the response variable.  From this information, what can we conclude? 1. The regression equation relating shoe length to height must have a positive intercept. 2. Approximately 89% of the variability in height can be explained by the regression equation. 3. Because the value of r is less than 1, we should characterize this relationship as being weak. 4. The regression equation relating shoe length to height must have a slope equal to 0.89. 5. The correlation coefficient should have no units.

17. When the heights (in inches) and shoe lengths (also in inches) were measured for a large random sample of individuals, it was found that r = 0.89, and a regression equation was constructed in order to further explore the relationship between shoe length and height, with height being the response variable.  From this information, what can we conclude? 1. The regression equation relating shoe length to height must have a positive intercept. 2. Approximately 89% of the variability in height can be explained by the regression equation. 3. Because the value of r is less than 1, we should characterize this relationship as being weak. 4. The regression equation relating shoe length to height must have a slope equal to 0.89. 5. The correlation coefficient should have no units.

College Algebra
College Algebra
7th Edition
ISBN:9781305115545
Author:James Stewart, Lothar Redlin, Saleem Watson
Publisher:James Stewart, Lothar Redlin, Saleem Watson
Chapter1: Equations And Graphs
Section: Chapter Questions
Problem 10T: Olympic Pole Vault The graph in Figure 7 indicates that in recent years the winning Olympic men’s...
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17. When the heights (in inches) and shoe lengths (also in inches) were measured for a large random sample of individuals, it was found that r = 0.89, and a regression equation was constructed in order to further explore the relationship between shoe length and height, with height being the response variable.  From this information, what can we conclude?


1. The regression equation relating shoe length to height must have a positive intercept.
2. Approximately 89% of the variability in height can be explained by the regression equation.
3. Because the value of r is less than 1, we should characterize this relationship as being weak.
4. The regression equation relating shoe length to height must have a slope equal to 0.89.
5. The correlation coefficient should have no units.
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