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. Approximately 89% of the variability in height can be explained by the regression equation. 2. The regression equation relating shoe length to height might have a negative intercept. 3. Because the value of r is less than 1, we should characterize this relationship as being weak. 4.The correlation coefficient should have the units of “inches.” 5. The regression equation relating shoe length to height must have a slope equal to 0.89.
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. Approximately 89% of the variability in height can be explained by the regression equation.
2. The regression equation relating shoe length to height might have a negative intercept.
3. Because the value of r is less than 1, we should characterize this relationship as being weak.
4.The
5. The regression equation relating shoe length to height must have a slope equal to 0.89.
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