As part of a bear population study, data were gathered on a sample of black bears in the western United States to examine the relationship between the bear's neck girth (the distance around the neck) and the weight of the bear. Below is some of the output from a least-squares regression analysis examining the linear relationship between neck girth and the weight of each of the bears. Assume conditions for regression are reasonably met. Which one of the following are the correct value and corresponding interpretation for the correlation coefficient? Predictor Coef SE Coef T P
Costant -293.53 19.27 -15.23 0.000
Neck. G 22.6447 0.8574 26.41 0.000
S = 30.1994 R-S = 93.6 R-Sq(adj) = 93.45
A) The correlation coefficient is 0.936, and 93.6% of the variation in a bear's weight can be explained by its neck girth.
B) The correlation coefficient is 0.936. There is a strong positive linear relationship between a bear's neck girth and its weight.
C) The correlation coefficient is 0.967, and 96.7% of the variation in a bear's weight can be explained by its neck girth.
D) The correlation coefficient is -0.967. There is a strong negative linear relationship between a bear's neck girth and its weight.
E) The correlation coefficient is 0.967. There is a strong positive linear relationship between a bear's neck girth and its weight.