Unlike the coefficient of determination, the coefficient of correlation: A) indicates the percentage of variation explained by a regression model. B) measures the strength of association between the two variables more exactly. C) indicates whether the slope of the regression line is positive or negative. Your answer: B was incorrect. The correct answer was C) indicates whether the slope of the regression line is positive or negative. In a simple linear regression the coefficient of determination (R2) is the squared correlation coefficient, so it is positive even when the correlation is negative. Why is B an incorrect statement? The coefficient of correlation does measure the strength of the association between two variables. Is the statement incorrect because the coeffeicnet of determination doesn’t do that? Also, is choice A a definition of Coefficient of Determination?
B gets you with the “more exactly” phrase, which might not be true. A is r^2 is
yea thats what i was thinking. that its technically wrong becuase c. of det. doesnt do that.
R = Correlation Coefficient. This measures how well two variables are associated. It can be positive or negative. R^2=Coefficient of Determination. This measures how well one variable can explain another one. Since it is R^2, it can never be negative, and it is always less than R unless R is 1. B R^2 does do that, but it is a vague answer. Not sure if it does it better than R.
This question is looking for something that is different b/w R and R^2 so… since A and B are both qualities of R and R^2; they are both wrong. C is the only one where there are stark differences b/w R and R^2. I would pick C.