Which statement regarding the interpretation of R^2 in linear regression is accurate?

R^2, or the coefficient of determination, is a key statistic in linear regression that quantifies how much of the variability in the dependent variable (y) can be explained by the independent variable(s) (x). The accurate interpretation of R^2 is that it represents the proportion of the variance in y that is predictable from x. Therefore, choice B correctly captures this meaning, clarifying that R^2 provides insight into the strength of the relationship between the independent variable and the dependent variable in the context of the model.

The other statements lack accuracy in their interpretations. For instance, R^2 does not measure variability in x explained by y, nor is it irrelevant for simple linear regression, as it remains a valuable measure even in this simpler context. Additionally, although a higher R^2 typically suggests a better fit of the model to the data, it does not guarantee accuracy in the model as it could be influenced by overfitting or other factors. Thus, choice B stands out as the best interpretation of R^2 in the context of linear regression.