How reliable is the R-square statistic as a standalone measure for the quality of a linear regression model? Does a low R-square value invariably indicate an inadequate model fit in linear regression?
Can the R-square value alone adequately represent the efficacy of linear regression analysis? Can we conclude that a model is insufficiently fitted if the R-square value is low?
Is relying on R-square values alone for linear regression analysis advisable? Is a low R-square value a definitive sign of poor fit in a linear regression model?
What are the limitations of using R-square as the sole metric for assessing linear regression models? How should a low R-square value be interpreted in the context of a model’s fit?
Why might the R-square value not be sufficient in evaluating a linear regression model’s fit? Does a low R-square necessarily mean that the linear regression has failed to capture the data relationship?
In what ways is R-square an incomplete metric for linear regression analysis? How do you justify a low R-square value in terms of linear regression model fit?
Is it enough to assess the goodness of fit in linear regression using only the R-square? What implications does a low R-square have for the validity of a linear regression analysis?
Can we consider R-square to be an all-encompassing measure for linear regression model accuracy? When faced with a low R-square measurement, how do you assess the adequacy of the regression model?
Should R-square be the exclusive determinant of fit for linear regression models? In linear regression, is a low R-square value synonymous with a poor model fit?
Is the R-square measure sufficient for linear regression analysis? What if the r-square value is low, does this mean that the fit is not good enough?
