There are two ways to assess the goodness of fit of a regression, one is the standard error of the estimate and the other is the R-square. How are they interpreted? What is the relation between the two?
Expert Answer
Interpretation: Standard error (say S) of the estimate is the measure of accuracy of the model. that how good regression line predicts.While R-square (say R) gives the goodness of fit.
S will tell distance between data points and regression line and we try to reduce it to minimum. While R-squared will explain R-squared = Explained variance / Total variance i.e.
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