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Standard Error of the Regression vs. R-squared - Statistics By Jim

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The standard error of the regression (S) and R-squared are two key goodness-of-fit measures for regression analysis. While R-squared is the most well-known amongst the goodness-of-fit statistics, I think it is a bit over-hyped. The standard error of the regression is also known as residual standard error. In this post, I’ll compare these two goodness-of-fit statistics. We’ll also work through a regression example to help make the comparison. I think you’ll see that the oft overlooked standard error of the regression can tell you things that the high and mighty R-squared simply can’t. At the very least, you’ll find that the standard error of the regression is a great tool to add to your statistical toolkit! Learn more about Goodness-of-Fit: Definition & Tests. You can find the standard error of the regression, also known as the standard error of the estimate and the residual standard error, near R-squared in the goodness-of-fit section of most statistical output. Both of these measures

The standard error of the regression (S) and R-squared are two key goodness-of-fit measures for regression analysis. While R-squared is the most well-known amongst the goodness-of-fit statistics, I think it is a bit over-hyped. The standard error of the regression is also known as residual standard error. In this post, I’ll compare these two goodness-of-fit statistics. We’ll also work through a regression example to help make the comparison. I think you’ll see that the oft overlooked standard error of the regression can tell you things that the high and mighty R-squared simply can’t. At

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