![]() In this case, knowing X does not help you predict Y. When R 2 equals 0.0, the best-fit curve fits the data no better than a horizontal line going through the mean of all Y values.But if you have replicate Y values at the same X value, it is impossible for the curve to go through every point, so R 2 has to be less than 1.00. R 2 equals 1.00 when the curve goes through every point.The simple answer is that R 2 is usually a fraction between 0.0 and 1.0, and has no units. What is the range of values R 2 can have? Another way to think about R 2 is the square of the correlation coefficient between the actual and predicted Y values.With experimental data (and a sensible model) you will always obtain results between 0.0 and 1.0. You can think of R 2 as the fraction of the total variance of Y that is explained by the model (equation).It compares the fit of your model to the fit of a horizontal line through the mean of all Y values. The value R 2 quantifies goodness of fit.
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