In contrast, you can see in the second dataset that when the R 2 is low, the observations are far from the model’s predictions. Note: The coefficient of determination is always positive, even when the correlation is negative. In other words, most points are close to the line of best fit: You can see in the first dataset that when the R 2 is high, the observations are close to the model’s predictions. The distance between the observations and their predicted values (the residuals) are shown as purple lines.The model’s predictions (the line of best fit) are shown as a black line.For example, the graphs below show two sets of simulated data: Graphing your linear regression data usually gives you a good clue as to whether its R 2 is high or low. It is the proportion of variance in the dependent variable that is explained by the model. More technically, R 2 is a measure of goodness of fit. ![]() ![]()
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