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An over-fit model occurs when you add terms for effects that are not important in the population, although they may appear important in the sample data. Models that have larger predicted R 2 values have better predictive ability.Ī predicted R 2 that is substantially less than R 2 may indicate that the model is over-fit. Use predicted R 2 to determine how well your model predicts the response for new observations.
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The adjusted R 2 value incorporates the number of predictors in the model to help you choose the correct model. R 2 always increases when you add a predictor to the model, even when there is no real improvement to the model. Use adjusted R 2 when you want to compare models that have different numbers of predictors. You should check the residual plots to verify the assumptions. Therefore, R 2 is most useful when you compare models of the same size.Ī high R 2 value does not indicate that the model meets the model assumptions. For example, the best five-predictor model will always have an R 2 that is at least as high the best four-predictor model. R 2 always increases when you add additional predictors to a model. The higher the R 2 value, the better the model fits your data. R 2 is the percentage of variation in the response that is explained by the model. However, a low S value by itself does not indicate that the model meets the model assumptions.
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The lower the value of S, the better the model describes the response. S is measured in the units of the response variable and represents the how far the data values fall from the fitted values. S Use S to assess how well the model describes the response. To determine how well the model fits your data, examine the goodness-of-fit statistics in the model summary table.