![]() SST measures how far the data are from the mean, and SSE measures how far the data are from the model’s predicted values. All three are based on two sums of squares: Sum of Squares Total (SST) and Sum of Squares Error (SSE). Three statistics are used in Ordinary Least Squares (OLS) regression to evaluate model fit: R-squared, the overall F-test, and the Root Mean Square Error (RMSE). The fit of a proposed regression model should therefore be better than the fit of the mean model. The mean model, which uses the mean for every predicted value, generally would be used if there were no informative predictor variables. A well-fitting regression model results in predicted values close to the observed data values. ![]()
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