MGMT 1050 Chapter Notes - Chapter 16: Explained Variation, Total Variation, Bias Of An Estimator
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So when sse is small, the fit is excellent and the model should be used. We judge the value of see by comparing to the mean of the dependant variable (mean of sample y). If see is small in comparison, then the linear regression can be used. However see has no upper limit, so it is not great to measure whether a model is a good fit or not. The other part, the difference between the actual value of y and the predicted value of y, is residual we cannot explain it with the model. This part of the difference is unexplained by the variation in x - sse. Thus, the total variation in y = the explained variation + the unexplained variation in y. The sum of squares for regression (ssr) is a measure of the explained variation in y. The sum of squares for error (sse) is a measure of the unexplained variation in y.
