Solved – Is sum of squared residuals smaller than sum of squared errors in linear regression

In the linear regression model, the true error vector $U=Y-Xbeta$ is based upon the true value of the unknown coefficient vector $beta$. Meanwhile, the OLS residual vector $U^* =Y-Xbeta^*$ uses the OLS estimator $beta'$ of $beta$, where $U^*$ and $beta^*$ are the estimators of $U$ and $beta$.

Then is it true that $U'Ult {U^*}'U^*$?


Suppose you have a true relationship described by whatever slope $betaneqbeta^*$, but just two data points. You fit a simple linear regression. Then, the straight line will connect the two points and thus the residuals will be zero, and hence so will be ${U^*}'U^*$.

In general, recall that OLS is, by definition, the procedure that minimizes the sum of squared differences from $Y$, so that it cannot be beaten by other linear functions of the same set of regressors $X$.

In relation to this, the many posts on overfitting on this site and elsewhere provide further counterexamples: by making your model increasingly complex through, e.g., including powers of regressors, interactions etc., you can reduce the "training error" (that is, obtain smaller sum of squared residuals) of your model.

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