The residuals are observations of the error variable e . Consequently, the minimized sum of squared deviations is called the sum of squares for error, denoted SSE.

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The residuals in a regression model are indeed observations of the error variable $e$, and the sum of squared deviations of these residuals from their mean (which is ideally zero) is referred to as the sum of squares for error (SSE). This measure is minimized in the process of finding the best-fitting line in least squares regression.

The Residuals Are Observations of the Error Variable E