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

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This statement is true. The residuals are the differences between the actual values and the predicted values, and they represent the error in the model. The sum of squared residuals is minimized in regression analysis to obtain the best-fit line, and this minimized value is called the sum of squares for error or SSE.

The Residuals Are Observations of the Error Variable ε\varepsilonε Consequently,the Minimized Sum of Squared Deviations Is Called the Sum

The Residuals Are Observations of the Error Variable $\varepsilon$ Consequently,the Minimized Sum of Squared Deviations Is Called the Sum