The residual sum of squares also goes by several other names, including the sum of squared errors (SSE) and the sum of squared deviations (SSD).
Here's a breakdown:
- Residual Sum of Squares (RSS): This is the most common term. It represents the sum of the squares of the residuals, which are the differences between the observed values and the values predicted by the model.
- Sum of Squared Errors (SSE): This term is frequently used interchangeably with RSS. It emphasizes that the residuals represent the "errors" in the model's predictions.
- Sum of Squared Deviations (SSD): This term is also used to refer to the residual sum of squares. It highlights that the residuals are deviations from the predicted values.
In summary, all three terms (RSS, SSE, and SSD) essentially describe the same quantity: a measure of the discrepancy between the data and the model's predictions. A lower value indicates a better fit.
