residual sum of squares

statistics

Also known as: SSR

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July 13, 2026

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In statistics, the residual sum of squares (RSS) is a measure of the unexplained variation in a dependent variable within a statistical model. It represents the sum of the squared differences between the actual observed values of the dependent variable and the values predicted by the regression model.1

The RSS is a key component in calculating the coefficient of determination (R²), which indicates how well a model fits the data. A lower RSS generally suggests a better fit, as it means the model's predictions are closer to the actual data points. The RSS is also known as the error sum of squares (SSE).21

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statistics
Analysis of variance and goodness of fit
…is referred to as the residual sum of squares. For the data in Figure 4, SSE is the sum of the squared distances from each point in the scatter diagram (see Figure 4) to the estimated regression line: Σ(y − ŷ)². SSE is also commonly referred to as the error…
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…is referred to as the residual sum of squares. For the data in Figure 4, SSE is the sum of the squared distances from each point in the scatter diagram (see Figure 4) to the estimated regression line: Σ(y − ŷ)². SSE is also commonly referred to as the error…
Read More