# Residual sum of squares

## Statistical measure of the discrepancy between data and an estimation model / From Wikipedia, the free encyclopedia

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In statistics, the **residual sum of squares** (**RSS**), also known as the **sum of squared residuals** (**SSR**) or the **sum of squared estimate of errors** (**SSE**), is the sum of the squares of residuals (deviations predicted from actual empirical values of data). It is a measure of the discrepancy between the data and an estimation model, such as a linear regression. A small RSS indicates a tight fit of the model to the data. It is used as an optimality criterion in parameter selection and model selection.

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In general, total sum of squares = explained sum of squares + residual sum of squares. For a proof of this in the multivariate ordinary least squares (OLS) case, see partitioning in the general OLS model.