In statistics, the residual sum of squares (RSS), also known as the sum of squared residuals (SSR) or the sum of squared errors of prediction (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. 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.
One explanatory variable
In a model with a single explanatory variable, RSS is given by:
where yi is the i th value of the variable to be predicted, xi is the i th value of the explanatory variable, and
where
Matrix expression for the OLS residual sum of squares
The general regression model with n observations and k explanators, the first of which is a constant unit vector whose coefficient is the regression intercept, is
where y is an n × 1 vector of dependent variable observations, each column of the n × k matrix X is a vector of observations on one of the k explanators,
The residual vector
(equivalent to the square-root of the norm of residuals); in full:
where H is the hat matrix, or the prediction matrix in linear regression.