<Tr> <Td> </Td> <Td> This article needs additional citations for verification . Please help improve this article by adding citations to reliable sources . Unsourced material may be challenged and removed . (April 2013) (Learn how and when to remove this template message) </Td> </Tr> <P> 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 . </P> <P> 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 . </P> <P> In a model with a single explanatory variable, RSS is given by: </P>

How do you find the sum of squared residuals