Residual sum of squares
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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 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.
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.
Contents
One explanatory variable
In a model with a single explanatory variable, RSS is given by:
where y_{i} is the i ^{th} value of the variable to be predicted, x_{i} is the i ^{th} value of the explanatory variable, and is the predicted value of y_{i} (also termed ). In a standard linear simple regression model, , where a and b are coefficients, y and x are the regressand and the regressor, respectively, and ε is the error term. The sum of squares of residuals is the sum of squares of estimates of ε_{i}; that is
where is the estimated value of the constant term and is the estimated value of the slope coefficient b.
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, is a k × 1 vector of true coefficients, and e is an n× 1 vector of the true underlying errors. The ordinary least squares estimator for is
The residual vector = , so the residual sum of squares is:
 ,
(equivalent to the squareroot of the norm of residuals); in full:
 ,
where H is the hat matrix, or the prediction matrix in linear regression.
Relation with Pearson's ProductMoment Correlation
The LeastSquares Regression Line is given by
, where , and, where and
Therefore where
The Pearson's ProductMoment Correlation is given by , therefore,
See also
 Sum of squares (statistics)
 Squared deviations
 Errors and residuals in statistics
 Lackoffit sum of squares
 Degrees of freedom (statistics)#Sum of squares and degrees of freedom
 Chisquared distribution#Applications
References
 Draper, N.R.; Smith, H. (1998). Applied Regression Analysis (3rd ed.). John Wiley. ISBN 0471170828.