Residual sum of squares

In statistics, the residual sum of squares (RSS) is the sum of squares of residuals,


 * $$RSS = \sum_{i=1}^n (y_i - f(x_i))^2. $$

In a standard regression model $$y_i = a+bx_i+\varepsilon_i\,$$, where a and b are coefficients, y and x are the regressand and the regressor, respectively, and &epsilon; is the "error term." The sum of squares of residuals is the sum of squares of estimates of &epsilon;i, that is


 * $$RSS = \sum_{i=1}^n (y_i - (a+bx_i))^2. $$

In general: total sum of squares = explained sum of squares + residual sum of squares.