Explained sum of squares

In statistics, the explained sum of squares (ESS), alternatively known as the model sum of squares or sum of squares due to regression (SSR – not to be confused with the residual sum of squares (RSS) or sum of squares of errors), is a quantity used in describing how well a model, often a regression model, represents the data being modelled. In particular, the explained sum of squares measures how much variation there is in the modelled values and this is compared to the total sum of squares (TSS), which measures how much variation there is in the observed data, and to the residual sum of squares, which measures the variation in the error between the observed data and modelled values. The explained sum of squares (ESS) is the sum of the squares of the deviations of the predicted values from the mean value of a response variable, in a standard regression model — for example, yi = a + b1x1i + b2x2i + ... + εi, where yi is the i th observation of the response variable, xji is the i th observation of the j th explanatory variable, a and bj are coefficients, i indexes the observations from 1 to n, and εi is the i th value of the error term. In general, the greater the ESS, the better the estimated model performs. If and are the estimated coefficients, then is the i th predicted value of the response variable. The ESS is then: where the value estimated by the regression line . In some cases (see below): total sum of squares (TSS) = explained sum of squares (ESS) + residual sum of squares (RSS). The following equality, stating that the total sum of squares (TSS) equals the residual sum of squares (=SSE : the sum of squared errors of prediction) plus the explained sum of squares (SSR :the sum of squares due to regression or explained sum of squares), is generally true in simple linear regression: Square both sides and sum over all i: Here is how the last term above is zero from simple linear regression So, Therefore, 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.