The goodness-of-fit of regression is measured by the coefficient of determination R2. This is the proportion of the total variation in Y that can be explained or accounted for by the variation in the predictor variables {X}. The higher the value of R2, the better the model fits the data. Often R2 is adjusted for the bias brought by the degree of freedom of the model and the limited number of observations as = R2- p × , where n is the number of observations, and p is the number of predictors.