explained sum of squares造句

For wide classes of linear models, the total sum of squares equals the explained sum of squares plus the residual sum of squares.
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, which measures how much variation there is in the observed data, and to the residual sum of squares, which measures the variation in the modelling errors.
where " R " 2 is the coefficient of determination and " VAR " err and " VAR " tot are the variance of the estimated residuals and the sample variance of the dependent variable . " SS " " err " ( the sum of squared predictions errors, equivalently the residual sum of squares ), " SS " " tot " ( the total sum of squares ), and " SS " " reg " ( the sum of squares of the regression, equivalently the explained sum of squares ) are given by
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