# Total sum of squares

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The value of the total sum of squares depends on the data being analyzed and the test that is being done.

In statistical linear models, (particularly in standard regression models), the total sum of squares is the sum of the squares of the difference of the dependent variable and its grand mean:

$\sum_{i=1}^{n}\left(y_{i}-\bar{y}\right)^2$.

Furthermore, total sum of squares = explained sum of squares + residual sum of squares.