The value of the total sum of squares [TSS] depends on the data being analyzed and the test that is being done. In statistical linear models, (particularly in standard regression models), the TSS is the sum of the squares of the difference of the dependent variable and its grand mean:
- <math>\sum_{i=1}^{n}\left(y_{i}-\bar{y}\right)^2.</math>
For wide classes of linear models: Total sum of squares = explained sum of squares + residual sum of squares.
