| 1. | Properties of ols : minimize the sum of squared residuals
Ols性质：最小化残差平方和。 |
| 2. | Sum of squares of partial regression
偏回归平方和 |
| 3. | Estimated error sum of squares
估计误差平方和 |
| 4. | Sum of squares between groups
组间平方和 |
| 5. | Weighted sum of square
加权平方和 |
| 6. | Sum of squares
离差平方和 |
| 7. | This algebraic fact follows because the sum of squared residuals never increase when additional regressor are added to the model
此代数事实成立，因为当模型加入更多回归元时，残差平方和绝不会增加。 |
| 8. | This algebraic fact follows because the sum of squared residuals never increase when additional regressor are added to the model
这一数学事实成立，因为当模型加入更多回归元时，残差平方和决不会增加。 |
| 9. | Idea : because the ols estimates are chosen to minimize the sum of squared residuals , the ssr always increases when variables are dropped from the model
由于ols是用于最小化残差平方和,当有变量被从模型中舍弃时, ssr必定上升 |
| 10. | The sum of square differences ( ssd ) , which has been defined as square summation of corresponding pixel differences between current and previous frame , has been endowed a novel connotation in this paper
本文赋予当前帧总方差ssd新内涵,定义其为当前帧与前一帧对应像素差的平方和。 |
