| 1. | R2 generally increases when a regressor is added to a regression
当回归中加入另外的解释变量时， r2通常会上升。 |
| 2. | Exception : if the new regressor is perfectly multicollinear with the original regressors , then ols cannot be implemented
例外：如果这个新解释变量与原有的解释变量完全共线，那么ols不能使用。 |
| 3. | 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。 |
| 4. | 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。 |
| 5. | If ols chooses any value other than zero , it must be that this value reduced the ssr relative to the regression that excludes the regressor
如果ols使此解释变量取任何非零系数，那么加入此变量之后， ssr降低了。 |
| 6. | In practice it is extremely unusual for an estimated coefficient to be exactly zero , so in general the ssr will decrease when a new regressor is added
实际操作中，被估计系数精确取零是极其罕见的，所以，当加入一个新解释变量后，一般来说， ssr会降低。 |
| 7. | If ols happens to choose the coefficient on the new regressor to be exactly zero , then ssr will be the same whether or not the second variable is included in the regression
如果ols恰好使第二个解释变量系数取零，那么不管回归是否加入此解释变量， ssr相同。 |
| 8. | The overall idea is that the system of robotic manipulators is decomposed as two parts : one is nominal system with perfect knowledge of dynamic model and the other is system with uncertainties . ctc is used to control nominal system . for uncertainties system , we utilize the regressor of robotic system or bounding function on uncertainties to design
基本思想都是将不确定性机器人系统分解成标称系统和不确定系统：对于标称系统，采用计算力矩控制；对于不确定系统，利用机器人系统的回归矩阵或集中不确定性上界的包络函数，设计不同的补偿控制器。 |
