In solving the regression equations, collinearity in the design matrix can result in inaccurate parameter estimates.
在解回归方程时，设计矩阵中的共线性可能产生不精确的参数估计。
It is usually required that the design matrix is column full rank in applying recursive weighted least squares algorithm.
通常在使用递推加权最小二乘算法时，需要设计矩阵列满秩。
The computational results demonstrate that the partial root root estimator can improve the LS estimator when the design matrix is ill conditioned.
计算结果表明，在设计阵呈病态时，部分根方估计确能改善LS估计。
Results show that iterative refinement using the SVD can improve regression coefficient estimates in the cases where the design matrix is highly collinear.
结果表明，在设计矩阵高度共线性时，用奇异值分解的迭代加细可以改进回归系数的估计。
Results show that iterative refinement using the SVD can improve regression coefficient estimates in the cases where the design matrix is highly collinear.
结果表明，在设计矩阵高度共线性时，用奇异值分解的迭代加细可以改进回归系数的估计。
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