The least Square estimates are not reliable when there exists multicollinearity in adjustment model.
当平差模型中存在复共线关系时，未知参数的最小二乘估计很不可靠。
In Chapter 5 I inquire into the influence analysis of variable selections in modeling through data , multicollinearity andmodel mis-specification.
第五章探讨了建摸中变量选择的影响分析，刻划了数据、复共线性关系和模型错定对自变量选择的影响。
Based on this, an on-line evaluation model of process stability with statistical method and partial-least-square regression (PLSR) was set up which overcome the multicollinearity of input parameters.
采用统计分析和偏最小二乘回归方法提出了过程稳定性在线评价模型，克服了输入变量严重多重相关性的问题。
Further, on this basis we derived an instrumental variable regression model. After disregarding the effect of multicollinearity among the explanatory variables, we verify the accuracy of the results.
并以此为基础，衍生出工具变量回归模型，在剔除了解释变量多重共线性的影响后，验证了结果的准确性。
Further, on this basis we derived an instrumental variable regression model. After disregarding the effect of multicollinearity among the explanatory variables, we verify the accuracy of the results.
并以此为基础，衍生出工具变量回归模型，在剔除了解释变量多重共线性的影响后，验证了结果的准确性。
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