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该法可以有效消除经济数据的异方差性和多重共线性。It is an efficient way to eliminate the heterogeneity and the multicollinearity of the data.

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。The least Square estimates are not reliable when there exists multicollinearity in adjustment model.

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特别，当变量X多重相关性突出时，本文方法效果显著。Especially, the advantages of the method are marked, while the variables Xs multicollinearity being serious.

合理地选择参数，避免参数间的强复共线性，是参数有效估计的关键之一。Properly selecting parameters to avoid stronger multicollinearity is a key to efficient estimation of parameters.

不管是马氏田口的施密特正交化法，还是伴随矩阵法，都是通过改进马氏距离函数来解决强相关问题。For both Gram-Schmidt method and adjoint matrix method of MTS, multicollinearity is solved by improving Mahalanobis distance function.

此方法另一特点是可以消除输入因素的多重共线性，不需要大量样本作为输入。Another feature of this method is that the multicollinearity of input factors can be eliminated, so a lot of samples to input are not needed.

得出了最佳的回归模型，而且完全消除了多重共线性的影响。This time the results are wonderful and it gets the most appropriate regression models moreover the multicollinearity does not exist any more.

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结果去除了回归模型中变量间的多重共线性影响，建立了较为理想的关系模型。Results The effect of multicollinearity among variables were eliminated in the regression model and an ideal mathematical model was constructed.

消除多重共线性常用的参数改进方法有主成分回归和岭回归。The staple methods of elimination multicollinearity and betterment methods of parameters are principal component regression and ridge regression.

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。In Chapter 5 I inquire into the influence analysis of variable selections in modeling through data , multicollinearity andmodel mis-specification.

尽管如此，三位学者指出模型指标仍然存在一定程度的共线性关系。But, the three scholars pointed out that the items were still interrelated and one fruitful area for future research was to explore the nature and causes of the multicollinearity.

PLS2回归较好地克服了各指标间的多重共线性问题，通过此方法求得的顾客满意度指数更准确、合理。PLS2 regression successfully solve the multicollinearity problems among the indices, which indicates that the customer satisfaction indices computed are better and more reasonable.

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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.