1. The paper discusses two questions:one about multicollinearity, and the other about the weakness of Partial Least Square Regression.

2. PLS2 regression successfully solve the multicollinearity problems among the indices, which indicates that the customer satisfaction indices computed are better and more reasonable.

3. Properly selecting parameters to avoid stronger multicollinearity is a key to efficient estimation of parameters.

4. The staple methods of elimination multicollinearity and betterment methods of parameters are principal component regression and ridge regression .

5. 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.

6. For the problem of multicollinearity and the number of explanatory variables rather than practical issues, S.

7. Despite the infection of multicollinearity, it forms a regression equation, the main component of which is real customer potential value considering customer credit risk.

8. Results The effect of multicollinearity among variables were eliminated in the regression model and an ideal mathematical model was constructed.

9. Pseudo Adaptation Data (PAD) method was proposed for decreasing the degree of multicollinearity.

10. Result indicates that Fuzzy Principal Component Method can improve multicollinearity of Least Squares Estimate.

11. Conclusion:This method has the practically applied value in resolving the multicollinearity.

12. Results:The uncertainty of parameter estimation and the difficulty in explaining the outcome due to the multicollinearity in mult-regression analysis are removed.

13. 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.

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14. This time the results are wonderful and it gets the most appropriate regression models moreover the multicollinearity does not exist any more.

15. Especially, the advantages of the method are marked, while the variables Xs multicollinearity being serious.

16. It is an efficient way to eliminate the heterogeneity and the multicollinearity of the data.

17. 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.

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

19. For both Gram-Schmidt method and adjoint matrix method of MTS, multicollinearity is solved by improving Mahalanobis distance function.

20. The parameter estimation problem in linear model is considered when multicollinearity and outliers exist simultaneously.