The Experts below are selected from a list of 300 Experts worldwide ranked by ideXlab platform
X. L. Hou - One of the best experts on this subject based on the ideXlab platform.
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Principal component Regression Analysis with SPSS.
Computer methods and programs in biomedicine, 2003Co-Authors: R. X. Liu, J. J. Kuang, Q. Gong, X. L. HouAbstract:The paper introduces all indices of multicollinearity diagnoses, the basic principle of principal component Regression and determination of 'best' equation method. The paper uses an example to describe how to do principal component Regression Analysis with SPSS 10.0: including all calculating processes of the principal component Regression and all operations of linear Regression, factor Analysis, descriptives, compute variable and bivariate correlations procedures in SPSS 10.0. The principal component Regression Analysis can be used to overcome disturbance of the multicollinearity. The simplified, speeded up and accurate statistical effect is reached through the principal component Regression Analysis with SPSS.
R. X. Liu - One of the best experts on this subject based on the ideXlab platform.
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Principal component Regression Analysis with SPSS.
Computer methods and programs in biomedicine, 2003Co-Authors: R. X. Liu, J. J. Kuang, Q. Gong, X. L. HouAbstract:The paper introduces all indices of multicollinearity diagnoses, the basic principle of principal component Regression and determination of 'best' equation method. The paper uses an example to describe how to do principal component Regression Analysis with SPSS 10.0: including all calculating processes of the principal component Regression and all operations of linear Regression, factor Analysis, descriptives, compute variable and bivariate correlations procedures in SPSS 10.0. The principal component Regression Analysis can be used to overcome disturbance of the multicollinearity. The simplified, speeded up and accurate statistical effect is reached through the principal component Regression Analysis with SPSS.
Wu Xiao-hu - One of the best experts on this subject based on the ideXlab platform.
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COMPARING CLUSTER Regression Analysis WITH MULTIPLE LINEAR Regression.
Modern Preventive Medicine, 2006Co-Authors: Wu Xiao-huAbstract:Objective:To compare cluster Regression Analysis with multiple linear Regression.Methods:Using multiple linear Regression and cluster Regression Analysis to study the relationship among the ten conventional items of liver function test:TP、ALB、GLB、A/G、TBIL、DBIL、IBIL、AST、ALT、AST/ALT whether the population was normal or not.Results:After variate transformation by clustering,the data was more similar to normal distribution.Because of the existence of multi-collinearity among the items,the effect of the multiple linear Regression was unsatisfactory.The ten items were divided into 5 clusters by cluster Analysis:TBIL.DBIL IBIL、ALB GLB A/G、AST ALT、TP、AST/ALT.Cluster Regression Analysis indicates that the relative importance of the 5 clusters in diagnosing patient's condition of liver disease was TBIL、DBIL、IBILALB、GLB A/GAST、ALTTPAST/ALT.In the same cluster,TBIL was more important than the others and so does A/G.Conclusion:Cluster Regression Analysis is more effective to deal with non-normal distribution data and multi-collinearity.
J. J. Kuang - One of the best experts on this subject based on the ideXlab platform.
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Principal component Regression Analysis with SPSS.
Computer methods and programs in biomedicine, 2003Co-Authors: R. X. Liu, J. J. Kuang, Q. Gong, X. L. HouAbstract:The paper introduces all indices of multicollinearity diagnoses, the basic principle of principal component Regression and determination of 'best' equation method. The paper uses an example to describe how to do principal component Regression Analysis with SPSS 10.0: including all calculating processes of the principal component Regression and all operations of linear Regression, factor Analysis, descriptives, compute variable and bivariate correlations procedures in SPSS 10.0. The principal component Regression Analysis can be used to overcome disturbance of the multicollinearity. The simplified, speeded up and accurate statistical effect is reached through the principal component Regression Analysis with SPSS.
Q. Gong - One of the best experts on this subject based on the ideXlab platform.
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Principal component Regression Analysis with SPSS.
Computer methods and programs in biomedicine, 2003Co-Authors: R. X. Liu, J. J. Kuang, Q. Gong, X. L. HouAbstract:The paper introduces all indices of multicollinearity diagnoses, the basic principle of principal component Regression and determination of 'best' equation method. The paper uses an example to describe how to do principal component Regression Analysis with SPSS 10.0: including all calculating processes of the principal component Regression and all operations of linear Regression, factor Analysis, descriptives, compute variable and bivariate correlations procedures in SPSS 10.0. The principal component Regression Analysis can be used to overcome disturbance of the multicollinearity. The simplified, speeded up and accurate statistical effect is reached through the principal component Regression Analysis with SPSS.
