Application of Stepwise Regression and Path Analysis in Principal Component

【Objective】The purpose of the study was to seek one practical method to assess the importance of detected attributes in multivariable systems and to reduce the data dimensions.【Method】Based on introducing stepwise regression analysis and path analysis into principal component analysis(PCA) by an example.【Result】The stepwise regressions of detected indicators against principal components were powerful in variances choice,variances with significant influence on component were remained.The path analysis revealed the direct influence and indirect influence of detected indicators on the principal component,which showed the intereaction between the indicators.Based on the determination coefficient of indicators and percentages of principal component eigenvalues,the influences of indicators on research objective can be evaluated.According to the evaluation,indicators with essential influence on the research objective can be extracted and the numbers of detected indicators in the system can be reduced.【Conclusion】PCA combined with stepwise regressions and path analysis is an useful tool in multivariable systems,which can assess the variables and choose the most important viable in a complex data to reduce the data dimensions in practice.