In order to solve the difficulty of interpreting variance of correlated variables， generalized variance and total variance approaches have been used to summarize covariance matrix into a single number as univariate variance generalization. the purpose of this paper is to trace the origin and development of the two methods. Discussion also focuses on the generalization of η2 to multivariate analysis as explained variance concept， and Wilks two maximum likelihood indices U and Λ. Three numerical examples were utilized to demonstrate the similarities and discrepences resulted from the two models. It was known that there is a relationship between generalized variance and total variance in their application in statistical analysis. The paper also briefly reviewed Miller’s complete model as possible compensation of total variance approach. *The entire paper is available in English version from the author. |
本站僅提供期刊文獻檢索。
