Objective To explore the application of marginal regression models on multivariate correlation analysis in medical research.
目的探讨边际回归模型在医学研究领域多变量相关分析中的应用。
Based on the analysing of the data, selected relevant factors, made a series of tests and amendments with models, then created forest volume estimation optimal multivariate linear regression model.
在分析数据的基础上，选择了相关遥感因子和定性因子，并通过一系列模型的检验与修正，建立了公顷蓄积量估测的最优多元线性回归模型。
The clinicopathological parameters associated with HCC rupture were investigated using univariate and multivariate logistic regression models.
研究人员通过单变量及多变量逻辑回归模型，对破裂HCC相关的临床病理学参数进行了考察。
However, when try to regression independent variables to the dependent variables in multivariate analysis, the coefficients are all non-zero. So these models lack of interpretation.
然而，在多元线性分析方法中，当求解因变量对自变量的回归时，得到的组合系数往往均不为零，因此这种回归模型的主要缺点是缺乏可解释性。
However, when try to regression independent variables to the dependent variables in multivariate analysis, the coefficients are all non-zero. So these models lack of interpretation.
然而，在多元线性分析方法中，当求解因变量对自变量的回归时，得到的组合系数往往均不为零，因此这种回归模型的主要缺点是缺乏可解释性。
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