在实际工作中，人们在采用回归模型解释因果变量间的相关关系时，经常会遇到自变量之间存在幂乘关系的情况。
In practical work, it is frequent that there is power relation among independent variables when the correlation between dependent variable and independent variables is explained by regression model.
这给人深刻印象的做法排除了因果变量，斯坦福大学的诺阿-古德曼说：“他们实际上自行设计了一个实验以获取他们想要的信息。”
That showed an impressive determination to isolate the causal variables, says Stanford’s Noah Goodman: “They actually designed an experiment to get the information they wanted.”
否则，统计学家表示只有进行严格的临床试验，对比一个控制组、一个测试组以及一个变量，才能真正证明因果关系。
Otherwise, statisticians say only strict clinical trials with a control group and a test group and one variable can truly prove a cause-and-effect association.
两个变量之间存在的联系几乎没有谈到相关的理论，但这种关系通常被随意臆断成有因果性的。
That a relationship between two variables exists says very little about the underlying theory but often the relationship is casually assumed to be causal.
即便是那些精心设计、对这些变量已作调整的观察性研究也只能发现其中的相关性，而无法证实一种因果关系。
Even carefully constructed observational studies that correct for such variables can only find correlations, not prove a cause-and-effect relationship.
然后，章典的研究小组进行了一种被称为格兰杰因果分析的推演，以确定这些变量之间的相互因果关系。
The team then performed a statistical analysis called a Granger causality analysis to establish whether cause-effect relationships existed between any of them.
但是他也承认这项研究并未建立一个因果关系的关联，而且该研究没有控制许多可能的变量。
But he conceded that the research does not establish a causal link and that the study did not control for many possible variables.
我们的分析只能识别变量之间的关系，决不意味着因果关系。
Our analysis can only identify relationships among variables and in no way implies causation.
贝叶斯网络用因果关系图的形式表达变量间相互关系，实现复杂系统的故障模式和效应分析。
Variable correlation is expressed with consequence graph in Bayesian Networks (BN), analysis of failure mode and effect of complex system is realized.
在战略管理研究中涉及众多隐含变量的测量以及因果模型的验证，结构方程模型为解决这些问题提供了新的更好的方法。
Structural equation modeling has great abilities to treat measure error and offers a strong tool for measuring latent variable and verifying the causality models in strategy management research.
本文分阶段对两变量的因果关系进行实证分析，通过部分调整模型给出了基准利率对同业拆借率的短期效应和长期效应。
In this paper, we analyse this by Grange Causality Test and then we give the short-term effect and long-term effect using Partial Adjust Model.
以产品属性效用为细分变量是建立在因果关系变量而非描述性变量基础上的一种细分方法。
The segmentation by taking production attribute utility as segmentation variable is based on causal variable rather than descriptive one.
通径分析能够表明变量之间作用的因果关系，更深刻地揭示变量之间的关系。
Path Analysis could uncover the causality in interaction among variables, thus more could reveal the further relationship among variables.
本文应用回归模型和因果引导关系模型检验了我国于1998年开始公布的CCI与宏观经济变量之间的动态影响关系。
Using regressive model and Granger-Causality model, we investigate the dynamic relationships between macro-economic variables and CCI in China, which was published every month from 1998.
该模型还可以进一步检验变量之间的因果关系是长期或短期的因果关系。
Moreover, the model can test if there exists the long-term or short-term Granger causality relation between variables.
本文采用协整和因果检验方法，研究宏观经济变量、货币金融变量与我国债券市场价格波动的联动和因果关系。
This article applies causal testing method to study the correlation and causal relation between macro economic variation, monetary, financial variation and price movement of China bond market.
研究认为，我国能源消费是关于经济增长的内生变量，两者之间存在着双向的因果关系；
Papers holds the view that China's energy consumption is an endogenous variable on the economic growth, there is a two-way causation between them;
系统由变量的因果关系的其他变量净的工作职能。
Systems consist of variables that are causally related to other variables in a net work of functions.
这是可能的，这些链接不因果关系，而是家庭共同因素协变量。
It is possible that these links are not causal, but rather covariates of the common factor of family.
识别过程输入变量和输出变量，通过因果图、关系矩阵记录它们之间的关系。
Identify process input variables and process output variables, and document their relationships through cause and effect diagrams, relational matrices, etc.
通过对时间序列建立向量误差修正模型，运用单位根检验、协整检验、格兰杰因果关系检验、脉冲响应函数等方法精确地度量系统中变量之间相互影响的动态过程。
The article USES VECM, ADF Test, Johansen Test, Granger Causal Relation Test, Impulse Response Function to accurately measure the process that the variables influence each other in the system.
同时由于时间序列的非线性，常规的线性向量自回归模型难以正确描述经济变量之间的因果关系。
At the same time, due to the non-linear condition of time sequence, conventional linear Vector Autoregressive model can hardly characterize the causality among economic variables correctly.
前者基于输入变量和输出变量之间的因果关系，要求变量满足某些特定的统计假设；
The former based on the input variables and the causal relationship between output variables require variables to meet certain statistical assumptions;
前者基于输入变量和输出变量之间的因果关系，要求变量满足某些特定的统计假设；
The former based on the input variables and the causal relationship between output variables require variables to meet certain statistical assumptions;
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