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In statistics, a mediation model seeks to identify and explain the mechanism or process that underlies an observed relationship between an independent variable and a dependent variable via the inclusion of a third hypothetical variable, known as a mediator variable (also a mediating variable, intermediary variable, or intervening variable). Rather than a direct causal relationship between the independent variable and the dependent variable, a mediation model proposes that the independent variable influences the (non-observable) mediator variable, which in turn influences the dependent variable. Thus, the mediator variable serves to clarify the nature of the relationship between the independent and dependent variables.

In statistics, a mediation model seeks to identify and explain the mechanism or process that underlies an observed relationship between an independent variable and a dependent variable via the inclusion of a third hypothetical variable, known as a mediator variable (also a mediating variable, intermediary variable, or intervening variable). Rather than a direct causal relationship between the independent variable and the dependent variable, a mediation model proposes that the independent variable influences the (non-observable) mediator variable, which in turn influences the dependent variable. Thus, the mediator variable serves to clarify the nature of the relationship between the independent and dependent variables.

在统计学中，调解模型试图通过加入第三个假设变量，即中介变量(也是中介变量、中介变量或中间变量) ，来确定和解释自变量和因变量之间的观察关系所依据的机制或过程。一个调解模型没有在自变量和因变量之间建立直接的因果关系，而是提出自变量影响(不可观察的)调解变量，这反过来又影响因变量。因此，中介变量的作用是阐明自变量和因变量之间关系的性质。

在统计学中，中介模型试图通过加入第三个假设变量，即中介变量(也是中介变量、中介变量或中间变量) ，来识别和解释自变量和因变量之间的观察关系所依据的机制或过程。一个调解模型没有在自变量和因变量之间建立直接的因果关系，而是提出自变量影响(不可观察的)调解变量，这反过来又影响因变量。因此，中介变量的作用是阐明自变量和因变量之间关系的性质。