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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，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。

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) ，来识别和解释所观察到的自变量与因变量之间关系的底层机制或过程。一个中介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。与自变量和因变量之间存在直接的因果关系不同，中介模型主张：自变量通过影响中介变量（不可观测），进而影响因变量。