利用这些数据资料进行了复回归分析，得到了方程模型，并进行了边际及投入产出分析。

This information is used to do multidimensional regression analysis, and an equation is gained, then the model is used for marginal and input-output analysis.

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经由逐步复回归分析，发现孤寂感最主要的预测变项为忧郁状态、院友支持及子女支持，共可解释48.5%之变异量。

Stepwise multiple regression showed that depression, residents' support, and children's support explained a considerable amount of variance (48.5%) in loneliness.

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了解为何复回归允许在有干扰变数的情况下，分析单一结果和预测变项的关联性。

Understand why multiple regression techniques allow for the analysis of the relationship between an outcome and a predictor in the presence of confounding variables.

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