Understanding logistic regression analysis.
Understanding logistic regression analysis.
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DOI：
10.11613/bm.2014.003
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发表时间：
2014
期刊：
影响因子：
3.3
通讯作者：
Sperandei S
中科院分区：
文献类型：
--
作者：
Sperandei S
逻辑回归用于在多个解释变量存在的情况下获得优势比。该过程与多元线性回归非常相似，除了响应变量是二项的。结果是每个变量对观察到的感兴趣事件的比值比的影响。其主要优点是通过分析所有变量的关联来避免混淆效应。在本文中，我们将使用示例解释逻辑回归过程，以使其尽可能简单。在对该技术进行定义后，重点介绍了结果的基本解释，然后讨论了一些特殊问题。
Logistic regression is used to obtain odds ratio in the presence of more than one explanatory variable. The procedure is quite similar to multiple linear regression, with the exception that the response variable is binomial. The result is the impact of each variable on the odds ratio of the observed event of interest. The main advantage is to avoid confounding effects by analyzing the association of all variables together. In this article, we explain the logistic regression procedure using examples to make it as simple as possible. After definition of the technique, the basic interpretation of the results is highlighted and then some special issues are discussed.