[Overview of logistic regression model analysis and application].

Logistic regression is a kind of multiple regression method to analyze the relationship between a binary outcome or categorical outcome and multiple influencing factors, including multiple logistic regression, conditional logistic regression, polytomous logistic regression, ordinal logistic regression and adjacent categorical logistic regression. This paper illustrates the basic principle, independent variable selection and assignment, applied condition, model evaluation and diagnosis for multiple logistic regression model. Moreover, the principle and application for polytomous logistic regression and ordinal logistic regression models were also introduced. By providing SAS codes and detailed explanations of the result for an example of obesity, readers could be able to better understand logistic regression model, and apply this method correctly to their research and daily work, so as to improve their capacity of the data analysis. logistic回归是研究一个二分类或多分类反应变量与多个影响因素之间关系的多因素分析方法，它由一族回归模型组成，包括二分类结果的多重logistic回归、配对资料的条件logistic回归、多分类结果的logistic回归、有序结果的累积优势logistic回归和有序结果的相邻优势logistic回归。本文从实际应用的角度出发，介绍多重logistic回归分析方法，包括模型原理、自变量筛选及赋值、应用条件、模型评价和模型诊断等内容，也阐述了多分类结果变量的logistic回归和有序结果的累积优势logistic回归的原理和应用，并结合实例给出SAS程序和结果解释，以帮助读者掌握logistic回归分析方法，在科研和工作实践中能正确使用，以提高数据使用效率和统计分析水平。.