体重指数、营养与体育锻炼之间有何关联？有序逻辑回归分析的应用。
How Are BMI, Nutrition, and Physical Exercise Related? An Application of Ordinal Logistic Regression.
作者信息
Wang Hongwei, Quintana Fernando G, Lu Yunlong, Mohebujjaman Muhammad, Kamronnaher Kanon
机构信息
Department of Mathematics and Physics, Texas A&M International University, Laredo, TX 78041, USA.
Department of Biology and Chemistry, Texas A&M International University, Laredo, TX 78041, USA.
出版信息
Life (Basel). 2022 Dec 14;12(12):2098. doi: 10.3390/life12122098.
BACKGROUND
This paper performs a detailed ordinal logistic regression study in an evaluation of a survey at a university in South Texas, USA. We show that, for categorical data in our case, ordinal logistic regression works well.
METHODS
The survey was designed according to the guidelines in diet and lifestyle from the American Heart Association and the United States Department of Agriculture and was sent out to all registered students at Texas A&M International University in Laredo, Texas. Data analysis included 601 students' results from the survey. Data analysis was conducted in Rstudio.
RESULTS
The results showed that, compared with students who do not have enough whole grain food and exercise, those who have enough in both tend to have normal BMIs. As age increases, BMI tends to be out of the normal range.
CONCLUSIONS
Because BMI in this research has three categories, applying an ordinal logistic regression model to describe the relationship between an ordered categorical response variable and more explanatory variables has several advantages compared with other models, such as the linear regression model.
背景
本文在美国南德克萨斯州一所大学的一项调查评估中进行了详细的有序逻辑回归研究。我们表明，对于我们案例中的分类数据，有序逻辑回归效果良好。
方法
该调查是根据美国心脏协会和美国农业部的饮食与生活方式指南设计的，并发送给了得克萨斯州拉雷多市德州农工国际大学的所有注册学生。数据分析包括601名学生的调查结果。数据分析在Rstudio中进行。
结果
结果表明，与没有足够全谷物食物和运动的学生相比，两者都充足的学生往往有正常的体重指数。随着年龄的增长，体重指数往往会超出正常范围。
结论
由于本研究中的体重指数有三个类别，与其他模型（如线性回归模型）相比，应用有序逻辑回归模型来描述有序分类响应变量与更多解释变量之间的关系有几个优点。
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