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Correlation between obesity and income: A statistical analysis using linear regression models
- 1 Shanghai American School, Shanghai, 200000, China
* Author to whom correspondence should be addressed.
Abstract
The rate of obesity across the world has seen a steady increase in the past several decades. This disease can lead to significant harm associated with one’s body functioning and mental health. The potential causes of obesity incorporate social, environmental, and personal factors. This article attempts to cover the correlation between obesity and income level using a linear regression model. Samples were extracted from the National Health and Nutrition Examination Survey about 545 individuals from 2017 to 2020. The dataset was subdivided into four cohorts, which include non-Hispanic White male, non-Hispanic White female, non-Hispanic Black male, and non-Hispanic Black female, respectively, to avoid variables that might confound with the explanatory variable. The study found out that the correlation for White male, White female, and Black female can be either positive, negative, or non-existent, and positive on the other hand for the Black male cohort. However, the study concluded that the linear regression model is ineffective for the topic of analysis as the metrics indicate. The incompatibility of the model can be attributed to the fact that other variables that can possibly confound with the explanatory variable are neglected and not filtered out.
Keywords
Obesity, BMI, income-to-poverty ratio, linear regression, confounding variables.
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Cite this article
Jiang,Y. (2024). Correlation between obesity and income: A statistical analysis using linear regression models. Theoretical and Natural Science,51,1-8.
Data availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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