American Journal of Theoretical and Applied Statistics

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Application of Binary Logistic Regression Model to Assess the Likelihood of Overweight

Received: Jan. 19, 2019    Accepted: Feb. 20, 2019    Published: Mar. 06, 2019
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Abstract

This study attempts to assess the likelihood of overweight and associated factors among the young students by analyzing their physical measurements and physical activity index. This paper has classified four hundred and fifteen subjects and precisely estimated the likelihood of outcome overweight by combining body mass index and CUN-BAE calculated. Multicollinearity is tested with multiple regression analysis. Box-Tidwell Test is used to check the linearity of the continuous independent variables and their logit (log odds). The binary regression analysis was executed to determine the influences of gender, physical activity index, and physical measurements on the likelihood that the subjects fall in overweight category. The sensitivity and specificity described by the model are 55.9% and 96.9% respectively. The increase in the value of waist to height ratio and neck circumference and drop in physical activity index are associated with the increased likelihood of subjects falling to overweight group. The prevalence of overweight is higher (27.8%) in female than in male (14.7%) subjects. The odds ratio for gender reveals that the likelihood of subjects falling to overweight category is 2.6 times higher in female compared to male subjects.

DOI 10.11648/j.ajtas.20190801.13
Published in American Journal of Theoretical and Applied Statistics ( Volume 8, Issue 1, January 2019 )
Page(s) 18-25
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

Overweight, Waist to Height Ratio, Neck Circumference, Binary Logistic Model, Odds Ratio

References
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[10] Hung, S. P., Chen, C. Y., Guo, F. R., Chang, C. I., and Jan, C. F. (2017). Combine body mass index and body fat percentage measures to improve the accuracy of obesity screening in young adults. Obesity Research and Clinical Practice, 11(1), 11-18.
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[19] Kleinbaum, G. D., and Klein, M. (2010). Logistic regression: A self learning text (3rd ed.). New York: Springer.
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    Noora Shrestha. (2019). Application of Binary Logistic Regression Model to Assess the Likelihood of Overweight. American Journal of Theoretical and Applied Statistics, 8(1), 18-25. https://doi.org/10.11648/j.ajtas.20190801.13

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    Noora Shrestha. Application of Binary Logistic Regression Model to Assess the Likelihood of Overweight. Am. J. Theor. Appl. Stat. 2019, 8(1), 18-25. doi: 10.11648/j.ajtas.20190801.13

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    AMA Style

    Noora Shrestha. Application of Binary Logistic Regression Model to Assess the Likelihood of Overweight. Am J Theor Appl Stat. 2019;8(1):18-25. doi: 10.11648/j.ajtas.20190801.13

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  • @article{10.11648/j.ajtas.20190801.13,
      author = {Noora Shrestha},
      title = {Application of Binary Logistic Regression Model to Assess the Likelihood of Overweight},
      journal = {American Journal of Theoretical and Applied Statistics},
      volume = {8},
      number = {1},
      pages = {18-25},
      doi = {10.11648/j.ajtas.20190801.13},
      url = {https://doi.org/10.11648/j.ajtas.20190801.13},
      eprint = {https://download.sciencepg.com/pdf/10.11648.j.ajtas.20190801.13},
      abstract = {This study attempts to assess the likelihood of overweight and associated factors among the young students by analyzing their physical measurements and physical activity index. This paper has classified four hundred and fifteen subjects and precisely estimated the likelihood of outcome overweight by combining body mass index and CUN-BAE calculated. Multicollinearity is tested with multiple regression analysis. Box-Tidwell Test is used to check the linearity of the continuous independent variables and their logit (log odds). The binary regression analysis was executed to determine the influences of gender, physical activity index, and physical measurements on the likelihood that the subjects fall in overweight category. The sensitivity and specificity described by the model are 55.9% and 96.9% respectively. The increase in the value of waist to height ratio and neck circumference and drop in physical activity index are associated with the increased likelihood of subjects falling to overweight group. The prevalence of overweight is higher (27.8%) in female than in male (14.7%) subjects. The odds ratio for gender reveals that the likelihood of subjects falling to overweight category is 2.6 times higher in female compared to male subjects.},
     year = {2019}
    }
    

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    JO  - American Journal of Theoretical and Applied Statistics
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    AB  - This study attempts to assess the likelihood of overweight and associated factors among the young students by analyzing their physical measurements and physical activity index. This paper has classified four hundred and fifteen subjects and precisely estimated the likelihood of outcome overweight by combining body mass index and CUN-BAE calculated. Multicollinearity is tested with multiple regression analysis. Box-Tidwell Test is used to check the linearity of the continuous independent variables and their logit (log odds). The binary regression analysis was executed to determine the influences of gender, physical activity index, and physical measurements on the likelihood that the subjects fall in overweight category. The sensitivity and specificity described by the model are 55.9% and 96.9% respectively. The increase in the value of waist to height ratio and neck circumference and drop in physical activity index are associated with the increased likelihood of subjects falling to overweight group. The prevalence of overweight is higher (27.8%) in female than in male (14.7%) subjects. The odds ratio for gender reveals that the likelihood of subjects falling to overweight category is 2.6 times higher in female compared to male subjects.
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Author Information
  • Department of Mathematics and Statistics, P. K. Multiple Campus, Tribhuvan University, Kathmandu, Nepal

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