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

Noora Shrestha

doi:doi:10.11648/j.ajtas.20190801.13

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

Noora Shrestha

Published in American Journal of Theoretical and Applied Statistics (Volume 8, Issue 1)

Received: 19 January 2019 Accepted: 20 February 2019 Published: 6 March 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.

Published in	American Journal of Theoretical and Applied Statistics (Volume 8, Issue 1)
DOI	10.11648/j.ajtas.20190801.13
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), 2019. Published by Science Publishing Group

Keywords

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

References

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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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ACS 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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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://article.sciencepublishinggroup.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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TY  - JOUR
T1  - Application of Binary Logistic Regression Model to Assess the Likelihood of Overweight
AU  - Noora Shrestha
Y1  - 2019/03/06
PY  - 2019
N1  - https://doi.org/10.11648/j.ajtas.20190801.13
DO  - 10.11648/j.ajtas.20190801.13
T2  - American Journal of Theoretical and Applied Statistics
JF  - American Journal of Theoretical and Applied Statistics
JO  - American Journal of Theoretical and Applied Statistics
SP  - 18
EP  - 25
PB  - Science Publishing Group
SN  - 2326-9006
UR  - https://doi.org/10.11648/j.ajtas.20190801.13
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.
VL  - 8
IS  - 1
ER  -

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Author Information

Noora Shrestha

Department of Mathematics and Statistics, P. K. Multiple Campus, Tribhuvan University, Kathmandu, Nepal

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APA Style

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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ACS 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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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

Copy | Download

@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://article.sciencepublishinggroup.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}
}

Copy | Download

TY  - JOUR
T1  - Application of Binary Logistic Regression Model to Assess the Likelihood of Overweight
AU  - Noora Shrestha
Y1  - 2019/03/06
PY  - 2019
N1  - https://doi.org/10.11648/j.ajtas.20190801.13
DO  - 10.11648/j.ajtas.20190801.13
T2  - American Journal of Theoretical and Applied Statistics
JF  - American Journal of Theoretical and Applied Statistics
JO  - American Journal of Theoretical and Applied Statistics
SP  - 18
EP  - 25
PB  - Science Publishing Group
SN  - 2326-9006
UR  - https://doi.org/10.11648/j.ajtas.20190801.13
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.
VL  - 8
IS  - 1
ER  -

Copy | Download