Application of Binary Logistic Regression Model to Assess the Likelihood of Overweight
American Journal of Theoretical and Applied Statistics
Volume 8, Issue 1, January 2019, Pages: 18-25
Received: Jan. 19, 2019;
Accepted: Feb. 20, 2019;
Published: Mar. 6, 2019
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Noora Shrestha, Department of Mathematics and Statistics, P. K. Multiple Campus, Tribhuvan University, Kathmandu, Nepal
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.
Application of Binary Logistic Regression Model to Assess the Likelihood of Overweight, American Journal of Theoretical and Applied Statistics.
Vol. 8, No. 1,
2019, pp. 18-25.
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