Application of Quantile Regression and Ordinary Least Squares Regression in Modeling Body Mass Index in Federal Medical Centre Jalingo, Nigeria
Article Sidebar
Page Numbers:
552-558
Digital Object Identifier:
Viewed: 223 times
Downloaded: 228 times
LYAS Publisher cordially invites qualified professionals to serve as Editors or Reviewers. Your expertise will make an important contribution to maintaining and strengthening the academic quality of our publications. Interested applicants are kindly requested to complete the application form at the following link: Editors & Reviewers
Please feel free to contact us if you need any further information about the submission process or if you have any additional questions.
Authors:Adeniyi Oyewole Ogunmola1*, Benjamin Ekene Okoye2
Copyright :Authors retain copyright and grant the journal right of first publication.
Explore Our Journals
Find the journal that best fits your research. If this journal does not match your manuscript’s scope, explore our portfolio below to select a more suitable publication venue.
Main Article Content
Abstract
Body mass index is a measure of nutritional status of an individual. Malnutrition is a leading public health problem in developing countries like Nigeria, it is also a major cause of morbidity and mortality. In this study, Body mass index is modeled using ordinary least squares method and quantile regression method. Data is collected from Antiretroviral therapy Clinic in Federal Medical Centre, Jalingo. Variables in the data collected are the Body mass index, age, weight, height, sex and occupation of the patients. Results showed that the ordinary least square regression and quantile regression at 25th percentile, median percentile, 75th percentile and 95th percentile fit the data. Weight, age, sex and height of patients are significant in determining the BMI of the patients when OLS method is applied. While weight, sex and height of patients are significant in determining the BMI of the patients. It is also discovered that OLS method fits the data more than quantile regression method using AIC and MSE.
Citation Metrics:
Downloads
Citation Metrics & Similar Scopus Articles
-
Wang J. (2027)Analysis and Applications of Neuromorphic Memristors in Artificial Intelligence ComputingNano Micro Letters, 19(1)
-
Lee H. (2027)Low-Temperature All-Solid-State BatteriesNano Micro Letters, 19(1)
-
Feng X. (2027)Designing Efficient Inverted Perovskite Solar Cells with Self-Assembled Monolayer Hole Transport LayersNano Micro Letters, 19(1)
Article Details
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
References
Adedia D., Boakye A.A., Mensah D., Lokpo S.Y., Afeke I., and Duedu K.O. (2020). Comparative assessment of anthropometric and bioimpedence methods for determining adiposity. Heliyon, Volume 6, Issue 12, e05740, ISSN 2405-8440, https://doi.org/10.1016/j.heliyon.2020.e05740. (https://www.sciencedirect.com/science/article/pii/S2405844020325834)
Akaike, H. (1974). A new look at the statistical model identification. IEEE Transactions on Automatic Control, 19(6), 716-723.
Burnham, K. P., & Anderson, D. R. (2004). Multimodel inference: Understanding AIC and BIC in model selection. Sociological Methods & Research, 33(2), 261-304.
Hurvich, C. M., & Tsai, C. L. (1989). Regression and time series model selection in small samples. Biometrika, 76(2), 297-307.
Hastie, T., Tibshirani, R., & Friedman, J. (2009). The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Springer Science & Business Media.
James, G., Witten, D., Hastie, T., & Tibshirani, R. (2013). An Introduction to Statistical Learning with Applications in R. Springer.
Mohajan D. and Mohajan H.K. (2023). Body Mass Index (BMI) is a Popular Anthropometric Tool to Measure Obesity Among Adults. Paradigm Academic Press Journal of Innovations in Medical Research ISSN 2788-7022 APR. 2023 VOL.2, NO.4. doi:10.56397/JIMR/2023.04.06
Montgomery, D. C., Peck, E. A., & Vining, G. G. (2012). Introduction to Linear Regression Analysis (5th ed.). John Wiley & Sons.
National Heart, Lung, and Blood Issue (NIH) (2024). Assessing Your Weight and Health Risk. Assessing Your Weight and Health Risk (nih.gov) September, 15th 2024.
Safaei M., Sundararajan E.A., Driss M., Boulila W., Shapi’I A. (2021). Understanding the causes & consequences of obesity and reviewing various machine learning approaches used to predict obesity. Computers in Biology and Medicine, Volume 136, 2021, 104754, ISSN 0010-4825, https://doi.org/10.1016/j.compbiomed.2021.104754. (https://www.sciencedirect.com/science/article/pii/S0010482521005485)
World Health Organsation (WHOa) (2024). Health Topics Obesity. https://www.who.int/health-topics/obesity September, 15th 2024.
World Health Organsation (WHOb) (2024). Health Topics Diabetics. https://www.who.int/health-topics/diabetics September, 15th 2024.