Application of Quantile Regression and Ordinary Least Squares Regression in Modeling Body Mass Index in Federal Medical Centre Jalingo, Nigeria

Ogunmola, Adeniyi Oyewole
Okoye, Benjamin Ekene

Journal of Multidisciplinary Science: MIKAILALSYS
Vol 3 No 2 (2025): Journal of Multidisciplinary Science: MIKAILALSYS

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.