Bayesian Modelling of The Distribution of Heart Rate (BPM) Among Nigerian Students


Authors : Adedoyin O. Taiwo; Abiodun A. Taiwo

Volume/Issue : Volume 8 - 2023, Issue 1 - January

Google Scholar : https://bit.ly/3IIfn9N

Scribd : https://bit.ly/3WZ3ZNS

DOI : https://doi.org/10.5281/zenodo.7606630

Heart Rate (bpm) is widely regarded as a very significant element in every human being. Numerous studies have been so far undertaken in this field, but a very few have really tried to touch the distribution of students' Heart Rate (bpm). The primary aim of this study is to investigate the heart rate distribution of faculty of science students at the University of Ibadan. A total number of 347 students' information was used in the analysis. The data was obtained in the faculty of science, a descriptive analysis of the data collected was used to investigate the distribution of the dataset, mean, standard deviation, minimum value and maximum value. The Kolmogorov - Smirnov’s test was used to validate the distribution of the likelihood. The Chi Square was used to test for the association between Gender and the distribution of Heart Rate. The descriptive analysis of the collected data revealed that the average heart rate and corresponding standard deviation of the observed data were 71.46 and 4.977, with the minimum value being 58 bpm and the maximum value being 87 bpm. The Kolmogorov-Smirnov test revealed that the data fits well with the normal distribution. The Chi Square test of independence revealed that there is no relationship between Gender and Heart Rate Distribution (bpm). For the Conjugate prior (which is informative), where the prior, the likelihood and the posterior was found to follow the normal distribution, the posterior mean (Bayes estimate) of the distribution of heart rate (bpm) is 71.55 while the posterior standard deviation is 4.65. The posterior credible interval for average heart rate (bpm) is [62.486, 80.71]. For the positive uniform prior (which is noninformative), the posterior follows a normal distribution, where the posterior mean (Bayes estimate) of the distribution of heart rate (bpm) was 71.46 while the posterior standard deviation was 4.977. The posterior means for both the Uniform and Normal priors are almost equal along with their credible intervals being close to the point estimate. But, considering the standard errors, the Normal prior performs slightly better than the Uniform prior. Hence, the normal likelihood is recommended for analyzing Heart Rate (bpm) in the Faculty of Science.

Keywords : Heart Rate (BPM), Uniform Prior, Normal Prior, Credible Interval, Normal Distribution.

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