A HYPOTHETICAL ANALYSIS USING MULTIPLE LINEAR REGRESSION TO ASSESS MORTALITY ASSOCIATED WITH ARSENIC-CONTAMINATED RICE CONSUMPTION
By
Mohammad Shakil1, Tassaddaq Hussain2, Rakhshinda Jabeen3, Aneeqa Khadim4, Jai Narain Singh5, and Mohammad Ahsanullah6
1Department of Mathematics, Miami Dade College, Hialeah, FL, USA - 33012
2,4Department of Statistics, Mirpur University of Science and Technology, Mirpur, Pakistan - 10250
3Department of Medicine, Dow University of Health Sciences, Karachi, Pakistan - 74200
5Department of Mathematics & Computer Science, Barry University, Miami Shores, FL, USA - 33161
6Professor Emeritus, Rider University, Lawrenceville, NJ, USA - 08648
Email: mshakil@mdc.edu, rakhshinda.jabeen@duhs.edu.pk, tafkho2000@gmail.com, Aneeqa89@gmail.com, jsingh@mail.barry.edu, ahsan@rider.edu (Received: July 02, 2025; In format: September 11, 2025; Revised: October 05, 2025; Accepted: October 08, 2025)
DOI: https://doi.org/10.58250/jnanabha.2025.55202
Abstract
This study presents a hypothetical analysis utilizing a multiple linear regression model to evaluate the association between mortality and various health and demographic factors linked to the consumption of arsenic-contaminated rice. In the absence of actual epidemiological data, simulated data were generated to illustrate the modeling approach and explore potential relationships. Key predictor variables included arsenic exposure levels, cancer rate, body mass index, blood pressure, blood sugar, life expectancy, population size, and overall death rate. Model estimation and diagnostic assessments were performed to ensure statistical validity and to examine the influence of individual predictors. The analysis revealed notable associations between mortality and several variables, particularly arsenic exposure, cancer rate, and blood sugar, suggesting a multifactorial impact on health outcomes. While the findings are based on hypothetical data, the study highlights the potential health risks posed by chronic arsenic ingestion through dietary sources. This work contributes a preliminary framework for quantitative assessment and demonstrates the utility of regression modeling in guiding future empirical investigations and public health policy with actual data.
2020 Mathematical Sciences Classification: 65F359, 15A12, 15A04, 62J05.
Keywords and Phrases: Arsenic contamination; rice consumption; multiple linear regression; mortality analysis; hypothetical analysis; simulated data; public health modeling; environmental exposure.