Mathematical Analysis of Regression Model Epidemiology

Authors

  • Prof. Dr. Sumit Kumar Banerjee Papua New Guinea University of Technology, Department of Mathematics & Computer Science, PMB, Morobe Province, Lae 411, Papua New Guinea
  • Mr. Boaz Andrews Papua New Guinea University of Technology, Department of Mathematics & Computer Science, PMB, Morobe Province, Lae 411, Papua New Guinea

Keywords:

Epidemiology, Regression Line, Logic, Public health, Policymakers

Abstract

Statistical modeling techniques, specifically regression line analysis have become important analytical tools and are contributing immensely to the field of epidemiology. However, many users do not understand their effective use and applications. Underlying epidemiological concepts and not the statistics should govern or justify the proper use and application of any modeling exercise. Main utility of the regression line analysis lies in its ability to provide a general but practical conceptual framework for casual problems, explaining and evaluating the role of biases, confounders and effect modifiers. Successful modeling of complex data is a part science, part statistics and part experience, but the major part is logic or common sense. Findings of this research article focuses on the contributions of regression analysis towards the pedagogical study of epidemiological models by enhancing the research process and serving as an effective tool for communicating findings to public health managers and policymakers and fostering interdisciplinary collaboration.

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Published

2023-04-24

How to Cite

Prof. Dr. Sumit Kumar Banerjee, & Mr. Boaz Andrews. (2023). Mathematical Analysis of Regression Model Epidemiology. American Scientific Research Journal for Engineering, Technology, and Sciences, 93(1), 39–49. Retrieved from https://www.asrjetsjournal.org/index.php/American_Scientific_Journal/article/view/8694

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Articles