Computational Journal of Mathematical and Statistical Sciences 2(2), 223–239 DOI:10.21608/CJMSS.2023.229207.1014 https://cjmss.journals.ekb.eg/ A Statistical Analysis of Excess Mortality Mean at Covid-19 Md Nurul Raihen 1 * , Sultana Akter 2 , Fariha Tabassum 3 , Farjana Jahan 2 , Shakera Begum 2 1 Department of Mathematics and Computer Science, Fontbonne University, Saint Louis, 63105, MO, USA; nraihen@fontbonne.edu 2 Department of Statistics, Western Michigan University, Kalamazoo 49006, MI, USA; sbg2612@wmich.edu 3 Department of Sociology, Western Michigan University, Kalamazoo, 49006, MI, USA; fbv2349@wmich.edu * Correspondence: nraihen@fontbonne.edu Abstract: When it comes to making assessments about public health, the mortality rate is a very important factor. The COVID-19 pandemic has exacerbated well-known biases that affect the mea- surement of mortality, which varies with time and place. The COVID-19 pandemic took the world off surveillance, and since the outbreak, it has caused damage that many would have thought unthink- able in the present era. By estimating excess mortality for 2020 and 2021, we provide a thorough and consistent evaluation of the COVID-19 pandemic’s effects. Excess mortality is a term used in epidemiology and public health to describe the number of fatalities from all causes during a crisis that exceeds what would be expected under ’normal’ circumstances. Excess mortality has been used for thousands of years to estimate health emergencies and pandemics like the 1918 ”Spanish Flu”6. Excess mortality occurs when actual deaths exceed previous data or recognized patterns. It could demonstrate how a pandemic affected mortality rate. The estimates of excess mortality presented in this research are generated using the procedure, data, and methods described in detail in the methods section and briefly summarized in this study. We explored different regression models in order to find the most effective factor for our estimates. We predict the pandemic period all-cause deaths in locations lack- ing complete reported data using the Binary logistic regression, and Probit regression analysis count framework. Standardized residual plots, AIC, and Variance Inflation Factor (VIF) after checking all of those, we found some significant predictors from our choosing model , and the coefficient of all predictors gave the information that some factors have positive effect, and some has a negative effect at excess mortality at COVID-19 (2020-2021). Keywords: COVID-19, Excess Mortality, Pandemic, Probit Regression, Logistic regression. Mathematics Subject Classification: 62J12; 62G08. Received: 14 August 2023; Revised: 15 September 2023; Accepted: 20 September 2023; Online: 24 September 2023. Copyright: © 2023 by the authors. Submitted for possible open access publication under the terms and conditions of the Creative Commons Attribution (CC BY) license.