Investigating Covid 19 data for G20, EU and OECD countries via using time series analysis & cluster analysis

Year 2020, Volume: 4 Issue: 2, 424 - 432, 31.12.2020

Mehmet Güray Ünsal Reşat Kasap

Abstract

Shortly after Covid 19 virus first appeared, it turned into an epidemic that threatened the health of the world. Many countries have difficulties to control the spread of the virus. It causes many casualties and also affects the countries from a socio-economic aspect. Therefore, it is an important issue that needs to be examined in detail. In this study, the detailed research for the Covid 19 data of G20 (Group of 20), EU (European Union) and OECD (Organisation for Economic Cooperation and Development) countries is carried out by using different statistical methods. It is aimed to present different perspectives for researchers by discussing important findings and results.

Keywords

Covid 19, G20, EU, OECD, Statistical analysis

References

• Medical News Today (2020) Retrieved May 2020, from https://medicalnewstoday.com/articles/COVID19#incubation-period Box, G. E. P., (1994) Time series analysis : forecasting and control. Englewood Cliffs, N.J. Prentice Hall. Ünsal, M.G., & Kasap, R., (2014) Cases of Residual Types in Diagnostic Checking for ARMA Model. Hacettepe Journal of Mathematics and Statistics 43(3):1-10. http://hjms.hacettepe.edu.tr/uploads/7cc26d6d-b394-4c77933e-24fd1b92796e.pdf
• Montgomery, D.C., (2015) Introduction to time series analysis and forecasting. New Jersey Wiley. Johnson, R.A., & Wichern, D.W., (2007) Applied Multivariate Statistical Analysis. 6th Edition, Pearson Prentice Hall, Upper Saddle River.
• MacQueen, J.B., (1967) Some Methods for Classification and Analysis of Multivariate Observations. In: Proceedings of the 5th Berkeley Symposium on Mathematical Statistics and Probability, Volume 1: Statistics, University of California Press, Berkeley, 281-297. Data Hub (2020) Retrieved May 2020, from https://datahub.io/core/covid-19#data