ANALYSIS OF STATISTICAL PERFORMANCES OF G20 COUNTRIES: AN APPLICATION WITH THE STATISTICAL VARIANCE PROCEDURE BASED MOOSRA METHOD

Abstract

In particular, the economic, social, technical and other activities of the countries with large economies based on decision systems created by statistical science can affect global economy. Therefore, analysis of statistical performances of countries with large economies is of great importance. In this context, in research, statistical performances of 19 countries in G20 group were measured with Statistical Variance Procedure (SVP) based MOOSRA method, over the latest and current Statistical Performance Indicator (SPI) values ​​developed by the World Bank for 2019. According to the findings, firstly, it was determined that the most important SPI component for countries was "Data Infrastructure" with SVP method. Secondly, it has been determined that the first three countries with the highest statistical performance are the USA, Canada and Italy, while the last three countries are China, Indonesia and Argentina. Apart from these, it has been concluded that countries with below average statistical performance value within scope of SVP-based MOOSRA method should increase their statistical performance in order to contribute more to the global economy. Finally, according to method sensitivity, separation distance and Pearson correlation coefficient analysis, it has been evaluated that statistical performances of SPI components and countries can be measured by SVP-based MOOSRA method, as well as SVP-based ARAS, TOPSIS and ROV methods