Relationship between Karl Pearson’s Coefficient of Skewness (Sk) and Ginni’s Coefficient of Concentration (G) under the purview of Digital Payments in India

Abstract

Aims: Statistics is one of the most important fields. It carries out observation, collection, analysis and interpretation of collected data with necessary techniques and tools for analysis. There are many parameters which help in analyzing the data. Amongst them, Karl Pearson’s Coefficient of Skewness and Ginni’ Coefficient of Concentration are very important. Karl Pearson’s Coefficient of Skewness plays an important role in measuring the symmetry. Symmetry and its measure help in knowing the manner of distribution carried out by the population. Karl Pearson’s Coefficient of Skewness is represented by Sk. Apart from Karl Pearson’s coefficient of Skewness, Ginni’s Coefficient of Concentration is also one more term which is the ratio of Ginni’s Coefficient of Mean deviation and two times of Mean of the distribution.  The range of Ginni’s Coefficient of Concentration is from 0 to 1. It is represented as G.  This paper tries to find the relationship between Karl Pearson’s Coefficient of Skewness and Ginni’s Coefficient of Concentration. Methodology: Theoretical framework is being constructed with the help of known formulas of Karl Pearson’s Coefficient of Skewness and Ginni’s Coefficient of Concentration. A new formula is being generated between Karl Pearson’s Coefficient of Skewness and Ginni’s Coefficient of Concentration by mediating mean as the common factor between the two parameters. Result: There is a positive relationship between Karl Pearson’s Coefficient of Skewness and Ginni’s Coefficient of Concentration. Conclusion: With the help of the generated formula, Karl Pearson’s Coefficient of Skewness can be calculated once Ginni’s Coefficient of Concentration is known where all other parameters are considered to be known.