标题：The Chi-plot and Its Asymptotic Confidence Interval for Analyzing Bivariate Dependence: An Application to the Average Intelligence and Atheism Rates across Nations Data
摘要：Bivariate data analysis plays a key role in several areas wherethe variables of interest are obtained in a paired form, leading to the con-sideration of possible association measures between them. In most cases,it is common to use known statistics measures such as Pearson correlation,Kendall's and Spearman's coecients. However, these statistics measuresmay not represent the real correlation or structure of dependence betweenthe variables. Fisher and Switzer (1985) proposed a rank-based graphicaltool, the so called chi-plot, which, in conjunction with its Monte Carlo basedcon dence interval can help detect the presence of association in a randomsample from a continuous bivariate distribution. In this article we constructthe asymptotic con dence interval for the chi-plot. Via a Monte Carlo sim-ulation study we discovery the coverage probabilities of the asymptotic andthe Monte Carlo based con dence intervals are similar. A immediate advan-tage of the asymptotic con dence interval over the Monte Carlo based one isthat it is computationally less expensive providing choices of any con dencelevel. Moreover, it can be implemented straightforwardly in the existingstatistical softwares. The chi-plot approach is illustrated in on the averageintelligence and atheism rates across nations data.
关键词：Analysis of dependence; chi-plot; con dence intervals.
