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Estimation of the simple correlation coefficient

机译:简单相关系数的估计

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This article investigates some unfamiliar properties of the Pearson product—moment correlation coefficient for the estimation of simple correlation coefficient. Although Pearson’s r is biased, except for limited situations, and the minimum variance unbiased estimator has been proposed in the literature, researchers routinely employ the sample correlation coefficient in their practical applications, because of its simplicity and popularity. In order to support such practice, this study examines the mean squared errors of r and several prominent formulas. The results reveal specific situations in which the sample correlation coefficient performs better than the unbiased and nearly unbiased estimators, facilitating recommendation of r as an effect size index for the strength of linear association between two variables. In addition, related issues of estimating the squared simple correlation coefficient are also considered.
机译:本文研究了皮尔逊积的一些陌生特性,即矩相关系数,用于估计简单相关系数。尽管Pearson的r是有偏的,除了有限的情况,并且文献中已经提出了最小方差无偏估计量,但是由于其简单性和受欢迎程度,研究人员在他们的实际应用中经常使用样本相关系数。为了支持这种做法,本研究检查了r的均方误差和几个突出的公式。结果揭示了在特定情况下,样本相关系数的表现要优于无偏和近似无偏的估计量,从而有利于推荐r作为两个变量之间线性关联强度的影响大小指标。另外,还考虑了估计平方简单相关系数的相关问题。

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