Title: Pearson's Correlation under the scope: Assessment of the efficiency of Pearson's correlation to select predictor variables for linear models Show Chinese title

Title: 皮尔逊相关性在范围内的评估：皮尔逊相关性在选择线性模型预测变量方面的效率评估

Abstract: This article examines the limitations of Pearson's correlation in selecting predictor variables for linear models. Using mtcars and iris datasets from R, this paper demonstrates the limitation of this correlation measure when selecting a proper independent variable to model miles per gallon (mpg) from mtcars data and the petal length from the iris data. This paper exhibits the findings by reporting Pearson's correlation values for two potential predictor variables for each response variable, then builds a linear model to predict the response variable using each predictor variable. The error metrics for each model are then reported to evaluate how reliable Pearson's correlation is in selecting the best predictor variable. The results show that Pearson's correlation can be deceiving if used to select the predictor variable to build a linear model for a dependent variable. Show Chinese abstract

Abstract: 本文探讨了皮尔逊相关性在选择线性模型预测变量方面的局限性。 使用R中的mtcars和iris数据集，本文展示了当从mtcars数据中选择适当的自变量来建模每加仑英里数（mpg）以及从iris数据中选择适当的自变量来建模花瓣长度时，这种相关性度量的局限性。 本文通过报告每个响应变量的两个潜在预测变量的皮尔逊相关性值来展示研究结果，然后分别使用每个预测变量构建一个线性模型来预测响应变量。 随后报告每个模型的误差指标，以评估皮尔逊相关性在选择最佳预测变量时的可靠性。 结果表明，如果用于选择构建因变量线性模型的预测变量，皮尔逊相关性可能会产生误导。