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| ケンドール順位相関係数× | ピアソンの積率相関係数× | スペルマン順位相関係数× | |
|---|---|---|---|
| 分野 | 統計学 | 統計学 | 統計学 |
| 系統 | Hypothesis test | Hypothesis test | Hypothesis test |
| 提唱年≠ | 1938 | 1895 | 1904 |
| 提唱者≠ | Maurice G. Kendall | Karl Pearson | Charles Spearman |
| 種類≠ | Rank-based association measure | Parametric correlation | Nonparametric rank-based correlation |
| 原典≠ | Kendall, M. G. (1938). A new measure of rank correlation. Biometrika, 30(1–2), 81–93. DOI ↗ | Cohen, J. (1988). Statistical Power Analysis for the Behavioral Sciences (2nd ed.). Lawrence Erlbaum Associates. DOI ↗ | Spearman, C. (1904). The proof and measurement of association between two things. The American Journal of Psychology, 15, 72–101. DOI ↗ |
| 別名≠ | Kendall's tau, Kendall tau-b, tau correlation, Kendall Tau Korelasyonu | pearson r, product-moment correlation, bivariate correlation, Pearson Korelasyon Analizi | Spearman's rho, Spearman rank-order correlation, Spearman Sıra Korelasyonu |
| 関連 | 4 | 4 | 4 |
| 概要≠ | Kendall Tau is a nonparametric rank correlation coefficient introduced by Maurice G. Kendall in 1938 to measure the strength and direction of a monotone association between two ordinal or continuous variables. It is particularly suited to small samples and datasets containing many tied ranks, where the Spearman coefficient can be less stable. | The Pearson product-moment correlation coefficient (r) is a parametric measure of the direction and strength of the linear association between two continuous variables. Introduced by Karl Pearson in 1895, it remains the most widely used bivariate correlation statistic in the social, health, and natural sciences. The coefficient ranges from −1 (perfect negative linear relationship) to +1 (perfect positive), with 0 indicating no linear association. | The Spearman rank correlation coefficient (ρ) is a nonparametric measure of the monotonic association between two variables. Introduced by Charles Spearman in 1904, it converts raw observations to ranks and measures how consistently one variable increases as the other increases, without assuming a normal distribution or a linear relationship. |
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