pearson相关的统计效应与效力

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--- pearson相关的统计效应与效力 [2023/04/06 00:20] – zhangruihao
+++ pearson相关的统计效应与效力 [2024/04/12 11:38] (当前版本) – aiyuheng
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-**Pearson相关的统计效应与效力**
+**Pearson相关的统计效应与效力（Statistical effects and potency of Pearson correlation）**
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   *用r的平方解释相关关系强度：一个变量的方差中，由X和Y间的相关解释的方差的比例。比如：当r=0.7时，Y变异的一部分能由X推出，r2=0.49，即Y 49%的变异能够由X推出。
+  *Pearson相关的效应：r=.10，小的效应；r=.30，中等效应；r=.50，大的效应。
+  *Pearson相关统计效力的影响因素：①效应大小；②样本容量；③单尾/双尾。
+  *相关描述两个变量之间的关系,但并不能解释变量相关的原因，即相关计算不能得到因果性推论。原因：在相关研究中, 研究者没有操纵一个 (或几个) 变量而保持其他变量不变。
+* Strength of correlation explained in terms of r squared: The proportion of variance in a variable explained by the correlation between X and Y. For example, when r=0.7, a portion of the variation in Y can be derived from X, and r2=0.49, that is, 49% of the variation in Y can be derived from X.
+*Pearson related effect: r=.10, small effect; r=.30, medium effect; r=.50, large effect.
+* The influencing factors of Pearson correlation statistical efficacy: ① effect size; ② Sample size; ③ Single tail/double tail.
+* Correlation describes the relationship between two variables, but does not explain why the variables are correlated, that is, correlation calculations do not yield causal inferences. Reason: In related studies, researchers did not manipulate one (or several) variables while keeping the others constant.

pearson相关的统计效应与效力.1680740435.txt.gz · 最后更改: 2023/04/06 00:20 由 zhangruihao