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 [2024/04/12 11:37] – aiyuheng | 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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| *相关描述两个变量之间的关系, | *相关描述两个变量之间的关系, | ||
| * 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. | * 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. | ||
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| *Pearson related effect: r=.10, small effect; r=.30, medium effect; r=.50, large effect. | *Pearson related effect: r=.10, small effect; r=.30, medium effect; r=.50, large effect. | ||
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| * The influencing factors of Pearson correlation statistical efficacy: ① effect size; ② Sample size; ③ Single tail/double tail. | * The influencing factors of Pearson correlation statistical efficacy: ① effect size; ② Sample size; ③ Single tail/double tail. | ||
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| * 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. | * 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.1712921848.txt.gz · 最后更改: 2024/04/12 11:37 由 aiyuheng