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| Cramer's V× | 费舍尔精确检验× | 逻辑回归× | |
|---|---|---|---|
| 领域≠ | 统计学 | 统计学 | 研究统计学 |
| 方法族≠ | Hypothesis test | Hypothesis test | Process / pipeline |
| 起源年份≠ | 1946 | 1922 | 1958 |
| 提出者≠ | Harald Cramér | R. A. Fisher | David Roxbee Cox |
| 类型≠ | Nonparametric association measure | Exact test of independence for categorical data | Method |
| 开创性文献≠ | Cramér, H. (1946). Mathematical Methods of Statistics. Princeton University Press. ISBN: 978-0691080420 | Fisher, R. A. (1922). On the interpretation of chi-squared from contingency tables, and the calculation of P. Journal of the Royal Statistical Society, 85(1), 87–94. DOI ↗ | Cox, D. R. (1958). The regression analysis of binary sequences. Journal of the Royal Statistical Society, Series B, 20(2), 215–242. DOI ↗ |
| 别名≠ | cramers v, cramer v, phi coefficient (r×c), Cramer's V (İlişki Kuvveti) | Fisher-Irwin test, exact test of independence, Fisher'ın Kesin Testi | logit model, binomial logistic regression, LR |
| 相关≠ | 3 | 2 | 3 |
| 摘要≠ | Cramer's V is a nonparametric effect-size statistic that measures the strength of association between two categorical variables on a scale from 0 to 1. Introduced by the Swedish mathematician Harald Cramér in his 1946 work Mathematical Methods of Statistics, it generalises the phi coefficient to tables of any size, making it the standard companion statistic to the chi-square test. | Fisher's exact test is a nonparametric exact-probability test of independence for small-sample contingency tables, introduced by R. A. Fisher in 1922. Rather than relying on a large-sample approximation, it computes the exact probability of the observed table directly from the hypergeometric distribution. | Logistic regression is a statistical method for modeling the probability of a binary outcome (disease present/absent, success/failure) as a function of continuous and categorical predictors. Developed by David Roxbee Cox (1958), it solves the problem of predicting categorical outcomes by applying a logistic transformation to constrain predictions to the [0,1] probability interval, enabling accurate risk stratification, diagnostic prediction, and causal inference in epidemiology, medicine, and social science. |
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