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| 皮尔逊卡方独立性检验× | 逻辑回归× | |
|---|---|---|
| 领域≠ | 统计学 | 研究统计学 |
| 方法族≠ | Hypothesis test | Process / pipeline |
| 起源年份≠ | 1900 | 1958 |
| 提出者≠ | Karl Pearson | David Roxbee Cox |
| 类型≠ | Nonparametric association / goodness-of-fit | Method |
| 开创性文献≠ | Pearson, K. (1900). On the criterion that a given system of deviations from the probable in the case of a correlated system of variables. Philosophical Magazine, Series 5, 50(302), 157–175. link ↗ | 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≠ | chi-squared test, χ² test, Ki-Kare Testi, chi-square test | logit model, binomial logistic regression, LR |
| 相关 | 3 | 3 |
| 摘要≠ | The chi-square test of independence is a nonparametric hypothesis test that determines whether two categorical variables are statistically associated or independent of one another. Introduced by Karl Pearson in 1900, it remains the standard procedure for analysing contingency tables and requires no assumption of normality — only that observations are independent and that expected cell frequencies are sufficiently large. | 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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