Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Z-критерий для двух долей× | Логистическая регрессия× | |
|---|---|---|
| Область≠ | Статистика | Статистика исследований |
| Семейство≠ | Hypothesis test | Process / pipeline |
| Год появления≠ | 1900 | 1958 |
| Автор метода≠ | Karl Pearson / classical large-sample z approximation | David Roxbee Cox |
| Тип≠ | Parametric proportion comparison | Method |
| Основополагающий источник≠ | Fleiss, J. L., Levin, B., & Paik, M. C. (2003). Statistical Methods for Rates and Proportions (3rd ed.). Wiley. DOI ↗ | Cox, D. R. (1958). The regression analysis of binary sequences. Journal of the Royal Statistical Society, Series B, 20(2), 215–242. DOI ↗ |
| Другие названия≠ | z-test for proportions, two-sample proportion test, one-proportion z-test, Oran Testi — z Testi (Oranlar) | logit model, binomial logistic regression, LR |
| Связанные≠ | 4 | 3 |
| Сводка≠ | The proportion test (z-test for proportions) is a parametric hypothesis test that compares one or two sample proportions against a reference value or each other. Grounded in the large-sample normal approximation formalized by Fleiss, Levin, and Paik (2003), it is the standard tool for binary outcome comparisons when samples are large enough for the central limit theorem to apply. | 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. |
| ScholarGateНабор данных ↗ |
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