Сравнение на методи
Прегледайте избраните методи един до друг; редовете с разлики са откроени.
| V на Крамер× | Хи-квадрат тест за независимост× | Логистична регресия× | |
|---|---|---|---|
| Област≠ | Статистика | Статистика | Статистика за изследвания |
| Семейство≠ | Hypothesis test | Hypothesis test | Process / pipeline |
| Година на възникване≠ | 1946 | 1900 | 1958 |
| Създател≠ | Harald Cramér | Karl Pearson | David Roxbee Cox |
| Тип≠ | Nonparametric association measure | Nonparametric test of association | Method |
| Основополагащ източник≠ | Cramér, H. (1946). Mathematical Methods of Statistics. Princeton University Press. ISBN: 978-0691080420 | Pearson, K. (1900). On the criterion that a given system of deviations from the probable in the case of a correlated system of variables is such that it can be reasonably supposed to have arisen from random sampling. Philosophical Magazine, 50(302), 157–175. DOI ↗ | Cox, D. R. (1958). The regression analysis of binary sequences. Journal of the Royal Statistical Society, Series B, 20(2), 215–242. DOI ↗ |
| Други названия≠ | cramers v, cramer v, phi coefficient (r×c), Cramer's V (İlişki Kuvveti) | chi-squared test, Pearson's chi-square test, test of independence, ki-kare bağımsızlık testi | logit model, binomial logistic regression, LR |
| Свързани≠ | 3 | 2 | 3 |
| Резюме≠ | 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. | The chi-square test of independence is a nonparametric hypothesis test that examines whether two categorical variables are associated by comparing observed and expected frequencies in a cross-tabulation. It rests on the chi-square criterion introduced by Karl Pearson in 1900. | 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 от данни ↗ |
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