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| Pearson의 독립성 카이제곱 검정× | 로지스틱 회귀× | |
|---|---|---|
| 분야≠ | 통계학 | 연구 통계 |
| 계열≠ | Hypothesis test | Process / pipeline |
| 기원 연도≠ | 1900 | 1958 |
| 창시자≠ | Karl Pearson | David Roxbee Cox |
| 유형≠ | Nonparametric association / goodness-of-fit | Method |
| 원전≠ | 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 ↗ |
| 별칭≠ | chi-squared test, χ² test, Ki-Kare Testi, chi-square test | logit model, binomial logistic regression, LR |
| 관련 | 3 | 3 |
| 요약≠ | 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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