Comparer des méthodes

	Régression logistique	Test de McNemar
Domaine	Statistiques de recherche	Statistique
Famille	Process / pipeline	Hypothesis test
Année d'origine	1958	1947
Auteur d'origine	David Roxbee Cox	Quinn McNemar
Type	Method	Nonparametric test for paired binary data
Source fondatrice	Cox, D. R. (1958). The regression analysis of binary sequences. Journal of the Royal Statistical Society, Series B, 20(2), 215–242.	McNemar, Q. (1947). Note on the sampling error of the difference between correlated proportions or percentages. Psychometrika, 12(2), 153–157.
Alias	logit model, binomial logistic regression, LR	McNemar chi-square test, test for correlated proportions, paired binary test, McNemar Testi
Apparentées	3	5
Résumé	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.	McNemar's test is a nonparametric hypothesis test that compares two paired (correlated) binary proportions, such as a yes/no measurement taken on the same subjects before and after an intervention. It was introduced by Quinn McNemar in 1947 and works on the 2×2 table of matched outcomes.