مقایسهٔ روش‌ها

	آزمون استقلال کای-دو پیرسون	رگرسیون لجستیک	آزمون مک‌نمار (McNemar's test)
حوزه	آمار	آمار پژوهش	آمار
خانواده	Hypothesis test	Process / pipeline	Hypothesis test
سال پیدایش	1900	1958	1947
پدیدآور	Karl Pearson	David Roxbee Cox	Quinn McNemar
نوع	Nonparametric association / goodness-of-fit	Method	Nonparametric test for paired binary data
منبع بنیادین	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.	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.
نام‌های دیگر	chi-squared test, χ² test, Ki-Kare Testi, chi-square test	logit model, binomial logistic regression, LR	McNemar chi-square test, test for correlated proportions, paired binary test, McNemar Testi
مرتبط	3	3	5
خلاصه	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.	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.