विधियों की तुलना करें

	Chi-square goodness-of-fit test	लॉजिस्टिक रिग्रेशन
क्षेत्र	सांख्यिकी	अनुसंधान सांख्यिकी
परिवार	Hypothesis test	Process / pipeline
उद्भव वर्ष	1900	1958
प्रवर्तक	Karl Pearson	David Roxbee Cox
प्रकार	Nonparametric association / goodness-of-fit	Method
मौलिक स्रोत	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.
उपनाम	chi-squared test, χ² test, Ki-Kare Testi, chi-square test	logit model, binomial logistic regression, LR
संबंधित	3	3
सारांश	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.