विधियों की तुलना करें

	साधारण न्यूनतम वर्ग (OLS) समाश्रयण	लॉजिस्टिक रिग्रेशन
क्षेत्र	अर्थमिति	अनुसंधान सांख्यिकी
परिवार	Regression model	Process / pipeline
उद्भव वर्ष	2019	1958
प्रवर्तक	Wooldridge (textbook treatment); classical least squares	David Roxbee Cox
प्रकार	Linear regression	Method
मौलिक स्रोत	Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860	Cox, D. R. (1958). The regression analysis of binary sequences. Journal of the Royal Statistical Society, Series B, 20(2), 215–242.
उपनाम	ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu	logit model, binomial logistic regression, LR
संबंधित	5	3
सारांश	Ordinary Least Squares is the classical linear regression method that explains a continuous outcome as a linear combination of predictors. It estimates the coefficients by minimising the sum of squared residuals, and under the Gauss-Markov assumptions these estimates are the best linear unbiased estimator (BLUE).	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.