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	प्रॉबिट रिग्रेशन मॉडल	लॉजिस्टिक रिग्रेशन	साधारण न्यूनतम वर्ग (OLS) समाश्रयण	पैनल डेटा फिक्स्ड इफेक्ट्स मॉडल
क्षेत्र	अर्थमिति	अनुसंधान सांख्यिकी	अर्थमिति	अर्थमिति
परिवार	Regression model	Process / pipeline	Regression model	Regression model
उद्भव वर्ष	2018	1958	2019	2014
प्रवर्तक	Greene (textbook treatment); classical discrete-choice modelling	David Roxbee Cox	Wooldridge (textbook treatment); classical least squares	Hsiao (textbook treatment); within transformation of panel data
प्रकार	Binary discrete-choice model	Method	Linear regression	Panel data regression
मौलिक स्रोत	Greene, W. H. (2018). Econometric Analysis (8th ed.). Pearson. ISBN: 978-0134461366	Cox, D. R. (1958). The regression analysis of binary sequences. Journal of the Royal Statistical Society, Series B, 20(2), 215–242.	Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860	Hsiao, C. (2014). Analysis of Panel Data (3rd ed.). Cambridge University Press.
उपनाम	probit regression, normit model, Probit Modeli	logit model, binomial logistic regression, LR	ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu	fixed effects model, within estimator, panel fixed-effects regression, Panel Veri — Sabit Etkiler Modeli
संबंधित	5	3	5	5
सारांश	The probit model is a regression method for a binary (0/1) outcome that maps a linear index of the predictors through the standard normal cumulative distribution function to produce a probability. It is a classical discrete-choice alternative to logistic regression, developed in standard econometrics treatments such as Greene's Econometric Analysis (2018).	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.	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).	The Panel Data Fixed Effects model estimates relationships from panel data (the same units observed over several time periods) while controlling for unit- and/or time-specific effects, supporting causal inference. It is developed as the within estimator in standard treatments such as Hsiao's Analysis of Panel Data (2014).