مقایسهٔ روش‌ها

	رگرسیون حداقل مربعات معمولی (OLS)	رگرسیون لجستیک	مدل اثرات ثابت داده‌های پانل
حوزه	اقتصادسنجی	آمار پژوهش	اقتصادسنجی
خانواده	Regression model	Process / pipeline	Regression model
سال پیدایش	2019	1958	2014
پدیدآور	Wooldridge (textbook treatment); classical least squares	David Roxbee Cox	Hsiao (textbook treatment); within transformation of panel data
نوع	Linear regression	Method	Panel data regression
منبع بنیادین	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.	Hsiao, C. (2014). Analysis of Panel Data (3rd ed.). Cambridge University Press.
نام‌های دیگر	ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu	logit model, binomial logistic regression, LR	fixed effects model, within estimator, panel fixed-effects regression, Panel Veri — Sabit Etkiler Modeli
مرتبط	5	3	5
خلاصه	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.	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).