مقایسهٔ روش‌ها

	رگرسیون لجستیک	رگرسیون لجستیک چندجمله‌ای (Multinomial Logistic Regression)	رگرسیون حداقل مربعات معمولی (OLS)
حوزه	آمار پژوهش	اقتصادسنجی	اقتصادسنجی
خانواده	Process / pipeline	Regression model	Regression model
سال پیدایش	1958	1974	2019
پدیدآور	David Roxbee Cox	McFadden	Wooldridge (textbook treatment); classical least squares
نوع	Method	Multinomial logistic regression	Linear regression
منبع بنیادین	Cox, D. R. (1958). The regression analysis of binary sequences. Journal of the Royal Statistical Society, Series B, 20(2), 215–242.	McFadden, D. (1974). Conditional Logit Analysis of Qualitative Choice Behavior. In P. Zarembka (Ed.), Frontiers in Econometrics (pp. 105-142). Academic Press. ISBN: 978-0127761503	Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860
نام‌های دیگر	logit model, binomial logistic regression, LR	multinomial logistic regression, polytomous logistic regression, softmax regression, Çok Kategorili Lojistik Regresyon	ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu
مرتبط	3	5	5
خلاصه	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.	Multinomial logistic regression is a maximum-likelihood method for a nominal (unordered) dependent variable with more than two categories. Building on McFadden's 1974 treatment of qualitative choice, it gives each category its own set of coefficients relative to a reference category.	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).