השוואת שיטות

	רגרסיה לוגיסטית	רגרסיית בינום שלילי	רגרסיית ריבועים פחותים רגילים (OLS)
תחום	סטטיסטיקה למחקר	אקונומטריקה	אקונומטריקה
משפחה	Process / pipeline	Regression model	Regression model
שנת המקור	1958	2011	2019
הוגה השיטה	David Roxbee Cox	Hilbe (textbook treatment); generalized linear model framework	Wooldridge (textbook treatment); classical least squares
סוג	Method	Generalized linear model for count data	Linear regression
מקור מכונן	Cox, D. R. (1958). The regression analysis of binary sequences. Journal of the Royal Statistical Society, Series B, 20(2), 215–242.	Hilbe, J. M. (2011). Negative Binomial Regression (2nd ed.). Cambridge University Press.	Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860
כינויים	logit model, binomial logistic regression, LR	NB regression, NB2 regression, negatif binom regresyonu	ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu
קשורות	3	4	5
תקציר	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.	Negative Binomial Regression is a generalized linear model for count outcomes that extends Poisson regression to handle overdispersion, where the variance of the counts exceeds their mean. Developed in the GLM tradition and treated in depth by Hilbe (2011), it adds a dispersion parameter so that inference stays valid when Poisson would understate the spread of the data.	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).