השוואת שיטות

	מודל מחסום לנתוני ספירה	רגרסיה לוגיסטית	רגרסיית ריבועים פחותים רגילים (OLS)
תחום	סטטיסטיקה	סטטיסטיקה למחקר	אקונומטריקה
משפחה	Regression model	Process / pipeline	Regression model
שנת המקור	1986	1958	2019
הוגה השיטה	Mullahy	David Roxbee Cox	Wooldridge (textbook treatment); classical least squares
סוג	Two-part count model	Method	Linear regression
מקור מכונן	Mullahy, J. (1986). Specification and Testing of Some Modified Count Data Models. Journal of Econometrics, 33(3), 341–365.	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
כינויים	hurdle count model, two-part count model, zero-truncated count model, Engel Modeli (Hurdle Model)	logit model, binomial logistic regression, LR	ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu
קשורות	5	3	5
תקציר	The hurdle model is a two-part count-data model introduced by Mullahy (1986). A first stage models the binary choice of crossing a hurdle (a zero versus a non-zero count), and a second stage models the strictly positive counts with a zero-truncated distribution such as a zero-truncated Poisson or negative binomial.	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).