השוואת שיטות

	רגרסיה לוגיסטית אורדינלית	רגרסיה לוגיסטית
תחום	סטטיסטיקה	סטטיסטיקה למחקר
משפחה	Regression model	Process / pipeline
שנת המקור	1980	1958
הוגה השיטה	Peter McCullagh	David Roxbee Cox
סוג	Ordinal regression / GLM	Method
מקור מכונן	McCullagh, P. (1980). Regression models for ordinal data. Journal of the Royal Statistical Society: Series B (Methodological), 42(2), 109–142.	Cox, D. R. (1958). The regression analysis of binary sequences. Journal of the Royal Statistical Society, Series B, 20(2), 215–242.
כינויים	proportional-odds model, cumulative link model, ordered logit, OLR	logit model, binomial logistic regression, LR
קשורות	6	3
תקציר	Ordinal logistic regression — most commonly the proportional-odds model — estimates the relationship between one or more predictors and an ordered categorical outcome (e.g., Likert scales, disease severity grades, educational attainment levels). It models cumulative log-odds across the ordered categories while assuming a single shared effect of each predictor at all thresholds.	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.