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	Regresja logistyczna	Model efektów stałych dla danych panelowych	Regresja kwantylowa
Dziedzina	Statystyka w badaniach	Ekonometria	Ekonometria
Rodzina	Process / pipeline	Regression model	Regression model
Rok powstania	1958	2014	1978
Twórca	David Roxbee Cox	Hsiao (textbook treatment); within transformation of panel data	Koenker & Bassett
Typ	Method	Panel data regression	Conditional quantile regression
Źródło pierwotne	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.	Koenker, R. & Bassett, G., Jr. (1978). Regression Quantiles. Econometrica, 46(1), 33-50.
Inne nazwy	logit model, binomial logistic regression, LR	fixed effects model, within estimator, panel fixed-effects regression, Panel Veri — Sabit Etkiler Modeli	conditional quantile regression, regression quantiles, Kantil Regresyon
Pokrewne	3	5	5
Podsumowanie	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).	Quantile regression models conditional quantiles of an outcome - the median, the 25th or 75th percentile, and so on - rather than the conditional mean that OLS targets. Introduced by Koenker and Bassett in 1978, it reveals how predictors act across the whole distribution, including its tails.