Porównaj metody

	Wielowymiarowa regresja liniowa z wieloma zmiennymi	Regresja logistyczna	Wielowymiarowa analiza kowariancji (MANCOVA)
Dziedzina	Statystyka	Statystyka w badaniach	Statystyka
Rodzina	Regression model	Process / pipeline	Hypothesis test
Rok powstania	2007	1958	1970
Twórca	Johnson & Wichern (textbook treatment); classical multivariate least squares	David Roxbee Cox	Extension of MANOVA and ANCOVA traditions; consolidated in multivariate textbooks by the 1970s–1980s
Typ	Multivariate linear regression	Method	Parametric multivariate mean comparison with covariate control
Źródło pierwotne	Johnson, R. A. & Wichern, D. W. (2007). Applied Multivariate Statistical Analysis (6th ed.). Pearson. ISBN: 978-0131877153	Cox, D. R. (1958). The regression analysis of binary sequences. Journal of the Royal Statistical Society, Series B, 20(2), 215–242.	Tabachnick, B. G. & Fidell, L. S. (2019). Using Multivariate Statistics (7th ed.). Pearson. ISBN: 978-0134790541
Inne nazwy	multivariate multiple regression, MLR with multiple dependent variables, multiple-outcome regression, Çok Değişkenli Regresyon (MLR — Çoklu DV)	logit model, binomial logistic regression, LR	MANCOVA, multivariate ANCOVA, MANOVA with covariates, MANCOVA — Çok Değişkenli Kovaryans Analizi
Pokrewne	5	3	5
Podsumowanie	Multivariate regression is a linear regression method that predicts several continuous dependent variables at the same time from a shared set of predictors. As developed in standard treatments such as Johnson and Wichern's Applied Multivariate Statistical Analysis (2007), each response equation can be fitted by ordinary least squares while the covariance structure of the residuals is used for joint testing across outcomes.	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.	MANCOVA (Multivariate Analysis of Covariance) is a parametric hypothesis test that simultaneously compares two or more groups on multiple continuous dependent variables while statistically controlling for one or more covariates. It extends MANOVA by incorporating covariate adjustment, a tradition consolidated in multivariate statistical methodology by the 1970s and authoritatively documented by Tabachnick and Fidell (2019).