Сравнение методов

	Линейный дискриминантный анализ (ЛДА	Логистическая регрессия	Многомерный дисперсионный анализ (MANOVA)
Область	Статистика	Статистика исследований	Статистика
Семейство	Hypothesis test	Process / pipeline	Hypothesis test
Год появления	1936	1958	1932
Автор метода	Ronald A. Fisher	David Roxbee Cox	Samuel Stanley Wilks (Wilks' Lambda, 1932); Roy, Hotelling, Pillai (mid-20th c.)
Тип	Parametric linear classifier / dimensionality reduction	Method	Parametric multivariate mean comparison
Основополагающий источник	Fisher, R.A. (1936). The Use of Multiple Measurements in Taxonomic Problems. Annals of Eugenics, 7(2), 179–188.	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. (2013). Using Multivariate Statistics (6th ed.). Pearson. ISBN: 978-0205849574
Другие названия	LDA, Fisher's LDA, Fisher's linear discriminant, discriminant function analysis	logit model, binomial logistic regression, LR	Multivariate ANOVA, Çok Değişkenli ANOVA (MANOVA)
Связанные	7	3	5
Сводка	Linear Discriminant Analysis (LDA) is a parametric supervised classification method that finds the linear combination of continuous predictors that best separates two or more predefined groups. Introduced by Ronald A. Fisher in his landmark 1936 paper on taxonomic measurements, it simultaneously serves as a classifier and a dimensionality-reduction tool, and can be understood as the classification-oriented counterpart of MANOVA.	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.	MANOVA is a parametric hypothesis test that simultaneously compares group means across multiple continuous dependent variables, controlling the inflation of Type I error that would result from running separate ANOVAs. Key multivariate test statistics — Wilks' Lambda, Pillai's Trace, Hotelling-Lawley Trace, and Roy's Greatest Root — were developed between the 1930s and 1950s, with Wilks' Lambda formalised by Samuel Stanley Wilks in 1932.