Сравнение методов

	Линейный дискриминантный анализ (ЛДА	Факторный анализ	Логистическая регрессия
Область	Статистика	Статистика исследований	Статистика исследований
Семейство	Hypothesis test	Process / pipeline	Process / pipeline
Год появления	1936	1931	1958
Автор метода	Ronald A. Fisher	Louis Leon Thurstone	David Roxbee Cox
Тип	Parametric linear classifier / dimensionality reduction	Method	Method
Основополагающий источник	Fisher, R.A. (1936). The Use of Multiple Measurements in Taxonomic Problems. Annals of Eugenics, 7(2), 179–188.	Thurstone, L. L. (1947). Multiple Factor Analysis. University of Chicago Press.	Cox, D. R. (1958). The regression analysis of binary sequences. Journal of the Royal Statistical Society, Series B, 20(2), 215–242.
Другие названия	LDA, Fisher's LDA, Fisher's linear discriminant, discriminant function analysis	EFA, CFA, latent variable modeling	logit model, binomial logistic regression, LR
Связанные	7	3	3
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.	Factor analysis is a statistical technique for identifying latent (unobserved) dimensions underlying observed variables, developed by Louis Leon Thurstone in the 1930s and formalized by Jöreskog (1969). Exploratory factor analysis (EFA) discovers unknown factor structure from data; confirmatory factor analysis (CFA) tests hypothesized relationships between observed and latent variables. Essential in psychometrics (test development), organizational research (measuring constructs like leadership style), and biomedicine (identifying disease subtypes), factor analysis reduces dimensionality while revealing conceptual organization in multivariate data.	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.