השוואת שיטות

	ניתוח דיסקרימיננטי ליניארי (LDA	ניתוח גורמים	ניתוח שונות רב-משתני (MANOVA)
תחום	סטטיסטיקה	סטטיסטיקה למחקר	סטטיסטיקה
משפחה	Hypothesis test	Process / pipeline	Hypothesis test
שנת המקור	1936	1931	1932
הוגה השיטה	Ronald A. Fisher	Louis Leon Thurstone	Samuel Stanley Wilks (Wilks' Lambda, 1932); Roy, Hotelling, Pillai (mid-20th c.)
סוג	Parametric linear classifier / dimensionality reduction	Method	Parametric multivariate mean comparison
מקור מכונן	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.	Tabachnick, B.G. & Fidell, L.S. (2013). Using Multivariate Statistics (6th ed.). Pearson. ISBN: 978-0205849574
כינויים	LDA, Fisher's LDA, Fisher's linear discriminant, discriminant function analysis	EFA, CFA, latent variable modeling	Multivariate ANOVA, Çok Değişkenli ANOVA (MANOVA)
קשורות	7	3	5
תקציר	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.	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.