השוואת שיטות

	אבחון השפעה (מרחק קוק, DFFITS, מינוף)	ניתוח רכיבים עיקריים
תחום	סטטיסטיקה	למידת מכונה
משפחה	Regression model	Machine learning
שנת המקור	1977	2002
הוגה השיטה	R. Dennis Cook (Cook's distance); Belsley, Kuh & Welsch (DFFITS, leverage)	Jolliffe, I.T. (textbook); Pearson & Hotelling (origins)
סוג	Regression diagnostic	Unsupervised dimensionality reduction
מקור מכונן	Cook, R. D. (1977). Detection of Influential Observations in Linear Regression. Technometrics, 19(1), 15-18.	Jolliffe, I.T. (2002). Principal Component Analysis (2nd ed.). Springer.
כינויים	Cook's distance, DFFITS, leverage, influential observation detection	Temel Bileşenler Analizi (PCA), PCA, principal components analysis, Karhunen-Loève transform
קשורות	5	3
תקציר	Influence diagnostics are a family of post-fit measures that quantify how much each single observation affects a fitted regression. Cook's distance was introduced by R. Dennis Cook in 1977, with leverage and DFFITS formalised by Belsley, Kuh and Welsch in 1980, to flag the observations that most strongly pull the estimated coefficients.	Principal Component Analysis (PCA) is an unsupervised dimensionality-reduction method — given its modern textbook treatment by Ian Jolliffe (2002) — that compresses high-dimensional data into fewer dimensions while preserving the maximum possible variance. It re-expresses correlated variables as a small set of uncorrelated principal components ordered by how much of the data's variation each one captures.