เปรียบเทียบวิธี

	การถดถอยโลจิสติก	การวิเคราะห์ความแปรปรวนร่วมหลายตัวแปร (MANCOVA)
สาขาวิชา	สถิติการวิจัย	สถิติศาสตร์
ตระกูล	Process / pipeline	Hypothesis test
ปีกำเนิด	1958	1970
ผู้ริเริ่ม	David Roxbee Cox	Extension of MANOVA and ANCOVA traditions; consolidated in multivariate textbooks by the 1970s–1980s
ประเภท	Method	Parametric multivariate mean comparison with covariate control
แหล่งต้นตำรับ	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
ชื่อเรียกอื่น	logit model, binomial logistic regression, LR	MANCOVA, multivariate ANCOVA, MANOVA with covariates, MANCOVA — Çok Değişkenli Kovaryans Analizi
ที่เกี่ยวข้อง	3	5
สรุป	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).