Linganisha mbinu

	Bayesian ANCOVA	Uchanganuzi wa Usawazishaji (ANCOVA)
Nyanja	Takwimu	Takwimu
Familia	Hypothesis test	Hypothesis test
Mwaka wa asili	2012 (formalized; Bayesian general linear models since 1960s)	1932
Mwanzilishi	Building on Jeffreys (1961) and developed formally for regression/ANCOVA by Rouder & Morey (2012)	Ronald A. Fisher
Aina	Bayesian parametric covariate-adjusted group comparison	Parametric group comparison with covariate control
Chanzo asilia	Rouder, J. N., & Morey, R. D. (2012). Default Bayes factors for model selection in regression. Multivariate Behavioral Research, 47(6), 877–903.	Tabachnick, B.G. & Fidell, L.S. (2013). Using Multivariate Statistics (6th ed.). Pearson. ISBN: 978-0205849574
Majina mbadala	Bayesian ANCOVA, Bayesian analysis of covariance, B-ANCOVA, Bayesian covariate-adjusted group comparison	analysis of covariance, covariance analysis, ANCOVA (Kovaryans Analizi)
Zinazohusiana	5	4
Muhtasari	Bayesian Analysis of Covariance (Bayesian ANCOVA) extends classical ANCOVA by placing prior distributions on group effects and covariate slopes, then updating them with observed data to obtain posterior distributions and Bayes factors. It quantifies evidence for group differences on a continuous outcome after statistically adjusting for one or more continuous covariates, without relying on p-value thresholds.	ANCOVA is a parametric hypothesis test that compares the adjusted means of two or more independent groups while statistically controlling for one or more continuous covariates. By removing the portion of outcome variance explained by the covariate, ANCOVA increases statistical precision and produces fairer group comparisons. The method builds on the general linear model framework consolidated by Fisher in the early 1930s and is described comprehensively by Tabachnick and Fidell (2013).