Linganisha mbinu

	Uchanganuzi wa Pamoja	Mfumo wa Logit Mchanganyiko
Nyanja	Muundo wa Majaribio	Ekonometriki
Familia	Hypothesis test	Regression model
Mwaka wa asili	1978	2000
Mwanzilishi	Paul E. Green & V. Srinivasan	Daniel McFadden & Kenneth Train
Aina	Decomposition-based utility estimation	Random-parameters discrete choice model
Chanzo asilia	Green, P.E. & Srinivasan, V. (1978). Conjoint analysis in consumer research: Issues and outlook. Journal of Consumer Research, 5(2), 103–123.	Train, K. E. (2009). Discrete Choice Methods with Simulation (2nd ed.). Cambridge University Press. ISBN: 978-0-521-74738-7
Majina mbadala	CBC conjoint, choice-based conjoint, adaptive conjoint analysis, full-profile conjoint	Random Parameters Logit, Mixed Multinomial Logit, Error Components Logit, Karma Logit Modeli
Zinazohusiana	6	3
Muhtasari	Conjoint analysis is a preference-measurement technique that decomposes overall product evaluations into the separate utility values — called part-worths — that respondents assign to each attribute level. Formalised by Green and Srinivasan in their seminal 1978 Journal of Consumer Research paper, the method has become the dominant tool in marketing research and product design for quantifying what buyers truly trade off when they choose between options.	The Mixed Logit model, introduced formally by McFadden and Train (2000) and elaborated in Train (2009), is a flexible discrete choice framework that allows preference parameters to vary randomly across decision-makers. By integrating standard logit probabilities over a mixing distribution of coefficients, it overcomes the restrictive independence of irrelevant alternatives (IIA) property and accommodates unobserved taste heterogeneity, panel data correlation, and complex substitution patterns across alternatives.