Linganisha mbinu

	Uchanganuzi wa Pamoja	Ubunifu wa Kijamii wa Nusu (2^(k-p) Fractional Factorial Design)
Nyanja	Muundo wa Majaribio	Muundo wa Majaribio
Familia	Hypothesis test	Hypothesis test
Mwaka wa asili	1978	1961
Mwanzilishi	Paul E. Green & V. Srinivasan	George E. P. Box and J. Stuart Hunter
Aina	Decomposition-based utility estimation	Screening and economical factorial design
Chanzo asilia	Green, P.E. & Srinivasan, V. (1978). Conjoint analysis in consumer research: Issues and outlook. Journal of Consumer Research, 5(2), 103–123.	Box, G.E.P. & Hunter, J.S. (1961). The 2^(k-p) Fractional Factorial Designs. Technometrics, 3(3), 311–351.
Majina mbadala	CBC conjoint, choice-based conjoint, adaptive conjoint analysis, full-profile conjoint	2^k-p design, fractional factorial, screening design, Kesirli Faktöriyel Desen (2^k-p Fractional Factorial)
Zinazohusiana	6	7
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 fractional factorial design is an economical experimental strategy that investigates k factors by running only a carefully chosen 1/2^p fraction of the full 2^k factorial experiment. Formalized by George E. P. Box and J. Stuart Hunter in their landmark 1961 Technometrics paper, it exploits the sparsity-of-effects principle — that high-order interactions are typically negligible — to screen many factors with far fewer runs than a complete factorial would require.