Linganisha mbinu

	Muundo Imara wa Factorial Fraktionali	Mbinu ya uso wa mwitikio (RSM)
Nyanja	Muundo wa Majaribio	Muundo wa Majaribio
Familia	Process / pipeline	Hypothesis test
Mwaka wa asili	1980s (Taguchi's crossed-array approach); fractional factorial roots 1935–1945	1951
Mwanzilishi	Genichi Taguchi (robust parameter design); fractional factorial foundations by Ronald Fisher and Frank Yates	George E. P. Box & K. B. Wilson
Aina	Experimental design / robust parameter design	Second-order polynomial response surface model
Chanzo asilia	Montgomery, D. C. (2017). Design and Analysis of Experiments (9th ed.). Wiley. ISBN: 978-1119492443	Box, G. E. P. & Wilson, K. B. (1951). On the experimental attainment of optimum conditions. Journal of the Royal Statistical Society, Series B, 13(1), 1–45.
Majina mbadala	robust FFD, robust fractional factorial experiment, crossed-array fractional factorial, Taguchi-style fractional factorial	RSM, Central Composite Design, Box-Behnken Design, CCD
Zinazohusiana	2	7
Muhtasari	Robust fractional factorial design combines the run-count efficiency of fractional factorial arrays with Taguchi's robust parameter design philosophy. By simultaneously manipulating control factors (inner array) and noise factors (outer array) — each structured as a fractional factorial — the method identifies factor settings that minimize product or process variation due to uncontrollable conditions, without requiring a full factorial experiment.	Response Surface Methodology is a collection of statistical and mathematical techniques for building an empirical second-order polynomial model that relates a continuous response variable to two or more controllable input factors, and then locating the factor settings that optimize that response. The approach was introduced by George E. P. Box and K. B. Wilson in their landmark 1951 paper and has since become a cornerstone of process optimization across engineering, chemistry, food science, and pharmaceutics.