Sammenlign metoder

	Optimal eksperimentelt design (D-optimal, I-optimal)	Responsflademetodologi (RSM)
Fagområde	Forsøgsdesign	Forsøgsdesign
Familie	Hypothesis test	Hypothesis test
Oprindelsesår	1972	1951
Ophavsperson	V. V. Fedorov	George E. P. Box & K. B. Wilson
Type	Computer-aided optimal design	Second-order polynomial response surface model
Oprindelig kilde	Fedorov, V.V. (1972). Theory of Optimal Experiments. Academic Press.	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.
Aliasser	D-Optimal Design, I-Optimal Design, Computer-Generated Design, Optimal Deneme Deseni (D-Optimal, I-Optimal)	RSM, Central Composite Design, Box-Behnken Design, CCD
Relaterede	5	7
Resumé	Optimal experimental design is a computer-aided approach to constructing experiments that maximises statistical efficiency for a given model and run budget. Formalised by V. V. Fedorov in 1972, it selects experimental points from a candidate set so that the information matrix M = X'X is optimised according to a chosen criterion — most commonly D-optimality (maximising the determinant) or I-optimality (minimising average prediction variance). It is the preferred strategy whenever classical designs such as central composite or Box-Behnken cannot be applied because the experimental region is constrained or factor ranges are irregular.	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.