 Response surface methodology

Response surface methodology fits local regression approximations to a quantitative response as experimental settings change. A first-order model estimates a local gradient; augmenting the design permits quadratic regression, curvature assessment and local optimization. Extrapolating a fitted surface outside the experimentally supported region requires caution.

Response Surface Methodology (RSM) is a statistical and mathematical technique used for modeling and analyzing problems where several variables influence a response or outcome of interest. The primary objective of RSM is to optimize this response, which can involve either maximizing or minimizing it, depending on the context of the study. ### Key Features of RSM: 1. **Design of Experiments (DOE)**: RSM employs a systematic approach to experimental design, allowing researchers to study the effects of multiple factors simultaneously.