Empirical model building and response surfaces

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empirical model building and response surfaces

Empirical Model-Building and Response Surfaces by George E.P. Box

An innovative discussion of building empirical models and the fitting of surfaces to data. Introduces the general philosophy of response surface methodology, and details least squares for response surface work, factorial designs at two levels, fitting second-order models, adequacy of estimation and the use of transformation, occurrence and elucidation of ridge systems, and more. Some results are presented for the first time. Includes real-life exercises, nearly all with solutions.
File Name: empirical model building and response surfaces.zip
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Published 24.12.2018

Basics of Response Surface Methodology (RSM) for Process Optimization, Part 1

ISBN 13: 9780471810339

Scientific Research An Academic Publisher. Box, G. Experimental Strategy. In a first step, the model is fitted in a traditional way and used to extrapolate forecast of the time-varying mortality index. The observed pattern of the mortality rates shows a different variability at different ages, highlighting that the homoscedasticity hypothesis is quite unrealistic.

Empirical Model-Building and Response Surfaces (Wiley Series in Probability and Statistics)

In statistics, response surface methodology RSM explores the relationships between several explanatory variables and one or more response variables. The method was introduced by George E. Box and K. Wilson in The main idea of RSM is to use a sequence of designed experiments to obtain an optimal response. Box and Wilson suggest using a second-degree polynomial model to do this. They acknowledge that this model is only an approximation, but they use it because such a model is easy to estimate and apply, even when little is known about the process.


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