Although widely used in science and technology for experimental data generating, modeling, and optimization, the response surface methodology (RSM) has many limitations. Showing how robust response surface methodology (RRSM) can overcome these limitations, Robust Response Surfaces, Regression, and Positive Data Analyses presents RRS designs, along with the relevant regression and positive data analysis techniques. It explains how to use RRSM in experimental designs and regression analysis.
The book addresses problems of RRS designs, such as rotatability, slope-rotatability, weak rotatability, and optimality. It describes methods for estimating model parameters as well as positive data analysis techniques. The author illustrates the concepts and methods with real examples of lifetime responses, resistivity, replicated measures, and more.
The range of topics and applications gives the book broad appeal both to theoreticians and practicing professionals. The book helps quality engineers, scientists in any area, medical practitioners, demographers, economists, and statisticians understand the theory and applications of RRSM. It can also be used in a second course on the design of experiments.
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There are many gaps in the existing literature of robust response surface designs (RSM) that need to be filled to solve real-life problems. This book presents the gaps and their solutions. The author proposes new concepts of RSM, along with the relevant regression analysis and positive data analysis techniques, and aims to remove many RSM limitations. Among a wide range of topics, the text covers applications of correlated regression analysis techniques in block design analysis, robust first and second order rotatable and optimal designs for normally distributed responses, as well as lifetime responses having log-normal, exponential, gamma and Weibull distributions.
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