Empirical Model-Building and Response Surfaces
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.
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THE USE OF GRADUATING FUNCTIONS
Appendix 2A A Theoretical Response Function
Appendix 3A Iteratively Reweighted Least Squares
25 other sections not shown
additional analysis analysis of variance applied appropriate approximation associated blocks calculated canonical center points Chapter choice coded coefficients column compared consider contours corresponding defined direction discussed distribution effects eight equation error estimates example experiment experimental factorial factorial design Figure first-order follows four fractional function further give given illustration important indicated input interactions interest lack of fit least squares levels linear main effects matrix maximum mean measure method normal Note observations obtained original orthogonal parameters particular plot polynomial possible predictor variables produce quadratic reaction region relationship represent residuals response response surface ridge rotatable runs scale second degree second-order shown shows significant signs Source standard Statistical steepest ascent sum of squares Suppose surface Table temperature tion transformation true values vector yield zero