Empirical Model-Building and Response SurfacesAn 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. |
Contents
THE USE OF GRADUATING FUNCTIONS | 20 |
Appendix 2A A Theoretical Response Function | 32 |
9 | 40 |
Copyright | |
30 other sections not shown
Common terms and phrases
analysis of variance appropriate approximation B₁ B₂ canonical analysis canonical form catalyst center points central composite design Chapter coded coefficients column composite design consider contours cube df MS F direction of steepest distribution example factorial design first-order design fitted equation fractional factorial fractional factorial design input variables lack of fit least squares levels linear main effects matrix maximum mean square normal observations obtained orthogonally blocked parameters plot possible predictor variables Pure error quadratic reaction region of interest regression relationship replicated residuals response function response surface response surface methodology ridge rotatable runs second-order model shown in Table Source SS df standard errors stationary stationary point Statistical steepest ascent sum of squares Suppose temperature third-order tion transformation two-factor interactions values variance table vector x₁ x₁ and x2 yield z₁ zero β₁ βο σ²