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Introduction to Response-Surface Methodology

In: Experimental Design

Author

Listed:
  • Paul D. Berger

    (Bentley University)

  • Robert E. Maurer

    (Boston University, Questrom School of Business)

  • Giovana B. Celli

    (Cornell University)

Abstract

Until now, we have considered how a dependent variable, yield, or response depends on specific levels of independent variables or factors. The factors could be categorical or numerical; however, we did note that they often differ in how the sum of squares for the factor is more usefully partitioned into orthogonal components. For example, a numerical factor might be broken down into orthogonal polynomials (introduced in Chap. 12 ). For categorical factors, methods introduced in Chap. 5 are typically employed. In the past two chapters, we have considered linear relationships and fitting optimal straight lines to the data, usually for situations in which the data values are not derived from designed experiments. Now, we consider experimental design techniques that find the optimal combination of factor levels for situations in which the feasible levels of each factor are continuous. (Throughout the text, the dependent variable, Y, has been assumed to be continuous.) The techniques are called response-surface methods or response-surface methodology (RSM).

Suggested Citation

  • Paul D. Berger & Robert E. Maurer & Giovana B. Celli, 2018. "Introduction to Response-Surface Methodology," Springer Books, in: Experimental Design, edition 2, chapter 0, pages 533-584, Springer.
    • Handle: RePEc:spr:sprchp:978-3-319-64583-4_16
    DOI: 10.1007/978-3-319-64583-4_16
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