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Modern design of experiments methods for screening and experimentations with mixture and qualitative variables

Chantarat, Navara
http://rave.ohiolink.edu/etdc/view?acc_num=osu1064198056

Abstract Details

2003, Doctor of Philosophy, Ohio State University, Industrial and Systems Engineering.
This dissertation re-examines some of the most basic design of experiment methods with respect to their ability to achieve intuitive objectives. For example, it provides probably the first comprehensive evaluation of the ability of standard screening approaches to correctly tell which factors have important effects on average outputs. Also, the dissertation examines the prediction errors that users of so-called mixture experimental design and qualitative response surface methods can achieve. In practical situations, the derived "decision support" information can tell the user in advance whether the number of runs used is adequate for the experimenter's needs and provide a basis for selecting one method over another when alternatives are presented. Also, the dissertation clarifies, perhaps for the first time, the potentially serious prediction error issues associated with the methods that have been proposed for response surface investigation when some factors are qualitative. In addition to developing comprehensive computational studies of existing methods, new methods are proposed with potentially important advantages. For example, the dissertation provides some of the first unbalanced screening experimental plans relevant to cases in which some combinations of settings have far higher costs than other combinations. For situations in which some factors are mixture components, e.g., %CO2, %Ar, %N, and other factors are process variables, the dissertation provides some of the first economically relevant experimental plans offering potentially substantial reductions in prediction errors. Also, the dissertation provides the first truly advisable experimental designs for many response surface cases in which some variables are qualitative. All new methods are derived from optimization formulations or "improvement systems design problems". In each case, the intent is to design the method using the objective or objectives that most directly describe the purpose of the improvement system. Also, the formulations build on the most realistic, concise assumption schemes in the applied statistics literature.
Theodore Allen (Advisor)
119 p.
Design of Experiments; DOE; Fractional Factorial Design; Mixture Design; Response Surface Method; Response Surface Design; Qualitative Factor; Categorical Factor; Qualitative Variable; Categorical Variable

Recommended Citations

Citations

  • Chantarat, N. (2003). Modern design of experiments methods for screening and experimentations with mixture and qualitative variables [Doctoral dissertation, Ohio State University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=osu1064198056

    APA Style (7th edition)

  • Chantarat, Navara. Modern design of experiments methods for screening and experimentations with mixture and qualitative variables. 2003. Ohio State University, Doctoral dissertation. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=osu1064198056.

    MLA Style (8th edition)

  • Chantarat, Navara. "Modern design of experiments methods for screening and experimentations with mixture and qualitative variables." Doctoral dissertation, Ohio State University, 2003. http://rave.ohiolink.edu/etdc/view?acc_num=osu1064198056

    Chicago Manual of Style (17th edition)

osu1064198056
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© 2003, all rights reserved.
This open access ETD is published by The Ohio State University and OhioLINK.