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1

Whitcomb, Pat. Response surface methods for process optimization: Ppt Version 27.04. Minneapolis, MN: Stat-Ease, Inc., 2006.

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2

Institute of Materials, Minerals, and Mining., ed. Optimisation of manufacturing processes: A response surface approach. London: Maney for the Institute of Materials, Minerals and Mining, 2003.

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3

N, Boyadjieva Lidia, ed. Quality improvement with design of experiments: A response surface approach. Dordrecht: Kluwer Academic Publishers, 2001.

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4

C, Montgomery Douglas, ed. Response surface methodology: Process and product optimization using designed experiments. New York: Wiley, 1995.

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5

Myers, Raymond H. Response surface methodology: Process and product optimization using designed experiments. 3rd ed. Hoboken, N.J: Wiley, 2008.

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6

C, Montgomery Douglas, ed. Response surface methodology: Process and product optimization using designed experiments. 2nd ed. New York: J. Wiley, 2002.

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7

1941-, Cornell John A., ed. Response surfaces: Designs and analyses. 2nd ed. New York: Marcel Dekker, 1996.

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8

1941-, Cornell John A., ed. Response surfaces: Designs and analyses. New York: M. Dekker, 1987.

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9

Box, George E. P. Empirical model-building and response surfaces. New York: Wiley, 1987.

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10

Robust response surfaces, regression, and positive data analyses. Boca Raton: CRC Press, 2014.

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11

United States. National Aeronautics and Space Administration. Scientific and Technical Information Program., ed. Effect of design selection on response surface performance. [Washington, D.C.]: National Aeronautics and Space Administration, Office of Management, Scientific and Technical Information Program, 1993.

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12

United States. National Aeronautics and Space Administration. Scientific and Technical Information Program., ed. Effect of design selection on response surface performance. [Washington, D.C.]: National Aeronautics and Space Administration, Office of Management, Scientific and Technical Information Program, 1993.

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13

Vuchkov, I. N., and N. L. Boyadjieva. Quality Improvement with Design of Experiments: A Response Surface Approach. Springer, 2014.

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14

L, Walsh Joanne, Young Katherine C, and United States. National Aeronautics and Space Administration., eds. Application of response surface techniques to helicopter rotor blade optimization procedure. [Washington, DC]: National Aeronautics and Space Administration, 1995.

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15

Application of response surface techniques to helicopter rotor blade optimization procedure. [Washington, DC]: National Aeronautics and Space Administration, 1995.

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16

Sudasna-na-Ayudthya, Prapaisri. Comparison of response surface model and Taguchi methodology for robust design. 1992.

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17

RSM Simplified: Optimizing Processes Using Response Surface Methods for Design of Experiments. Productivity Press, 2005.

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18

Montgomery, Douglas C., Raymond H. Myers, and Christine M. Anderson-Cook. Response Surface Methodology: Process and Product Optimization Using Designed Experiments. Wiley & Sons, Incorporated, John, 2016.

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19

Montgomery, Douglas C., Raymond H. Myers, and Christine M. Anderson-Cook. Response Surface Methodology: Process and Product Optimization Using Designed Experiments. Wiley & Sons, Incorporated, John, 2014.

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20

Montgomery, Douglas C., Raymond H. Myers, and Christine M. Anderson-Cook. Response Surface Methodology: Process and Product Optimization Using Designed Experiments. Wiley & Sons, Incorporated, John, 2011.

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21

Montgomery, Douglas C., Raymond H. Myers, and Christine M. Anderson-Cook. Response Surface Methodology: Process and Product Optimization Using Designed Experiments. Wiley & Sons, Incorporated, John, 2016.

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22

Montgomery, Douglas C., Raymond H. Myers, and Christine M. Anderson-Cook. Response Surface Methodology: Process and Product Optimization Using Designed Experiments. Wiley & Sons, Incorporated, John, 2016.

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23

Comparison of response surface and kriging models in the multidisciplinary design of an aerospike nozzle. Hampton, VA: Institute for Computer Applications in Science and Engineering, NASA Langley Research Center, 1998.

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24

United States. National Aeronautics and Space Administration., ed. Effect of design selection on response surface performance: Final report, grant number NAG-1-1378. Tampa, Fla: College of Engineering, University of South Florida, 1993.

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25

United States. National Aeronautics and Space Administration., ed. Effect of design selection on response surface performance: Final report, grant number NAG-1-1378. Tampa, Fla: College of Engineering, University of South Florida, 1993.

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26

Vuchkov, I. N., and N. L. Boyadjieva. Quality Improvement with Design of Experiments: A Response Surface Approach (Topics in Safety, Risk, Reliability and Quality). Springer, 2001.

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27

Vuchkov, I. N., and N. L. Boyadjieva. Quality Improvement with Design of Experiments: A Response Surface Approach (Topics in Safety, Risk, Reliability and Quality). Springer, 2001.

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28

Draper, Norman R., and George E. P. Box. Response Surfaces, Mixtures, and Ridge Analyses. Wiley & Sons, Incorporated, John, 2007.

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29

Sensitivity Analysis: Gauging the Worth of Scientific Models. Chichester, UK: John WIley & Sons, Ltd., 2000.

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30

Pérez, Efrén O. The Language-Opinion Connection. Edited by Lonna Rae Atkeson and R. Michael Alvarez. Oxford University Press, 2015. http://dx.doi.org/10.1093/oxfordhb/9780190213299.013.18.

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This chapter critically reflects on the deceptively simple relationship between language and survey response: the language-opinion connection. It discusses what survey researchers actually know about this link, paying close attention to challenges involving conceptualization, measurement, and research design. Throughout, the discussion emphasizes a core theme: despite great advancements in sampling, measurement, and research design, the study of language and survey response is bereft of strong theory. Thus, while the language-opinion connection seems on the surface easy to assess, public opinion researchers have modest theory to explain how, when, and among whom language influences survey response. Against this backdrop, the chapter outlines several ways forward, stressing in particular the importance of identifying and testing psychological mechanisms.
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31

Venter, Gerhard. Non-dimensional response surfaces for structural optimization with uncertainty. 1998.

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32

Piepel, Gregory Frank. Models and designs for generalizations of mixture experiments where the response depends on the total amount. 1985.

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33

Box, George E. P. Response Surfaces, Mixtures, and Ridge Analyses (Wiley Series in Probability and Statistics). Wiley-Interscience, 2007.

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34

Creighton, Lee, and Jacques Goupy. Introduction to Design of Experiments with JMP Examples, Third Edition. 3rd ed. SAS Press, 2007.

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