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overviewf2011 - Statistics 514 Overview and Fundamental...

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Statistics 514: Overview and Fundamental Principles Lecture 1. Overview and Basic Principles Montgomery: Chapter 1 Page 1
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Statistics 514: Overview and Fundamental Principles Statistical Methods for Design and Analysis of Experiments 1. Experimental error. 2. Confusion of correlation with causation. 3. Complexity of the effects in study. Some Terminologies: Experimental factor (or variable) Factor level Treatment (setting or level combination) Unit Experimental run (trial) Experimental error Page 3
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Statistics 514: Overview and Fundamental Principles Machine Tool Life Experiment An engineer is interested in the effects of cutting speed (A), tool geometry (B) and cutting angle (C) on the lifespan (in hours) of a machine tool. Two levels of each factor are chosen and three replicates of a 2 3 factorial design are run. The results follow. Factor Replicate A B C I II III - - - 22 31 25 + - - 32 43 29 - + - 35 34 50 + + - 55 47 46 - - + 44 45 38 + - + 40 37 36 - + + 60 50 54 + + + 39 41 47 Page 4
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Statistics 514: Overview and Fundamental Principles A Brief History of Experimental Design 1. Agricultural Era: -Treatment Comparisons and ANOVA -R.A. Fisher, Rothamsted Agricultural Experimental Station (1930, England) -Introduced statistical experimental design and data analysis -Summarized the fundamental principles: replication, randomization, and blocking. -An influential book, The Design of Experiments Combinatorial Design Theory: R. C. Bose Page 5
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Statistics 514: Overview and Fundamental Principles 2. Industrial Era: -Process modeling and optimization -Box and coworkers in chemical industries and other processing industries -Empirical modeling, response surface methodologies, central composite design Optimal designs: J. Kieffer 3. Quality Era: -Quality improvement and variation reduction -Taguchi and robust parameter design -Statistical design and analysis toward robustness. Page 6
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Statistics 514: Overview and Fundamental Principles 4. Current State of Experimental Design: -Popular outside statistics, and an indispensable tool in many scientific/engineering endeavors -New challenges: –Large and complex experiments, e.g., screening design in pharmaceutical industry, experimental design in biotechnology –Computer experiments: efficient ways to model complex systems based on computer simulation.
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