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UNIT1 - Notes for Stat/ME 424 Design and Analysis of...

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Notes for Stat/ME 424 Design and Analysis of Experiments, Spring 2008 Instructor : Peter Qian E-mail : [email protected] Department of Statistics University of Wisconsin-Madison Text book : Experiments : Planning, Analysis, and Parameter Design Optimization (by Wu and Hamada; Wiley, 2000) 1
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Unit 1 : Introduction to DAE and Basic Regression Analysis Sources : Sections 1.1 to 1.5, additional materials (in these notes) on regression analysis. Historical perspectives and basic definitions. Planning and implementation of experiments. Fisher’s fundamental principles. Simple linear regression. Multiple regression, variable selection. Regression diagnostics. 2
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Historical Perspectives Agricultural Experiments : Comparisons and selection of varieties (and/or treatments) in the presence of uncontrollable field conditions, Fisher’s pioneering work on design of experiments and analysis of variance (ANOVA). Industrial Era : Process modeling and optimization, Large batch of materials, large equipments, Box’s work motivated in chemical industries and applicable to other processing industries, regression modeling and response surface methodology. 3
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Historical Perspectives (Contd.) Quality Revolution : Quality and productivity improvement, variation reduction, total quality management, Taguchi’s work on robust parameter design, Six-sigma movement. A lot of successful applications in manufacturing (cars, electronics, home appliances, etc.) Current Trends and Potential New Areas : Computer modeling and experiments, large and complex systems, applications to biotechnology, nanotechnology, material development, etc. 4
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Types of Experiments Treatment Comparisons : Purpose is to compare several treatments of a factor (have 4 rice varieties and would like to see if they are different in terms of yield and drought resistance). Variable Screening : Have a large number of factors, but only a few are important. Experiment should identify the important few. Response Surface Exploration : After important factors have been identified, their impact on the system is explored; regression model building. 5
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Types of Experiments (Contd.) System Optimization : Interested in determining the optimum conditions (e.g., maximize yield of semiconductor manufacturing or minimize defects). System Robustness : Wish to optimize a system and also reduce the impact of uncontrollable (noise) factors. (e.g., would like cars to run well in different road conditions and different driving habits; an IC fabrication process to work well in different conditions of humidity and dust levels). 6
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Some Definitions Factor : variable whose influence upon a response variable is being studied in the experiment. Factor Level : numerical values or settings for a factor.
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