Wuchap_01_09fall - Unit 1 : Basic Concepts and Introductory...

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Unformatted text preview: Unit 1 : Basic Concepts and Introductory Regresssion Analysis Sources : Chapter 1. Historical perspectives and basic definitions (Section 1.1). Planning and implementation of experiments (Section 1.2). Fishers fundamental principles (Section 1.3). Simple linear regression (Sections 1.4-1.5). Multiple regression, variable selection (Sections 1.6-1.7). Example: Air Pollution Data (Section 1.8). 2 Historical perspectives Agricultural Experiments : Comparisons and selection of varieties (and/or treatments) in the presence of uncontrollable field conditions, Fishers pioneering work on design of experiments and analysis of variance (ANOVA). Industrial Era : Process modeling and optimization, Large batch of materials, large equipments, Boxs work motivated in chemical industries and applicable to other processing industries, regression modeling and response surface methodology. 3 Historical perspectives (Contd.) Quality Revolution : Quality and productivity improvement, variation reduction, total quality management, Taguchis 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 modelling and experiments, large and complex systems, applications to biotechnology, nanotechnology, material development, etc. 4 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 draught resistence). 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 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 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. Trial (or run ) : application of a treatment to an experimental unit. Treatment or level combination : set of values for all factors in a trial. Experimental unit : object to which a treatment is applied. Randomization : using a chance mechanism to assign treatments to experimental units or run order. 7 Systematic Approach to Experimentation State the objective of the study. Choose the response variable .......
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Wuchap_01_09fall - Unit 1 : Basic Concepts and Introductory...

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