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Quality by experimental design / Thomas B. Barker, Professor Emeritus, Rochester Institute of Technology, New York, Andrew Milivojevich, Knowledge Management Group, Mississauga, Ontario, Canada

Author:
Barker, Thomas B., 1941-
Published:
Boca Raton : CRC Press, Taylor & Francis Group, CRC Press is an imprint of the Taylor & Francis Group, an informa business, [2016]
Edition:
Fourth edition.
Physical Description:
xxxii, 721 pages : illustrations ; 26 cm
Additional Creators:
Milivojevich, Andrew

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Contents:
Machine generated contents note: 1.Why Design Experiments? -- Uses of Experimental Design -- Efficiency -- First, Experiment -- Second, Required Information -- Third, Resources -- A General Example -- A Note on the Simulation -- Going-In Assumptions for Simulation -- Reasons for Designed Experiments -- Structured Plan of Attack -- Meshes with Statistical Analysis Tools -- Forces Experimenter to Organize -- Efficiency -- Appendix: Key Concepts from This Chapter -- Some Uses of Experimental Design -- Four Reasons for Experimental Design -- Efficiency -- Test -- Experiment -- Required Information -- Resources -- 2.Organizing the Experiment -- The Elements of a Good Experiment -- Prior Knowledge -- The Qualities of a Response -- Goals and Objectives -- Gathering Information -- Organizational Psychology -- The Brainstorming Process -- Experimental Phases -- Appendix: Key Concepts from This Chapter -- Example of Goals and Objectives -- Guidelines for Brainstorming -- Sample Report -- 3.The Neglected Response Variable -- Quantitative -- Precise -- Meaningful -- Appendix: Key Concepts from This Chapter -- 4.The Factorial Two-Level Design and General Factorial Designs -- Orthogonality -- Design Units -- Yates Order -- Using Minitab -- Plotting Interactions -- Cost -- General Factorial Designs -- Using Minitab -- Appendix -- Definitions -- Formulas -- 5.Fractional Factorials at Two Levels -- About Interactions -- A Simple Example -- Fractionalization Element -- More Factors[—]Smaller Fractions -- The Logical Approach (Information Analysis) -- Information Analysis -- Other Designs -- Putting It All to Use -- Using Minitab -- Resolution -- Final Thoughts -- Appendix -- Definitions -- 2k-p Defining Contrast Algorithm and Modulus Algebra Rules -- Four and Eight Treatment Combination -- Design Matrix Template -- Sixteen Treatment Combination -- Design Matrix Templates -- 6.Multilevel Designs -- The Central Composite Design (CCD) -- A Note on Choosing Levels -- Strategies of Using a CCD -- Using Minitab to Build a CCD -- Comments on the Minitab CCD -- Building a Custom CCD in Minitab -- Final Thoughts -- Appendix -- Some Functional Relationships and Their Polynomial Forms -- CCD: Center Composite Design -- 7.Three-Level Designs -- 3k-p Designs -- Generating a 3k-P Design -- Information and Resources -- 3k-p Design Rules -- Larger 3k-p Designs -- A Comment on Three-Level Fractions -- Appendix -- 3k-p Design Rules -- Using Minitab -- 3k-P in Minitab -- 8.Blocking in Factorial Designs -- Basis of Blocking -- Choice of Primary Blocks -- Appendix -- Definitions -- Blocking with Minitab -- Blocked Fractional Factorials -- Comments on Blocked Fractional Factorials -- 9.Randomized Block and Latin Square -- Complete Randomized Block -- Generalization of Results -- Misconceptions in Using Blocked Designs -- Latin Squares -- The Misuse of the Latin Square -- Appendix -- Definitions -- 10.Nested Designs -- A Nested Design -- Coals to Kilowatts -- Summary -- Appendix -- Definitions -- 11.Evolutionary Operation -- The Prime Directive of Manufacturing -- Evolutionary Operation -- A Slow Experience -- Use of the EVOP Worksheet -- Signal to Noise in EVOP -- Time for Decision -- Phase II -- Equations from EVOP -- Appendix -- Definitions -- 12.Simple Analysis -- Hypothesis Testing -- Example 1 -- The Alternative Hypothesis -- Alpha Risk -- Beta Risk -- Steps in Hypothesis Testing -- A Designed Hypothesis Test -- Tests between Two Means -- Buying a Copier -- Response Variable -- Setting Risks -- Setting the Important Difference -- Determining Variation -- Finding the Number of Observations -- What It All Means -- Pooling Variances -- Appendix A -- Some Basic Rules of Probability -- Basic Statistical Formulas -- Steps in Hypothesis Testing -- Construction of OC Curves -- Construction of the OC Curve -- Road Map of Significance Testing -- Appendix B -- Using Minitab -- 13.Analysis of Means by Using the Variance -- This Remedy Is Spelled A-N-O-V-A -- Signal/Noise -- No Difference -- Big Difference -- An Apple a Day -- Investigating the Residuals -- A Learning Example -- The Correct Approach -- Comparisons of Individual Levels -- More Conclusions -- Functional Relationships -- ANOVA Assumptions (Requirements) -- Test for Homogeneity of Variance -- Test for Normality -- Appendix A -- Definitions -- Appendix B -- Minitab and ANOVA -- Specifying the Model -- Validating the Model Requirements -- Plotting the Factor Effects -- Using "Automated DOE" in Minitab -- Specifying the Model -- Validating the Model Requirement -- Plotting the Factor Effects -- 14.Yates Analysis: Analysis of 2k and 2k-P Designs -- Using Minitab -- Application to 2k -P Fractional Designs -- Half Effects -- Deconfounding Effects -- Minimum Number of Deconfounding Runs -- Fold-Over Designs and Analysis -- Appendix -- Definitions -- More on Using Minitab -- 15.Matrix Algebra -- Matrix Defined -- Transposition -- Multiplication -- Matrix Division -- Appendix -- Definitions -- 16.Least Squares Analysis -- Least Squares Developed -- Using the Formula -- The Role of the Matrix -- The Dummy Factor -- Regression, ANOVA, and Yates -- Appendix -- Definitions -- 17.Putting ANOVA and Least Squares to Work -- Using the Half Effects -- Plotting Interactions -- Plotting Curves -- Using the Computer -- Obtaining the Data and Analysis -- Appendix A -- Appendix B -- Calculating Regression Coefficients from Half Effects -- 18.ANOVA for Blocked and Nested Designs -- Using Minitab -- Comments on the Minitab Analysis -- Complete Randomized Block -- Paired Comparison -- Latin Square -- Split-Plot Analysis -- Missing Data -- Nested Designs -- Using the Hierarchy -- Appendix A -- Definitions -- More on EMS -- Nested Sums of Squares from a Crossed ANOVA -- Appendix B -- Minitab -- General Blocked Design -- Paired Comparison in Minitab -- Latin Square in Minitab -- Nested Design and Analysis in Minitab -- Split-Plot in Minitab -- Epilogue -- 19.Case History of an Experimental Investigation -- The Phase Approach -- Example One: A Popcorn Formula -- Phase II -- Volume -- Yield -- Taste -- Example Two: A Photographic Process -- The Concept Phase -- Defining the Required Information -- Adding Levels -- The Second Phase -- Plotting the Results -- Constructing the Model -- A Final Note on the Developer Experiment -- Example Three: The KlatterMax -- Background -- KlatterMax: The Details (Appendix to Report) -- Practice Problem: Photographic Emulsion -- Visual Basic Program to Generate Emulsion Example Responses -- Use of Emulsion Program -- Appendix -- 20.Robust Design -- The Concept of Quality -- Expected Loss (for a Distribution) -- An Application of the Expected Loss Function -- The Signal to Noise Transformation[—]or Finding the Elusive Loss Function -- S/N Compared with Expected Loss -- Using the S/N in Optimization Experiments -- The Parameter Design -- Process Capability Study Approach -- A Designed Approach -- Beyond Just "Does It Work?" -- Structure of the Parameter Design -- Outside Noise Factors -- Formulating the Vaccine from the Noise Matrices -- Using Minitab for Parameter Design -- Analysis Procedure in Minitab -- Interpretation of the Plots -- Practical Parameter Design: Internal Stress Method -- Interpretation of Results -- Proof in the Pudding -- Considerations for the Internal Stress Approach -- Theory of Parameter Design -- Strategy of Seeking an Optimum -- Summary -- What's Next? -- Appendix: OAs -- Two-Level Orthogonal Arrays -- Three-Level Orthogonal Arrays -- The Use of Linear Graphs -- 21.Monte Carlo Simulation and Tolerance Design -- Simulation -- Combining Simulations and Probabilities -- Application to More Complex Situations -- Random Numbers -- Transmission of Variation -- Adding a Frequency Distribution -- Tolerance Design -- Making Uniform Distributions Look Like Normals -- Finding the Quality Sensitive Components: A Sensitivity Analysis -- Using Minitab for Tolerance Design -- Using the Percentage Contribution to Rationally Apply Tolerances -- Appendix -- 22.Case History Completed: The Utilization of the Equation -- Random Method -- Another Approach -- ANOVA of the Results -- Appendix -- Control Chart Monte Carlo Visual Basic Program -- 23.Introduction to Mixture Experiments -- The Simple Two-Ingredient Mixture Experiment: An Example from Real Life -- The Prediction Equation -- Testing the Significance of the Coefficients -- The Analysis of Variance (ANOVA) -- Step 1: Computing the Predicted Values -- Step 2: Computing the Sum of Squares -- Residual Error versus Pure Error -- Explaining Lack of Fit -- Testing for Lack of Fit -- Minitab -- Summary -- Appendix -- Definitions -- 24.Simplex Lattice Design -- The Three-Ingredient Simplex Lattice Mixture Experiment: An Example from Real Life -- Plotting the Effects -- The Prediction Equation -- Testing the Significance of the Regression Coefficients -- Variance Inflation Factor -- The Analysis of Variance (ANOVA) -- Step 1: Computing the Predicted Values -- Step 2: Computing the Sum of Squares -- Testing for Lack of Fit and the Suitability of the Prediction Equation -- Standard Error of Regression (S) -- Coefficient of Determination R2 -- R2 Predicted -- Adjusted R2 -- Minitab -- Summary -- A Note about Replication -- MSPE versus MSE -- Final Thoughts -- Appendix -- Definitions -- The Standard Form of the First and Second Degree Mixture Polynomial -- The Standard Form of the First Degree Mixture Polynomial -- The Standard Form of the Second Degree Mixture Polynomial -- 25.The Simplex Centroid Design -- Principal Axes -- The Three-Ingredient Simplex Centroid Design -- Three-Ingredient Simplex Centroid Experiment: An Example from Real Life -- The Prediction Equation -- Computing Pure Error -- Computing the Model Coefficients -- Graphing the Effect of the Binary Blends -- Minitab Output -- Multicollinearity -- R2 versus Adjusted R2 -- Minitab -- Summary -- Appendix -- Definitions -- 26.Constrained Mixtures -- and Contents note continued: Lower Constraints -- Computing the Upper Bounds -- Computing the Coordinates along the Midpoints of an Edge and Centroid -- Upper and Lower Constraints -- Checking the Consistency of Upper and Lower Constraints -- The XVERT Algorithm -- Algorithm Steps -- Adding a Centroid -- Minitab -- Summary -- Appendix -- Definitions -- 27.Statistical Tables and Graphs.
Subject(s):
ISBN:
9781482249668 (Hardback : acid-paper)
1482249669 (Hardback : acid-paper)
Note:
"A Chapman & Hall Book."
Bibliography Note:
Includes bibliographical references (pages 703-706) and index.
View MARC record | catkey: 17280379