Design and Analysis of Experiments by Douglas Montgomery: A Supplement for Using JMP

Description

With a growing number of scientists and engineers using JMP software for design of experiments, there is a need for an example-driven book that supports the most widely used textbook on the subject, Design and Analysis of Experiments by Douglas C. Montgomery. Design and Analysis of Experiments by Douglas Montgomery: A Supplement for Using JMP meets this need and demonstrates all of the examples from the Montgomery text using JMP. In addition to scientists and engineers, undergraduate and graduate students will benefit greatly from this book. While users need to learn the theory, they also need to learn how to implement this theory efficiently on their academic projects and industry problems. In this first book of its kind using JMP software, Rushing, Karl and Wisnowski demonstrate how to design and analyze experiments for improving the quality, efficiency, and performance of working systems using JMP. Topics include JMP software, two-sample t-test, ANOVA, regression, design of experiments, blocking, factorial designs, fractional-factorial designs, central composite designs, Box-Behnken designs, split-plot designs, optimal designs, mixture designs, and 2 k factorial designs. JMP platforms used include Custom Design, Screening Design, Response Surface Design, Mixture Design, Distribution, Fit Y by X, Matched Pairs, Fit Model, and Profiler. With JMP software, Montgomery?s textbook, and Design and Analysis of Experiments by Douglas Montgomery: A Supplement for Using JMP, users will be able to fit the design to the problem, instead of fitting the problem to the design.SAS Products and Releases: JMP: 9.0.2, 11.0, 10.0.2, 10.0.1, 10.0 Operating Systems: All Additional Resources Contents About This Book About These Authors Acknowledgments Chapter 1 Introduction Chapter 2 Simple Comparative Experiments Section 2.2 Basic Statistical Concepts Section 2.4.1 Hypothesis Testing Section 2.4.3 Choice of Sample Size Section 2.5.1 The Paired Comparison Problem Section 2.5.2 Advantages of the Paired Comparison Design Chapter 3 Experiments with a Single Factor: The Analysis of Variance Section 3.1 A One-way ANOVA Example Section 3.4 Model Adequacy Checking Section 3.8.1 Single Factor Experiment Section 3.8.2 Application of a Designed Experiment Section 3.8.3 Discovering Dispersion Effects Chapter 4 Randomized Blocks, Latin Squares, and Related Designs Section 4.2 Creating a Latin Square Design in JMP Chapter 5 Introduction to Factorial Designs Example 5.1 The Battery Design Experiment Example 5.2 A Two-Factor Experiment with a Single Replicate Example 5.3 The Soft Drink Bottling Problem Example 5.4 The Battery Design Experiment with a Covariate Example 5.5 A 3² Factorial Experiment with Two Replicates Example 5.6 A Factorial Design with Blocking Chapter 6 The 2ᵏ Factorial Design Section 6.2 The 2² design Example 6.1 A 2³ Design Example 6.2 A Single Replicate of the 2⁴ Design Example 6.3 Data Transformation in a Factorial Design Example 6.5 Duplicate Measurements on the Response Example 6.6 Credit Card Marketing Example 6.7 A 2⁴ Design with Center Points Chapter 7 Blocking and Confounding in the 2ᵏ Factorial Design Example 7.1 A 2ᵏ Replicated Factorial Design with Blocking Example 7.2 Blocking and Confounding in an Unreplicated Design Example 7.3 A 2³ Design with Partial Confounding Chapter 8 Two-Level Fractional Factorial Designs Example 8.1 A Half-Fraction of the 2⁴ Design Example 8.2 A 2⁵⁻¹ Design Used for Process Improvement Example 8.3 A 2⁴⁻¹ Design with the Alternate Fraction Example 8.4 A 2⁶⁻² Design Example 8.5 A 2⁷⁻³ Design Example 8.6 A 2⁸⁻³ Design in Four Blocks Example 8.7 A Fold-Over 2⁷⁻⁴ Resolution III Design Example 8.8 The Plackett-Burman Design Section 8.7.2 Sequential Experimentation with Resolution IV Designs Chapter 9 Three-Level and Mixed-Level Factorial and Fractional Factorial Designs Example 9.1 The 3³ Design Example 9.2 The 3² Design Confounded in 3 Blocks Example 9.3 The Spin Coating Experiment Example 9.4 An Experiment with Unusual Blocking Requirements Chapter 10 Fitting Regression Models Example 10.1 Multiple Linear Regression Model Example 10.2 Regression Analysis of a 2³ Factorial Design Example 10.3 A 2³ Factorial Design with a Missing Observation Example 10.4 Inaccurate Levels in Design Factors Example 10.6 Tests on Individual Regression Coefficients Example 10.7 Confidence Intervals on Individual Regression Coefficients Chapter 11 Response Surface Methods and Designs Example 11.1 The Path of Steepest Ascent Example 11.2 Central Composite Design Section 11.3.4 Multiple Responses Example 11.4 Space Filling Design with Gaussian Process Model Example 11.5 A Three-Component Mixture Example 11.6 Paint Formulation Chapter 12 Robust Parameter Design and Process Robustness Studies Example 12.1 Two Controllable Variables and One Noise Variable Example 12.2 Two Controllable Variables and Three Noise Variables Chapter 13 Experiments with Random Factors Example 13.1 A Measurement Systems Capability Study Example 13.3 The Unrestricted Model Example 13.5 A Three-Factor Factorial Experiment with Random Factors Example 13.6 Approximate F Tests Chapter 14 Nested and Split-Plot Designs Example 14.1 The Two-Stage Nested Design Example 14.2 A Nested-Factorial Design Section 14.4 The Experiment on the Tensile Strength of Paper Example 14.3 A 2⁵⁻¹ Split-Plot Experiment Chapter 15 Other Design and Analysis Topics Example 15.1 Box-Cox Transformation Example 15.2 The Generalized Linear Model and Logistic Regression Example 15.3 Poisson Regression Example 15.4 The Worsted Yarn Experiment Section 15.2 Unbalanced Data in a Factorial Design Example 15.5 Analysis of Covariance Section 15.3.4 Factorial Experiments with Covariates Index