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

Book Description

Big data: It's unstructured, it's coming at you fast, and there's lots of it. In fact, the majority of big data is text-oriented, thanks to the proliferation of online sources such as blogs, emails, and social media.

However, having big data means little if you can't leverage it with analytics. Now you can explore the large volumes of unstructured text data that your organization has collected with Text Mining and Analysis: Practical Methods, Examples, and Case Studies Using SAS.

This hands-on guide to text analytics using SAS provides detailed, step-by-step instructions and explanations on how to mine your text data for valuable insight. Through its comprehensive approach, you'll learn not just how to analyze your data, but how to collect, cleanse, organize, categorize, explore, and interpret it as well. Text Mining and Analysis also features an extensive set of case studies, so you can see examples of how the applications work with real-world data from a variety of industries.

Text analytics enables you to gain insights about your customers' behaviors and sentiments. Leverage your organization's text data, and use those insights for making better business decisions with Text Mining and Analysis.

Table of Contents

  1. About This Book
  2. About The Authors
  3. Acknowledgments
  4. Chapter 1 Introduction
  5. Chapter 2 Simple Comparative Experiments
    1. Section 2.2 Basic Statistical Concepts
    2. Section 2.4.1 Hypothesis Testing
    3. Section 2.4.3 Choice of Sample Size
    4. Section 2.5.1 The Paired Comparison Problem
    5. Section 2.5.2 Advantages of the Paired Comparison Design
  6. Chapter 3 Experiments with a Single Factor: The Analysis of Variance
    1. Section 3.1 A One-way ANOVA Example
    2. Section 3.4 Model Adequacy Checking
    3. Section 3.8.1 Single Factor Experiment
    4. Section 3.8.2 Application of a Designed Experiment
    5. Section 3.8.3 Discovering Dispersion Effects
  7. Chapter 4 Randomized Blocks, Latin Squares, and Related Designs
    1. Section 4.2 Creating a Latin Square Design in JMP
  8. Chapter 5 Introduction to Factorial Designs
    1. Example 5.1 The Battery Design Experiment
    2. Example 5.2 A Two-Factor Experiment with a Single Replicate
    3. Example 5.3 The Soft Drink Bottling Problem
    4. Example 5.4 The Battery Design Experiment with a Covariate
    5. Example 5.5 A 32 Factorial Experiment with Two Replicates
    6. Example 5.6 A Factorial Design with Blocking
  9. Chapter 6 The 2k Factorial Design
    1. Section 6.2 The 22 design
    2. Example 6.1 A 23 Design
    3. Example 6.2 A Single Replicate of the 24 Design
    4. Example 6.3 Data Transformation in a Factorial Design
    5. Example 6.5 Duplicate Measurements on the Response
    6. Example 6.6 Credit Card Marketing
    7. Example 6.7 A 24 Design with Center Points
  10. Chapter 7 Blocking and Confounding in the 2k Factorial Design
    1. Example 7.1 A 2k Replicated Factorial Design with Blocking
    2. Example 7.2 Blocking and Confounding in an Unreplicated Design
    3. Example 7.3 A 23 Design with Partial Confounding
  11. Chapter 8 Two-Level Fractional Factorial Designs
    1. Example 8.1 A Half-Fraction of the 24 Design
    2. Example 8.2 A 25-1 Design Used for Process Improvement
    3. Example 8.3 A 24-1 Design with the Alternate Fraction
    4. Example 8.4 A 26-2 Design
    5. Example 8.5 A 27-3 Design
    6. Example 8.6 A 28-3 Design in Four Blocks
    7. Example 8.7 A Fold-Over 27-4 Resolution III Design
    8. Example 8.8 The Plackett-Burman Design
    9. Section 8.7.2 Sequential Experimentation with Resolution IV Designs
  12. Chapter 9 Three-Level and Mixed-Level Factorial and Fractional Factorial Designs
    1. Example 9.1 The 33 Design
    2. Example 9.2 The 32 Design Confounded in 3 Blocks
    3. Example 9.3 The Spin Coating Experiment
    4. Example 9.4 An Experiment with Unusual Blocking Requirements
  13. Chapter 10 Fitting Regression Models
    1. Example 10.1 Multiple Linear Regression Model
    2. Example 10.2 Regression Analysis of a 23 Factorial Design
    3. Example 10.3 A 23 Factorial Design with a Missing Observation
    4. Example 10.4 Inaccurate Levels in Design Factors
    5. Example 10.6 Tests on Individual Regression Coefficients
    6. Example 10.7 Confidence Intervals on Individual Regression Coefficients
  14. Chapter 11 Response Surface Methods and Designs
    1. Example 11.1 The Path of Steepest Ascent
    2. Example 11.2 Central Composite Design
    3. Section 11.3.4 Multiple Responses
    4. Example 11.4 Space Filling Design with Gaussian Process Model
    5. Example 11.5 A Three-Component Mixture
    6. Example 11.6 Paint Formulation
  15. Chapter 12 Robust Parameter Design and Process Robustness Studies
    1. Example 12.1 Two Controllable Variables and One Noise Variable
    2. Example 12.2 Two Controllable Variables and Three Noise Variables
  16. Chapter 13 Experiments with Random Factors
    1. Example 13.1 A Measurement Systems Capability Study
    2. Example 13.3 The Unrestricted Model
    3. Example 13.5 A Three-Factor Factorial Experiment with Random Factors
    4. Example 13.6 Approximate F Tests
  17. Chapter 14 Nested and Split-Plot Designs
    1. Example 14.1 The Two-Stage Nested Design
    2. Example 14.2 A Nested-Factorial Design
    3. Section 14.4 The Experiment on the Tensile Strength of Paper
    4. Example 14.3 A 25-1 Split-Plot Experiment
  18. Chapter 15 Other Design and Analysis Topics
    1. Example 15.1 Box-Cox Transformation
    2. Example 15.2 The Generalized Linear Model and Logistic Regression
    3. Example 15.3 Poisson Regression
    4. Example 15.4 The Worsted Yarn Experiment
    5. Section 15.2 Unbalanced Data in a Factorial Design
    6. Example 15.5 Analysis of Covariance
    7. Section 15.3.4 Factorial Experiments with Covariates
  19. Index