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Building Better Models with JMP Pro

Book Description

Building Better Models with JMP® Pro provides an example-based introduction to business analytics, with a proven process that guides you in the application of modeling tools and concepts. It gives you the "what, why, and how" of using JMP® Pro for building and applying analytic models. This book is designed for business analysts, managers, and practitioners who may not have a solid statistical background, but need to be able to readily apply analytic methods to solve business problems. In addition, this book will greatly benefit faculty members who teach any of the following subjects at the lower to upper graduate level: predictive modeling, data mining, and business analytics. Novice to advanced users in business statistics, business analytics, and predictive modeling will find that it provides a peek inside the black box of algorithms and the methods used. Topics include: regression, logistic regression, classification and regression trees, neural networks, model cross-validation, model comparison and selection, and data reduction techniques. Full of rich examples, Building Better Models with JMP Pro is an applied book on business analytics and modeling that introduces a simple methodology for managing and executing analytics projects. No prior experience with JMP is needed. Make more informed decisions from your data using this newest JMP book.

Table of Contents

  1. Acknowledgments
  2. About This Book
  3. About These Authors
  4. Part 1 Introduction
    1. Chapter 1 Introduction
      1. Overview
      2. Analytics Is Hot!
      3. What You Will Learn
      4. Analytics and Data Mining
      5. How the Book Is Organized
      6. Let’s Get Started
      7. References
    2. Chapter 2 An Overview of the Business Analytics Process
      1. Introduction
      2. Commonly Used Process Models
      3. The Business Analytics Process
        1. Define the Problem
        2. Prepare for Modeling
        3. Modeling
        4. Deploy Model
        5. Monitor Performance
      4. Conclusion
      5. References
  5. Part 2 Preparing for Modeling
    1. Chapter 3 Working with Data
      1. Introduction
      2. JMP Basics
        1. Opening JMP and Getting Started
        2. JMP Data Tables
        3. Examining and Understanding Your Data
        4. Preparing Data for Modeling
        5. Summary and Getting Help in JMP
      3. Exercises
      4. References
  6. Part 3 Model Building
    1. Chapter 4 Multiple Linear Regression
      1. In the News
      2. Representative Business Problems
      3. Preview of End Result
      4. Looking Inside the Black Box: How the Algorithm Works
      5. Example 1: Housing Prices
        1. Applying the Business Analytics Process
        2. Summary
      6. Example 2: Bank Revenues
        1. Applying the Business Analytics Process
        2. Summary
      7. Exercises
      8. References
    2. Chapter 5 Logistic Regression
      1. In the News
      2. Representative Business Problems
      3. Preview of the End Result
      4. Looking Inside the Black Box: How the Algorithm Works
      5. Example 1: Lost Sales Opportunities
        1. Applying the Business Analytics Process
      6. Example 2: Titanic Passengers
        1. Applying the Business Analytics Process
      7. Summary
      8. Key Take-Aways and Additional Considerations
      9. Exercises
      10. References
    3. Chapter 6 Decision Trees
      1. In the News
      2. Representative Business Problems
      3. Preview of the End Result
      4. Looking Inside the Black Box: How the Algorithm Works
        1. Classification Tree for Status
        2. Statistical Details Behind Classification Trees
      5. Other General Modeling Considerations
        1. Exploratory Modeling versus Predictive Modeling
        2. Model Cross-Validation
        3. Dealing with Missing Values
        4. Decision Tree Modeling with Ordinal Predictors
      6. Example 1: Credit Card Marketing
        1. The Study
        2. Applying the Business Analytics Process
        3. Case Summary
      7. Example 2: Printing Press Yield
        1. The Study
        2. Applying the Business Analytics Process
        3. Case Summary
      8. Summary
      9. Exercises
      10. References
    4. Chapter 7 Neural Networks
      1. In the News
      2. Representative Business Problems
      3. Measuring Success
      4. Preview of the End Result
      5. Looking Inside the Black Box: How the Algorithm Works
        1. Neural Networks with Categorical Responses
      6. Example 1: Churn
        1. Applying the Business Analytics Process
        2. Modeling
        3. The Neural Model and Results
        4. Case Summary
      7. Example 2: Credit Risk
        1. Applying the Business Analytics Process
        2. Case Summary
      8. Summary and Key Take-Aways
      9. Exercises
      10. References
  7. Part 4 Model Selection and Advanced Methods
    1. Chapter 8 Using Cross-Validation
      1. Overview
      2. Why Cross-Validation?
      3. Partitioning Data for Cross-Validation
        1. Using a Random Validation Portion
        2. Specifying the Validation Roles for Each Row
        3. K-fold Cross-Validation
        4. Using Cross-Validation for Model Fitting in JMP Pro
      4. Example
        1. Creating Training, Validation, and Test Subsets
        2. Examining the Validation Subsets
        3. Using Cross-Validation to Build a Linear Regression Model
        4. Choosing the Regression Model Terms with Stepwise Regression
        5. Making Predictions
        6. Using Cross-Validation to Build a Decision Tree Model
        7. Fitting a Neural Network Model Using Cross-Validation
        8. Model Comparison
      5. Key Take-Aways
      6. Exercises
      7. References
    2. Chapter 9 Advanced Methods
      1. Overview
      2. Concepts in Advanced Modeling
        1. Bagging
        2. Boosting
        3. Regularization
      3. Advanced Partition Methods
        1. Bootstrap Forest
        2. Boosted Tree
      4. Boosted Neural Network Models
      5. Generalized Regression Models
        1. Maximum Likelihood Regression
        2. Ridge Regression
        3. Lasso Regression
        4. Elastic Net
      6. Key Take-Aways
      7. Exercises
      8. References
    3. Chapter 10 Capstone and New Case Studies
      1. Introduction
      2. Case Study 1: Cell Classification
        1. Stage 1: Define the Problem
        2. Stage 2: Prepare for Modeling
        3. Stage 3: Modeling
      3. Case Study 2: Blue Book for Bulldozers (Kaggle Contest)
        1. Getting to Know the Data
        2. Data Preparation
        3. Modeling
        4. Model Comparison
        5. Next Steps
      4. Case Study 3: Default Credit Card, Presenting Results to Management
        1. Developing a Management Report
      5. Case Study 4: Carvana (Kaggle Contest)
      6. Exercises
      7. References
  8. Appendix
  9. Index