Fundamentals of Predictive Analytics with JMP

Table of contents

1. Contents
2. About This Book
3. About These Authors
4. Acknowledgments
5. Chapter 1: Introduction
   1. Two Questions Organizations Need to Ask
      1. Return on Investment (ROI)
      2. Culture Change
   2. Business Intelligence
   3. Clarification
   4. Book Focus
   5. Introductory Statistics Courses
   6. Practical Statistical Study
   7. Plan Perform, Analyze, Reflect (PPAR) Cycle
   8. References
6. Chapter 2: Statistics Review
   1. Always Take a Random and Representative Sample
   2. Statistics Is Not an Exact Science
   3. Understand a Z Score
   4. Understand the Central Limit Theorem
   5. Understand One-Sample Hypothesis Testing and p-Values
   6. Many Approaches/Techniques Are Correct, and a Few Are Wrong
7. Chapter 3: Introduction to Multivariate Data
   1. Multivariate Data and Multivariate Data Analysis
   2. Using Tables to Explore Multivariate Data
   3. Using Graphs to Explore Multivariate Data
8. Chapter 4: Regression and ANOVA Review
   1. Regression
      1. Simple Regression
      2. Multiple Regression
      3. Regression with Categorical Data
   2. ANOVA
      1. One-way ANOVA
      2. Testing Statistical Assumptions
      3. Testing for Differences
      4. Two-way ANOVA
   3. References
9. Chapter 5: Logistic Regression
   1. Dependence Technique: Logistic Regression
   2. The Linear Probability Model (LPM)
   3. The Logistic Function
   4. Example: toylogistic.jmp
   5. Odds Ratios in Logistic Regression
   6. A Logistic Regression Statistical Study
   7. References
   8. Exercises
10. Chapter 6: Principal Components Analysis
    1. Principal Component
    2. Dimension Reduction
    3. Discovering Structure in The Data
    4. Exercises
11. Chapter 7: Cluster Analysis
    1. Hierarchical Clustering
    2. Using Clusters in Regression
    3. K-means Clustering
    4. K-means versus Hierarchical Clustering
    5. References
    6. Exercises
12. Chapter 8: Decision Trees
    1. An Example of Classification Trees
    2. An Example of a Regression Tree
    3. References
    4. Exercises
13. Chapter 9: Neural Networks
    1. Validation Methods
    2. Hidden Layer Structure
    3. Fitting Options
    4. Data Preparation
    5. An Example
    6. Summary
    7. References
    8. Exercises
14. Chapter 10: Model Comparison
    1. Model Comparison with Continuous Dependent Variable
    2. Model Comparison with Binary Dependent Variable
    3. Model Comparison Using the Lift Chart
    4. Train, Validate, and Test
    5. References
    6. Exercises
15. Chapter 11: Telling the Statistical Story
    1. From Multivariate Data to the Modeling Process
    2. What Is Data Mining?
    3. A Framework for Predictive Analytics Techniques
    4. The Goal, Tasks, and Phases of Predictive Analytics
    5. References
16. Appendix
    1. Smaller Data Sets
    2. Large Case Data Sets
17. Index
18. Accelerate Your SAS Knowledge with SAS Books