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Fundamentals of Predictive Analytics with JMP

Book Description

With the new emphasis on business intelligence, business analytics and predictive analytics, Fundamentals of Predictive Analytics with JMP is invaluable to everyone who needs to expand their knowledge of statistics and apply real problem-solving analysis.

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