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Preface to Third Edition
Preface of Second Edition
Acknowledgments
Author
1. Introduction
2. Science Dealing with Data: Statistics and Data Science
3. Two Basic Data Mining Methods for Variable Assessment
4. CHAID-Based Data Mining for Paired-Variable Assessment
5. The Importance of Straight Data Simplicity and Desirability for Good Model-Building Practice
6. Symmetrizing Ranked Data: A Statistical Data Mining Method for Improving the Predictive Power of Data
7. Principal Component Analysis: A Statistical Data Mining Method for Many-Variable Assessment
8. Market Share Estimation: Data Mining for an Exceptional Case
9. The Correlation Coefficient: Its Values Range between Plus and Minus 1, or Do They?
10. Logistic Regression: The Workhorse of Response Modeling
11. Predicting Share of Wallet without Survey Data
12. Ordinary Regression: The Workhorse of Profit Modeling
13. Variable Selection Methods in Regression: Ignorable Problem, Notable Solution
14. CHAID for Interpreting a Logistic Regression Model
15. The Importance of the Regression Coefficient
16. The Average Correlation: A Statistical Data Mining Measure for Assessment of Competing Predictive Models and the Importance of the Predictor Variables
17. CHAID for Specifying a Model with Interaction Variables
18. Market Segmentation Classification Modeling with Logistic Regression
19. Market Segmentation Based on Time-Series Data Using Latent Class Analysis
20. Market Segmentation: An Easy Way to Understand the Segments
21. The Statistical Regression Model: An Easy Way to Understand the Model
22. CHAID as a Method for Filling in Missing Values
23. Model Building with Big Complete and Incomplete Data
24. Art, Science, Numbers, and Poetry
25. Identifying Your Best Customers: Descriptive, Predictive, and Look-Alike Profiling
26. Assessment of Marketing Models
27. Decile Analysis: Perspective and Performance
28. Net T-C Lift Model: Assessing the Net Effects of Test and Control Campaigns
29. Bootstrapping in Marketing: A New Approach for Validating Models
30. Validating the Logistic Regression Model: Try Bootstrapping
31. Visualization of Marketing Models: Data Mining to Uncover Innards of a Model
32. The Predictive Contribution Coefficient: A Measure of Predictive Importance
33. Regression Modeling Involves Art, Science, and Poetry, Too
34. Opening the Dataset: A Twelve-Step Program for Dataholics
35. Genetic and Statistic Regression Models: A Comparison
36. Data Reuse: A Powerful Data Mining Effect of the GenIQ Model
37. A Data Mining Method for Moderating Outliers Instead of Discarding Them
38. Overfitting: Old Problem, New Solution
39. The Importance of Straight Data: Revisited
40. The GenIQ Model: Its Definition and an Application
41. Finding the Best Variables for Marketing Models
42. Interpretation of Coefficient-Free Models
43. Text Mining: Primer, Illustration, and TXTDM Software
44. Some of My Favorite Statistical Subroutines
Index
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