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Logistic Regression, and Related Issues

1. Logistic Regression: An Overview

2. CHAID For Interpreting A Logistic Regression Model

3. The Importance of the Regression Coefficient

4. Alternative Direct Marketing Response Models:
Linear Probability, Logit And Probit Models

5. Assessing the Importance of Variables in Database Response Models 

6. Determining Which Variables in a Model Are Its Most Important Predictors: The Predictive Contribution Coefficient

7. Statistics versus Machine Learning: A Significant Difference for Database Response Modeling

8. Assessment of Direct Marketing Response Models

9. Market Segment Classification Modelling with Logistic Regression

10. A Genetic Approach to Building a Database Marketing Censored Regression Model

11. Think GenIQ Model: Rethink Regression Model

12. Modeling a Distribution with a Mass at Zero 

13. Model Selection Is A Problem 

14. A Database Marketing Model for Zero-inflated Data 

15. Building A Database Response Model for Categorical Data 

16. A Genetic Approach to Building a Database Marketing Censored Regression Model

17. Using the GenIQ Model to Insure the Validation of a Model is Unbiased

18. Algorithmic Methods: Non-Statistical Methods Solving Statistical Problems

19. A Phat Example of the GenIQ Model's Predictive Power

20. Analysis and Modeling for Today's Data

21. Interpretation of Coefficient-free Models

22. An Alternative Response Model

23. Discrimination Between Alternative Binary Response Models

24. Direct Response Marketing

25. Market Segment Classification Modeling with Machine Learning

26. A 9-Step Computer Program for Analysts Who Want to Better Their Modeling


For more information about this article, call Bruce Ratner at 516.791.3544 or
1 800 DM STAT-1; or e-mail at