DM STAT-1 DIGEST III -
Logistic Regression, and Related Issues
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
GO BACK TO ARTICLES PAGE.
1 800 DM STAT-1; or e-mail at br@dmstat1.com.
br@dmstat1.com574 Flanders Drive / North Woodmere, NY 11581 / U S A
Voice 1-516-791-3544 / Fax 1-516-791-5075
Toll Free 1 800 DM STAT-1