Data defines the model by dint of genetic programming, producing the best decile table.
References Articles
Bruce Ratner, Ph.D.
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    1. Predicting the Quality of Your Statistical Regression Models
    2. Pop Quiz on Pi
    3. Linear Probability, Logit, and Probit Models: How Do They Differ?
    4. The Correlation Coefficient: Definition
    5. Calculating the Average Correlation Coefficient
    6. Genetic Data Mining Method for the Proper Use of the Correlation Coefficient
    7. Logistic Regression: Definition
    8. CHAID: Its Original Intent
    9. CHAID for Uncovering Relationships: A Data Mining Tool
    10. Market Segmentation: Defining Target Markets with CHAID
    11. The Working Concepts for Building a Database Acquisition Model
    12. The Working Concepts for Building a Database Retention Model
    13. The Working Concepts for Building a Database Attrition Model
    14. Optimizing Website Content via the Taguchi Method
    15. Sensitivity Analysis for Database Marketing Models
    16. Creating a SAS8 Dataset from a SAS9 Dataset
    17. A Very Automatic Coding of Dummy Variables
    18. Einstein: A Clever, Self-taught Statistician
    19. Data Mining Paradigm: Historical Perspective
    20. Data Mining: An Ill-defined Concept
    21. Karl Pearson: Everybody Knows His Correlation Coefficient, but Not How “Close” the Binomial Distribution is to a Normal Distribution
    22. Florence Nightingale: You Know Her as the Pioneer of Modern Nursing, But as a Passionate Statistician!
    23. Statistical Terms: Who Coined Them, and When?
    24. Historical Notes on the Two Most Popular Prediction Models, and One Not-yet Popular Model
    25. Different Data, Identical Regression Models: Which Model is Better?
    26. The Importance of Straight Data: For Simplicity, Desirable for Good Modeling
    27. The Correlation Coefficient: Its Values Range Between Plus/Minus 1, or Do They?
    28. Confusion Matrix: Perhaps Confusing, but Definitely Biased

For more information about this article, call Bruce Ratner at 516.791.3544 or 1 800 DM STAT-1; or e-mail at br@dmstat1.com.
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