Data defines the model by dint of genetic programming, producing the best decile table.
References Articles
Bruce Ratner, Ph.D.


    1. Accidental Statistician: Who Can Befitted of a Self-described Caption?
    2. A Popular Statistical Term Coined with the Formula X's Y
    3. "Few things are harder to put up with than the annoyance of a good (statistics) example"
    4. Survival of the Fittest: Who Coined It, and When?
    5. How Does Spearman's Coefficient Relate to Pearson's Coefficient?
    6. Calculating the Average Correlation Coefficient: Why?
    7. What If There Were No Significance Testing?
    8. Predicting the Quality of Your Statistical Regression Models
    9. Pop Quiz on Pi
    10. Linear Probability, Logit, and Probit Models: How Do They Differ?
    11. The Correlation Coefficient: Definition
    12. Calculating the Average Correlation Coefficient
    13. Genetic Data Mining Method for the Proper Use of the Correlation Coefficient
    14. Logistic Regression: Definition
    15. CHAID: Its Original Intent
    16. CHAID for Uncovering Relationships: A Data Mining Tool
    17. Market Segmentation: Defining Target Markets with CHAID
    18. The Working Concepts for Building a Database Acquisition Model
    19. The Working Concepts for Building a Database Retention Model
    20. The Working Concepts for Building a Database Attrition Model
    21. Optimizing Website Content via the Taguchi Method
    22. Sensitivity Analysis for Database Marketing Models
    23. Creating a SAS8 Dataset from a SAS9 Dataset
    24. A Very Automatic Coding of Dummy Variables
    25. Einstein: A Clever, Self-taught Statistician
    26. Data Mining Paradigm: Historical Perspective
    27. Data Mining: An Ill-defined Concept
    28. Karl Pearson: Everybody Knows His Correlation Coefficient, but Not How “Close” the Binomial Distribution is to a Normal Distribution
    29. Florence Nightingale: You Know Her as the Pioneer of Modern Nursing, But as a Passionate Statistician!
    30. Statistical Terms: Who Coined Them, and When?
    31. Historical Notes on the Two Most Popular Prediction Models, and One Not-yet Popular Model
    32. Different Data, Identical Regression Models: Which Model is Better?
    33. The Importance of Straight Data: For Simplicity, Desirable for Good Modeling
    34. The Correlation Coefficient: Its Values Range Between Plus/Minus 1, or Do They?
    35. Given an Irrational Number, are the Digits after the Decimal Point Random? 
    36. Given the Irrational Number Pi, are the Digits after the Decimal Point Random?
    37. Confusion Matrix: Perhaps Confusing, but Definitely Biased
    38. Handling Qualitative Attributes: Upgrading Discrete Heritable Information

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.
Sign-up for a free GenIQ webcast: Click here.


DM STAT-1 CONSULTING / br@dmstat1.com
574 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