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1)
Features
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2)
Extra-GenIQ Applications
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3)
Books
,
4)
Webcast
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5)
Analytics
,
6)
Solutions
,
7)
Reference Articles
,
8)
Useful SAS Code
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9)
Third Edition
.
1) Features
Shakespearian Modelogue
GenIQ: A Visual Introduction
Value-added B
enefits
of GenIQ
GenIQ as a Unique Data Mining Tool
GenIQ Lets the Data Specify the Model
GenIQs Predictive Power
GenIQ as a Data-straightener
GenIQs User-friendliness
GenIQs Model is Best for Allotted Time
What is Genetic Programming?
GenIQs 9-step Modeling Process
FAQs about GenIQ
How GenIQ Works
How To Use GenIQ
Scoring GenIQ Models with Excel
Nonrandom Words of Praise for GenIQ
Random Words of Praise for GenIQ
Analytical Model Development and Deployment
GenIQ: Nonlinear Curve Fitter
GenIQ: OLS Curve Fitter
A Method for Moderating Outliers, Instead of Discarding Them
GenIQ-enhanced Regression Model
GenIQ-enhanced/Data-reused Regression
Real World Data are Dirty: Data Cleaning and the "Noise" Problem
Statistical Modeling Problems: Nonissue for GenIQ
Overfitting: Old Problem, New Solution
Data Cleaning is Not Completed Until the “Noise” is Eliminated
2) Extra-GenIQ Applications
A Database Marketing Regression Model that Maximizes Cum Lift
Overfitting: Old Problem, New Solution
Data Cleaning is Not Completed Until the “Noise” is Eliminated
GenIQ-enhanced Regression Model
GenIQ-enhanced/Data-reused Regression
A Method for Moderating Outliers, Instead of Discarding Them
How to Make the Best Credit Score Even Better
GenIQ: Nonlinear Curve Fitter
GenIQ: OLS Curve Fitter
Real World Data are Dirty: Data Cleaning and the "Noise" Problem
Optimizing Website Content via the Taguchi Method
GenIQ Text-Miner Software
3) Books
Statistical Modeling
and
Analysis
for
Database Marketing:
Effective Techniques for Mining Big Data
NEW!
Statistical and Machine-Learning Data Mining: Techniques for Better Predictive Modeling and Analysis of Big Data
4) Webcast
For Regression Modelers & Data Miners:
The Unequaled, Unsuspected GenIQ Model
If
you can think …, then I promise … not to waste your time
5)
Analytics
Profile Analysis of Any Regression-based Model
Opening the Dataset: A Twelve-Step Program for Dataholics
Opening the Dataset: Confession of a Dataholic
Market Segmentation: An Easy Way to Understand the Segments
The Statistical Golden Rule: Measuring the Art and Science of Statistical Practice
What is Your First Data Step?
One Pound of Pennies: The Correlation Between the Mean Value of Pennies and the Skew of the Year of Mint
Statistically Confident: Asking a Dumb Question
Big Data and Learning Analtyics: A Recommended Resource
Stevens’ Four Scales of Measurement: The Addition of a New Scale
Apple and Orange Comparison: Statistically Fruitless or Fruitful?
Data Mining and the Golden Gut: Complementary, Supplementary or Mutually Exclusive?
Re-Modeling the Coupon Redemption Decision
Big Data, Schmea Data, It Still Boils Down to the Super Six Statistics
Book-Mash: Random Stacking of Statistics Books
A Glass of Water vs. A Can of Trash: What Say You, Half-Empty or Half-Full?
Wouldn’t It Be Nice to Have a Regression Technique that Builds the Best Model Possible Within an Allotted Time?
Life-Time Value Modeling of Big-ticket Items
My Statistics Floater: One-Sample Test for Two Mutually-Exclusive Proportions
Zero-Inflated Regression: Modeling a Distribution with a Mass at Zero
The Originative Regression Models: Are They too Old and Untenable?
Outperforming a Multi-Level Classification Model Whose Chance Performance is Large
Bruce Ratner's Statistical and Machne-Learning Data Mining Book is on Intel's Recommended Reading List
Building a Multi-Level Classification Model to Simultaneously Maximize Decile Tables for Each Level, Not the Traditional Confusion Matrix
Principal Component Analysis of Yesterday and Today
Building a Model to Insure a TEST Group Outperforms a CONTROL Group
The Uplift Model: Building a Database Model to Assess the True Impact of a Test Campaign
A Data Mining Method for Moderating Outliers, Instead of Discarding Them
The Originative Statistical Regression Models: Are They Too Old and Untenable?
The Predictive Model: Its Reliability and Validity
Life-Time Value Modeling of Big-ticket Items
Validating the Logistic Regression Model: Try Bootstrapping
Regression Modeling Involves Art, Science, and Poetry Too
Re-Data-Mining Your Constantly-updated Database: A Criterion for Doing So
What Criteria Do You Use to Build a Model that Maximizes the Cum Lift?
What Criteria Do You Use to Determine the Best Model?
Top Five Statistical Modeling Problems: Nonissues for the Machine-learning GenIQ Model
Statistical vs. Machine-Learning Data Mining
CHAID-based Data Mining for Paired-Variable Assessment
The Missing Statistic in the Decile Table: The Confidence Interval
The Importance of Straight Data: Simplicity and Desirability for Good Model Building Practice
The Paradox of Overfitting
Building a Database Model to Assess the True Impact of a Test Campaign
To Fit or Not to Fit Data to a Model
Assessing the Predictiveness of a Classification Model: Traditional vs. Modern Methods
Two-by-Two Classification and Decile Tables - A Comparison
Genetic vs. Statistic Regression Models - A Comparison
Your Customers are Talking: Are You Listening?
Is Not a Response-Model Tree a Response-Model Tree by Any Other Name?
Interpretation of Coefficient-free Models
Social Network Analysis, Social Media Data, and Text Mining to Boost Business Intelligence
Predictive Modeling Using Real-time Data
Data Mining Quiz - II
Data Mining Quiz
How Large a Sample is Required to Build a Database Response Model?
CHAID: Nine Inventive, Utile Applications Beyond Its Original Intent
Response-Approval Model: An Effective Approach for Implementation
Data Mining: Illustration of the Pythagorean Theorem
Stepwise is a Problematic Method for Variable Selection in Regression: Alternative Methods are Available
What If There Were No Significance Testing?
A Simple Method for Assessing Linear Trend and Seasonality Components in Database Models
Variable Selection Methods in Regression: Ignorable Problem, Outing Notable Solution
A New CRM Method for Identifying High-value Responders
Predicting the Quality of Your Statistical Regression Models
Confusion Matrix: Perhaps Confusing, but Definitely Biased
What is the GenIQ Model?
Linear Probability, Logit, and Probit Models: How Do They Differ?
When Data Are Too Large to Handle in the Memory of Your Computer
A New Method of Modeling Missing Data: Deliverance of Discarded, Incomplete Cases
Predicting Share of Wallet without Survey Data
Variable Selection Methods in Regression: Many Statisticians Know Them, But Few Know They Produce Poorly Performing Models
Statistical Modelers and Data Miners: Variable Selection, Data Mining Paradigm, Optimal Decile Table, and more ...
The GenIQ Model:
Data-defined, Data Mining, Variable Selection, and Decile Optimization
Data Mining: An Ill-defined Concept
GenIQ: A Visual Introduction
Overfitting: Old Problem, New Solution
Genetic Data Mining: The Correlation Coefficient
Data Cleaning is Not Completed Until the “Noise” is Eliminated
How to Make the Best Credit Score Even Better
Multivariate Regression Trees: An Alternative Method
"Grand" words (1000) about the GenIQ Model
Historical Notes on the Two Most Popular Prediction Models, and One Not-yet Popular Model
Real World Data are Dirty: Data Cleaning and the "Noise" Problem
GenIQ: For Modelers Who Daringly Consider a Different Model –
The Most Compelling Illustration of the GenIQ Model
A Most Compelling Illustration of the GenIQ Model
GenIQ Lets the Data Specify the Model
Data Mining Using Genetic Programming
GenIQ-enhanced Regression Model
GenIQ-enhanced/Data-reused Regression
GenIQ: Nonlinear Curve Fitter
GenIQ: OLS Curve Fitter
A Method for Moderating Outliers, Instead of Discarding Them
Building Statistical Regression Models: Straight Data are Necessary
Logistic Regression versus Machine Learning Regression
Ordinary Regression versus Machine Learning Regression
The GenIQ Model: FAQs
Interpreting Model Performance: Use the “Smart” Decile Analysis
Predictor Variable Importance: Multicollinearity is Not a Problem for a Genetic Regression Model
Dummy Variables: The Problem and Its Solution
Finding the Best Variables for Database Marketing Models
Decile Analysis Primer: Cum Lift for Response Model
Maximizing the Lift in Database Marketing
When Statistical Model Performance is Poor: Try Something New, and Try It Again
A Hybrid Statistics-Machine Learning Paradigm for Database Response Modeling
Tukey's Bulging Rule: Why Use It, and What to Do When It Fails
Tukey's Bulging Rule for Straightening Data
Modeling a Skewed Distribution with Many Zero Values
A New Jackknife Method: 3-in-1 Tool for Variable Selection, Data Mining and Model Building
A Genetic Model to Identify Titanic Survivors
Statistics versus Machine Learning: A Significant Difference for Database Response Modeling
The Genetic Programming Engine that Does: Data Specify the Model, Not Fit Data to a Model
GenIQ-Parkinson's Law: The GenIQ Model Expands to Fill the Time Available for Model Completion
Genetic Data Mining Method for the Proper Use of the Correlation Coefficient
A Genetic Imputation Method for Database Modeling
Missing Value Analysis: A Machine-learning Approach
A Genetic Logistic Regression Model: A Model-free Approach to Identifying Responders to a CRM Solicitation
Predictive Analytics Now Accessible to Excel Spreadsheet Users: GenIQ Model Software with an Excel Toolbar
An Alternative Response Model
Analysis and Modeling for Today's Data
Using the GenIQ Model to Insure the Validation of a Model is Unbiased
Gain of a Predictive Information Advantage: Data Mining via Evolution
Response-Approval Model: An Effective Approach for Implementation
Marketing Optimization Model: A Genetic Approach
Binary Logistic Regression: A Model-free Approach
Ordinal Logistic Regression: A Model-free Approach
Multinomial Logistic Regression: A Model-free Approach
Quantile Regression: Model-free Approach
Rethink The Regression Model: Think GenIQ Model
Building a CRM Model for Identifying Profitable Leads: The Genetic Contact-Profit Model
A New Method of Decile Analysis Optimization for Database Models
Multiple Catalog Mail Campaigns: Who Gets Mailed Next, and Which Catalog Should It Be?
Building and Solving Response Optimization Models with the GenIQ Model
Gaining Insights from Your Data: A Neoteric Machine Learning Method
Data Mining for the Desktop
Radically Distinctive Without Equal Predictive Model
Extracting Nonlinear Dependencies: An Easy, Automatic Method
Retail Revenue Optimization: Accounting for Profit-eating Markdowns
Handling Qualitative Attributes: Upgrading Discrete Heritable Information
6) Solutions
Social Marketing Intelligence for Sweeping Improvement in Marketing Campaigns
Model Selection for Credit Card Profitable Approval
Your Customers are Talking: Are You Listening?
Controlling Credit Risk: Building a Not-Yet Popular Forecasting Model
Improve Marketing ROI: Predictive Analytics Using Real-time Data
A Customer Intelligence Model: A New Approach to Gain Customer Insight
Marketing Optimization: Regression-tree Approach for Outbound Campaigns
Identifying Your Best Customers: Descriptive, Predictive and Look-Alike Profiling
Latent Class Analysis and Modeling: A Pharmaceutical Case Study
Subprime Lender Short Term Loan Models for Credit Default and Exposure
Credit Risk Modeling – A Machine Learning Approach
Finding Tax Cheaters Easily
CRM Success with Data Mining
Retail Revenue Optimization: Accounting for Profit-eating Markdowns
Nonprofit Modeling: Remaining Competitive and Successful
Detecting Fraudulent Insurance Claims: A Machine Learning Approach
Demand Forecasting for Retail: A Genetic Approach
CRM: Cross-Sell and Up-Sell to Improve Response Rates and Increase Revenue
Performance Management: Improve It via Machine Learning
Risk Management for the Insurance Industry: A Machine Learning Approach
Credit Scoring: A New Approach to Control Risk
Customer-Value Based Segmentation: An Overview
Trigger Marketing: Predicting the Next Best Offer to Give Customers
Marketing Mix Model: Right Offer, Right Time, and Right Channel
Building a CRM Model for Identifying Profitable Leads: The Genetic Contact-Profit Model
A Machine Learning Approach to Conjoint Analysis
Subprime Borrower Market: Building a Subprime Lender Scoring Model for a Homogeneous Segment
The Financial Services Problem-Solution: Reduce Costs, Increase Profits by Data Mining and Modeling
Retail Revenue Optimization: A Model-free Approach
Fraud Detection: Beyond the Rules-Based Approach
Product Positioning: Predicting the Next Best Offer to Give Customers
Marketing Mix Model: A Genetic Approach
Optimizing Customer Loyalty
Telecommunication Fraud Reduction: Analytical Approaches
The Banking Industry Problem-Solution: Reduce Costs, Increase Profits by Data Mining and Modeling
Fundraising Modeling: Competitive and Successful
7) Reference Articles
Statisticians Have a Bad Habit
Power of Thought
Accidental Statistician: Who Can Befitted of a Self-described Caption?
A Dozen Statisticians, A Dozen Outcomes
A Popular Statistical Term Coined with the Formula X's Y
"Few things are harder to put up with than the annoyance of a good (statistics) example"
Survival of the Fittest: Who Coined It, and When?
How Does Spearman's Coefficient Relate to Pearson's Coefficient?
Calculating the Average Correlation Coefficient: Why?
What If There Were No Significance Testing?
Predicting the Quality of Your Statistical Regression Models
Pop Quiz on Pi
Linear Probability, Logit, and Probit Models: How Do They Differ?
How To Bootstrap
The Correlation Coefficient: Definition
Genetic Data Mining Method for the Proper Use of the Correlation Coefficient
Logistic Regression: Definition
CHAID: Its Original Intent
CHAID for Uncovering Relationships: A Data Mining Tool
Market Segmentation: Defining Target Markets with CHAID
The Working Concepts for Building a Database Acquisition Model
The Working Concepts for Building a Database Retention Model
The Working Concepts for Building a Database Attrition Model
Optimizing Website Content via the Taguchi Method
Sensitivity Analysis for Database Marketing Models
Creating a SAS8 Dataset from a SAS9 Dataset
A Very Automatic Coding of Dummy Variables
Einstein: A Clever, Self-taught Statistician
Data Mining Paradigm: Historical Perspective
Data Mining: An Ill-defined Concept
Pythagoras: Everyone Knows His Famous Theorem, but Not Who Discovered It One Thousand Years before Him
Karl Pearson: Everybody Knows His Correlation Coefficient, but Not How “Close” the Binomial Distribution is to a Normal Distribution
Florence Nightingale: You Know Her as the Pioneer of Modern Nursing, But as a Passionate Statistician!
Statistical Terms: Who Coined Them, and When?
Historical Notes on the Two Most Popular Prediction Models, and One Not-yet Popular Model
Different Data, Identical Regression Models: Which Model is Better?
The Importance of Straight Data: For Simplicity, Desirable for Good Modeling
The Correlation Coefficient: Its Values Range Between Plus/Minus 1, or Do They?
A Trilogy of “Item” Biographies of Our Favorite Statisticians
HELP! I Need Somebody, Not Just Anybody ...
Do-It-Yourself Method for Finding the Square Root of 2
Given an Irrational Number, are the Digits after the Decimal Point Random?
Given the Irrational Number Pi, are the Digits after the Decimal Point Random?
What is the Probability of a Miracle?
Confusion Matrix: Perhaps Confusing, but Definitely Biased
Handling Qualitative Attributes: Upgrading Discrete Heritable Information
8) Useful SAS Code
SAS Code for Performance of Model vs. Chance Model
SAS Code for K-Means Clustering
SAS Code for Changing Prefix of Variable Names
SAS Code for Determining Number of Variables in a Dataset
SAS Code for Bootstrapped Decile Analysis
SAS Code for Normalizing Variable to Lie Within [0, 1]
SAS Code for Direction of Correlates of Varclus
SAS Code for Smoothplot
SAS Code for Finding Mid-Spread of Two Variables
SAS RENAME Coding
SAS Code for WHERE Statement
SAS Code for Removing All Variable Labels
SAS Code for Proc Corr with WITH-Variable, Output Vertical
SAS Code for Calculation of Average Correlation Among Variables
Splitting a Dataset Between Numeric and Character Variables
SAS Code for Finding Frequent Variables Across Files
SAS Code for Listing Predictor Variables in a Title
SAS Import Wizard Needs a Little Magic
SAS Code for INPUT and PUT Functions
Decile Analysis - the Basic
Decile Analysis of X1 and X2 Based on Model Estimate
Decile Analysis - Sales
SAS Code for Running Medians of Three
SAS Code for Creating a Trend Dataset
SAS Code for Appending a Calculated Value
SAS Code for Ranking Predictors
SAS Code for Proc Tabulate - basic
SAS Code for Converting a Num to Char Variable, and Back
SAS Code for Reshaping 3x5 Dataset into 5x3 Dataset
SAS Code for Renaming A Variable's Case
SAS Code for Dots to Zeros
SAS Code for Basic ODS
Scoring A Principal Component
Scoring An Oblique Principal Component
Scoring and Appending the Assigned-Cluster from An Oblique Principal Component
Creating a Variable List of Big Data
Calculating a Weight Variable for the Number of Repeated Values of a Variable
Creating Dummy Variables Corresponding to Values of Character Variables
Creating Count Variables Corresponding to Values of Character Variables
Creating Time-On-File Variable
Creating a Numeric Date (mmddyy) to a SAS Date
Calculating the Average Correlation Coefficient: Why?
Creating a Bootstrap Sample
A Very Automatic Coding of Dummy Variables
Collapsing Multiple Observations into a Single Observation
Spreading Mutliple (Monthly) Observations into a Single Observation
Spreading and Summing Multiple (Monthly) Obserations into a Single Observation
9)Third Edition
1.
Chapter 8 - Market Share Estimation: Data Mining for an Exceptional Case
2.
Chapter 11 - Predicting Share of Wallet without Survey Data
3.
Chapter 19 - Market Segmentation Based on Time-Series Data Using Latent Class Analysis
4.
Chapter 20 - Market Segmentation: An Easy Way to Understand the Segments
5.
Chapter 21 - The Statistical Regression Model: An Easy Way to Understand the Model
6.
Chapter 23 - Model Building with Big Complete and Incomplete Data
7.
Chapter 27 - Decile Analysis: Perspective and Performance
8.
Chapter 28 - Net T-C Lift Model: Assessing the Net Effects of Test and Control Campaigns
9.
Chapter 31 - Visualization of Marketing Models: Data Mining to Uncover Innards of a Model
10.
Chapter 34 - Opening the Dataset: A Twelve-Step Program for Dataholics
11.
Chapter 43 - Text Mining: Primer, Illustration, and TXTDM Software
12.
Chapter 44 - Some of My Favorite Statistical Subroutines
For more information about these articles, call Bruce Ratner at 516.791.3544 or 1 800 DM STAT-1; or e-mail at
br@dmstat1.com
.
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DM STAT-1 CONSULTING
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