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Data defines the model by dint of genetic programming, producing the best decile table. GenIQ Model Related Articles Features, Book, Analytics, Solutions, and References Live chat by Boldchat If you would like to be notified when new articles are added, please click here. Click any or all eight interesting sections with engaging topics, below:       1) Features,           2) Extra-GenIQ Applications,                 3) Book,                      4) Webcast,                           5) Analytics,                                 6) Solutions,                                     7) Reference Articles,                                          8) Useful SAS Code. 1) Features GenIQ: A Visual Introduction Value-added Benefits 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   3) Book   Statistical Modeling and Analysis for Database Marketing: Effective Techniques for Mining Big Data (click title) - Bruce Ratner, Ph.D. 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  Genetic vs. Statistic Regression - 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 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 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 Calculating the Average Correlation Coefficient Creating a Bootstrap Sample A Very Automatic Coding of Dummy Variables Collapsing Multiple Observations into a Single Observation Collapsing Mutliple (Monthly) Observations into a Single Observation 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. 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