Statistical data mining using SAS applications / George Fernandez.

First published under title: Data mining using SAS applications.

Includes bibliographical references and index.

Data Mining: A Gentle Introduction Introduction Data Mining: Why It Is Successful in the IT World Benefits of Data Mining Data Mining: Users Data Mining: Tools Data Mining: Steps Problems in the Data Mining Process SAS Software the Leader in Data Mining Introduction of User-Friendly SAS Macros for Statistical Data Mining Preparing Data for Data Mining Introduction Data Requirements in Data Mining Ideal Structures of Data for Data Mining Understanding the Measurement Scale of Variables Entire Database or Representative Sample Sampling for Data Mining User-Friendly SAS Applications Used in Data Preparation Exploratory Data Analysis Introduction Exploring Continuous Variables Data Exploration: Categorical Variable SAS Macro Applications Used in Data Exploration Unsupervised Learning Methods Introduction Applications of Unsupervised Learning Methods Principal Component Analysis (PCA) Exploratory Factor Analysis (EFA) Disjoint Cluster Analysis (DCA) Biplot Display of PCA, EFA, and DCA Results PCA and EFA Using SAS Macro FACTOR2 Disjoint Cluster Analysis Using SAS Macro DISJCLS2 Supervised Learning Methods: Prediction Introduction Applications of Supervised Predictive Methods Multiple Linear Regression Modeling Binary Logistic Regression Modeling Ordinal Logistic Regression Survey Logistic Regression Multiple Linear Regression Using SAS Macro REGDIAG2 Lift Chart Using SAS Macro LIFT2 Scoring New Regression Data Using the SAS Macro RSCORE2 Logistic Regression Using SAS Macro LOGIST2 Scoring New Logistic Regression Data Using the SAS Macro LSCORE2 Case Study 1: Modeling Multiple Linear Regressions Case Study 2: If-Then Analysis and Lift Charts Case Study 3: Modeling Multiple Linear Regression with Categorical Variables Case Study 4: Modeling Binary Logistic Regression Case Study 5: Modeling Binary Multiple Logistic Regression Case Study 6: Modeling Ordinal Multiple Logistic Regression Supervised Learning Methods: Classification Introduction Discriminant Analysis Stepwise Discriminant Analysis Canonical Discriminant Analysis Discriminant Function Analysis Applications of Discriminant Analysis Classification Tree Based on CHAID Applications of CHAID Discriminant Analysis Using SAS Macro DISCRIM2 Decision Tree Using SAS Macro CHAID2 Case Study 1: Canonical Discriminant Analysis and Parametric Discriminant Function Analysis Case Study 2: Nonparametric Discriminant Function Analysis Case Study 3: Classification Tree Using CHAID Advanced Analytics and Other SAS Data Mining Resources Introduction Artificial Neural Network Methods Market Basket Analysis SAS Software: The Leader in Data Mining Appendix I: Instruction for Using the SAS Macros Appendix II: Data Mining SAS Macro Help Files Appendix III: Instruction for Using the SAS Macros with Enterprise Guide Code Window Index A Summary and References appear at the end of each chapter.

Compatible with SAS version 9, SAS Enterprise Guide, and SAS Learning Edition, this resource describes statistical data mining concepts and methods and includes 13 user-friendly SAS macro applications for performing complete data mining tasks. Each chapter emphasizes step-by-step instructions for using SAS macros and interpreting the results.