Fernandez Statistical Data Mining Using SAS Applications, Second Edition

2. Auflage 2010
ISBN: 978-1-4398-1076-7
Verlag: Taylor & Francis
Format: PDF
Kopierschutz: Adobe DRM (»Systemvoraussetzungen)

E-Book, Englisch, 477 Seiten

Reihe: Chapman & Hall/CRC Data Mining and Knowledge Discovery Series

Statistical Data Mining Using SAS Applications, Second Edition describes statistical data mining concepts and demonstrates the features of user-friendly data mining SAS tools. Integrating the statistical and graphical analysis tools available in SAS systems, the book provides complete statistical data mining solutions without writing SAS program codes or using the point-and-click approach. Each chapter emphasizes step-by-step instructions for using SAS macros and interpreting the results. Compiled data mining SAS macro files are available for download on the author’s website. By following the step-by-step instructions and downloading the SAS macros, analysts can perform complete data mining analysis fast and effectively.
New to the Second Edition—General Features

- Access to SAS macros directly from desktop

- Compatible with SAS version 9, SAS Enterprise Guide, and SAS Learning Edition

- Reorganization of all help files to an appendix

- Ability to create publication quality graphics

- Macro-call error check

New Features in These SAS-Specific Macro Applications

- Converting PC data files to SAS data (EXLSAS2 macro)

- Randomly splitting data (RANSPLIT2)

- Frequency analysis (FREQ2)

- Univariate analysis (UNIVAR2)

- PCA and factor analysis (FACTOR2)

- Multiple linear regressions (REGDIAG2)

- Logistic regression (LOGIST2)

- CHAID analysis (CHAID2)

Requiring no experience with SAS programming, this resource supplies instructions and tools for quickly performing exploratory statistical methods, regression analysis, logistic regression multivariate methods, and classification analysis. It presents an accessible, SAS macro-oriented approach while offering comprehensive data mining solutions.

Zielgruppe

Data analysts, SAS users, and graduate students in data mining, computer science, business, medical research, and the social sciences.

Autoren/Hrsg.

Fernandez, George

Fachgebiete

• Mathematik | Informatik Mathematik Stochastik Mathematische Statistik
• Mathematik | Informatik Mathematik Stochastik Wahrscheinlichkeitsrechnung
• Mathematik | Informatik EDV | Informatik Daten / Datenbanken

Inhaltsverzeichnis

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.

Über Autor(innen)

George Fernandez is a professor of applied statistical methods and the director of the Center for Research Design and Analysis at the University of Nevada in Reno.