Statistical Data Mining Using SAS Applications

ISBN-13: 9781439810750 / Angielski / Twarda / 2010 / 477 str.

George Fernandez

Opis

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."

Most books on data mining focus on principles and furnish few instructions on how to carry out a data mining project. Data Mining Using SAS Applications not only introduces the key concepts but also enables readers to understand and successfully apply data mining methods using powerful yet user-friendly SAS macro-call files. These methods stress the use of visualization to thoroughly study the structure of data and check the validity of statistical models fitted to data.

Learn how to convert PC databases to SAS data

Discover sampling techniques to create training and validation samples

Understand frequency data analysis for categorical data

Explore supervised and unsupervised learning

Master exploratory graphical techniques

Acquire model validation techniques in regression and classification

The text furnishes 13 easy-to-use SAS data mining macros designed to work with the standard SAS modules. No additional modules or previous experience in SAS programming is required. The author shows how to perform complete predictive modeling, including data exploration, model fitting, assumption checks, validation, and scoring new data, on SAS datasets in less than ten minutes!

Spis treści

Data Mining: A Gentle Introduction. Preparing Data for Data Mining. Exploratory Data Analysis. Unsupervised Learning Methods. Supervised Learning Methods: Prediction. Supervised Learning Methods: Classification. Advanced Analytics and Other SAS Data Mining Resources. Appendices. Index.

O autorze

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.