Includes information about mixed effects models, applications of the MIXED procedure, regression diagnostics with the corresponding IML procedure code, and covariance structures. Together with Multivariate Data Reduction and Discrimination with SAS Software, provides comprehensive information about using SAS for multivariate statistics.

Provides a discussion of categorical data analysis techniques as well as an invaluable aid in applying these methods with SAS.

In this instructive guide, Randy Collica employs SAS Enterprise Miner and the most commonly available techniques for customer relationship management (CRM).

Combine complex concepts facing the financial sector with the software toolsets available to analysts.

An expanded and enhanced resource which provides the most comprehensive treatment of decision tree theory, use, and applications available in one easy-to-access place. Users will be able to make recommendations from decision trees and search algorithms in order to improve and customize their segmentation of the market.

An invaluable resource for all researchers interested in adding fixed effects regression methods to their tool kit of statistical techniques.

With the new emphasis on business intelligence, business analytics and predictive analytics, Fundamentals of Predictive Analytics with JMP is invaluable to everyone who needs to expand their knowledge of statistics and apply real problem-solving analysis.

This book both explains the theory behind logistic regression and looks at all the practical details involved in its implementation using SAS; the second edition covers many new features of PROC LOGISTIC, as well as PROC SURVEYLOGISTIC, PROC GLIMMIX, PROC QLIM, and PROC MDC.

Ideal for learning multiple comparisons procedures in standard linear models, multivariate analysis, categorical analysis, and regression and nonparametric statistics.
Exercises available.

This book provides a friendly methodology-based introduction to the ubiquitous phenomenon of overdispersion and features examples--many of which use the GLIMMIX, GENMOD, and NLMIXED procedures--covering a variety of fields of application.

Explains and demonstrates through examples how you can use SAS for time series analysis. It offers modern procedures for forecasting, seasonal adjustments, and decomposition of time series that can be used without involved statistical reasoning.

This title addresses data collection and data cleaning, decision trees and regression trees, logistic regression models, neural networks, variable selection and variable transformation, and more.

From designing database tables to tuning for performance and efficiency, this second edition delves into the workings of PROC SQL with greater analysis and discussion. Includes unique application-oriented code along with real-world examples of a powerful relational database interface language.

A graduate-level book that focuses on the general linear model (GLM) theory, stated in matrix terms, which provides a more compact, clear, and unified presentation of regression and ANOVA than do traditional sums of squares and scalar equations.
Exercises included.

A tutorial guide that demonstrates how SAS performs univariate and multivariate time series analysis.

Discover the latest capabilities available for a variety of applications featuring the MIXED, GLIMMIX, and NLMIXED procedures. Completely revised and updated for SAS 9.

Learn how to perform analyses to identify customers and make informed marketing investments. This book answers core questions on customer relationship management (CRM), provides an overall framework for thinking about CRM, and offers real-world examples across a variety of industries.

A how-to book for statistical programmers who use SAS software and who want to simulate data efficiently.

Provides comprehensive description of the SAS/IML software and how to use it. Presents tips and techniques, with numerous code snippets and more than 100 graphs.

An easy-to-read and comprehensive guide for biomedical and social science researchers who want to analyze survival data with SAS.