Using SAS Enterprise Miner, this example-driven guide illustrates the application and operation of decision trees in data mining, business intelligence, business analytics, prediction, and knowledge discovery. It explains in detail the use of decision trees as a data mining technique and how this technique complements and supplements other business intelligence applications such as OLAP cubes; in addition, it explains other data mining approaches, like regression, cluster analysis, and neural networks. The book contains examples that illustrate how various aspects of decision trees are constructed, how they operate, how to interpret them, and how to use them in a range of predictive and descriptive applications. Many of the examples are drawn from purchase behavior, risk assessment, and business-to-business marketing.

The target audience for this book includes analysts who already have an introductory understanding of data mining and who want to benefit from a more advanced, in-depth look at the theory and methods of a decision-tree approach to data mining and business intelligence.

This essential book provides a general introduction to data mining and is filled with a variety of examples. While there are no "right" answers to investigating data sets, there are many questions that can be asked of the data. Each answer creates a path that will help you drill down to explore the data in more detail. It is up to the investigator to determine what is of interest and what is important to analyze, and this book provides excellent starting points.

Explore two decades of proven SAS tips. Each tip is presented in a self-contained format that is both informative and easy to use. Every chapter contains a collection of related tips with frequent use of code and log examples to improve productivity. This dynamic collection of helpful tips is updated for SAS 8 & SAS®9, keeping the best of the previous edition and adding many new tips. This book focuses on Foundation SAS software and is applicable to all industry sectors. Tips are also included for other SAS products such as SAS/GRAPH software, as well as specific procedures, the DATA step, SQL, macros, functions, ODS, and much more. You'll gain a world of experience through Phil Mason's tips and techniques.

Updated for SAS 9, this task-oriented book will guide you step-by-step through the fundamentals of the SAS macro facility. Included are a generous amount of illustrations, examples, and tips. With this book you will handily learn the advantages of macro programming, elements of the macro facility (macro variables, macro programs, macro language), how to write a macro program, macro programming techniques, tips on using the macro facility, how the macro facility fits into SAS, and interfaces between the macro facility and other components of SAS. Beginning macro programmers will learn to write SAS macro programs quickly and efficiently. More experienced macro programmers will find this book useful to refresh their conceptual knowledge and expand on their macro programming skills. The book assumes some SAS programming experience, including knowledge of how to write a DATA step and how to use SAS procedures.

Written for anyone involved in the data preparation process for analytics, this user-friendly text offers practical advice in the form of SAS coding tips and tricks, along with providing the reader with a conceptual background on data structures and considerations from the business point of view. Topics addressed include viewing analytic data preparation in the light of its business environment, identifying the specifics of predictive modeling for data mart creation, understanding the concepts and considerations for data preparation for time series analysis, using various SAS procedures and SAS Enterprise Miner for scoring, creating meaningful derived variables for all data mart types, using powerful SAS macros to make changes among the various data mart structures, and more!

Pharmaceutical Statistics will consist of 13 chapters motivated by data analysis problems arising at various stages of drug development: early drug discovery, animal toxicology, clinical pharmacology and other early human studies and, finally, clinical trials. This book will provide a lot of tutorial material and SAS examples to help professionals new to a certain area of drug development quickly understand and learn popular data analysis methods and apply them to real-life problems. The main goal of the book is to introduce a wide range of data analysis problems encountered in drug development and explain how to solve them in a real-time fashion, that is, describe relevant methodology, illustrate it using real-life examples, and provide SAS code for solving the problem. It will cover a broad range of topics including data analysis problems in drug discovery, toxicology, and pharmacology studies. It will address problems concerning the design and analysis of

• chemical experiments in drug discovery
• animal studies to assess the toxicological profile of chemical compounds
• clinical pharmacology studies to examine the properties of new drugs in healthy human subjects
• Phase II and Phase III clinical trials to establish therapeutic benefits of experimental drugs.