Becoming a Reviewer
Become a technical reviewer and help shape new SAS Press books. Your insight and knowledge will contribute to the process of creating cutting-edge resources for customers around the world. Plus, you'll get a firsthand look at the work of thought leaders and expert SAS users – while earning credit toward purchasing SAS books. Here's how to get started:
- Select the book you're interested in reviewing from the list below and complete all the information on the form.
- After you submit the form, your name will be forwarded to an editor. The editor will review your application and contact you if there is an opportunity for you to review the book.
- by Ruth Hummel, Elizabeth A. Claassen, and Russell D. WolfingerThis book will use the visual features and the other unique features of JMP to appeal to a broader data analytics audience than the SAS programming audience of SAS for Mixed Models, covering the topic of mixed models for the first time in a full JMP book. This book will be a comprehensive introduction to and reference manual on and include new features of the software. Topics will include analysis of simple, intermediate and complex experiments using mixed model methods in JMP, specifically DOE, single and multiple random effect models, repeated measures and spatial models, random coefficient models, and power analysis.
- By Alun Lloyd Brain and Sameer RahmanThis book is a practical guide designed to walk readers through the different steps of developing and deploying a predictive model in a direct marketing environment. This book will contain information on creating the models in both SAS and R, before focusing on how to present and deploy them to get the best business outcome.
- By Robert RodriguezThis book will give readers a high-level awareness and understanding of newer regression modeling procedures in SAS that are valuable for supervised machine learning, predictive analytics, and statistical modeling. This goal of this short book is to explain the relative benefits of these procedures, introduce the procedures with basic examples, and help users navigate to procedures and methods that meet their needs. The audience for this book includes newcomers to SAS—in particular, data scientists—who might have encountered some of the methods in open source software and are unaware of what SAS offers. The audience also includes longtime SAS users who are familiar with the REG procedure but have not kept up with the availability of newer and more effective procedures in SAS/STAT and SAS Viya.
- By John WardAs a new SAS Viya administrator, it can be a bit overwhelming knowing where to start in making sure the SAS Viya system is setup as best as it can be and making sure it runs at peak performance. This book will provide a starting point on configuration settings to be aware of and system monitoring techniques to help the SAS system administrator.
- By Richard and Adele CutlerThis book is aimed at university students taking a second course in statistics with an emphasis on regression methods, and at SAS practitioners in business, industry, and many other disciplines who wish to know more about modern, machine learning regression methods. For some this book will be an introduction to regression methods, but others will be familiar with linear regression such as is implemented in PROC REG, and this book will provide information on modern methods including the LASSO and elastic net for regularization, quantile regression, and non‐linear semi‐parametric and non‐parametric regression methods such as generalized additive models, regression trees, and random forests. While the format of the book is that of a textbook, a SAS user with experience doing traditional regression will be able to immediately turn to chapters on machine learning methods, see a full discussion of the methods, and examples in SAS illustrating their use.
- By Kriss Harris and Richann WatsonThe book primarily shows users how to use GTL to create figures that are needed to support Clinical Drug Development.
- By Randy CollicaThis book is about is about one of the most popular analytic methods that is used by all industries, and that is clustering and segmentation. This book will endeavor to use the latest and greatest methods available that shows the power of SAS Viya while also solving typical industry issues. It will also cover how to combine segments into one segmentation while retaining the information content of the input segments – ensemble segmentation with business applications. The main book’s subject is on the use of clustering and ML methods for the purpose of segmenting customer or client data into useful categories for marketing, market research, next best offers by segment, etc.