SAS® Upcoming Titles

Business Forecasting: The Emerging Role of Artificial Intelligence and Machine Learning

By Michael Gilliland, Udo Sglavo, and Len Tashman
Anticipated publication date: Second quarter 2021

This book provides ideas from the most important and influential authors in the field of forecasting on an array of topics that are highly relevant. It provides multiple perspectives on relevant issues like monitoring forecast performance, forecasting process, communication and accountability for the forecast, the use of big data in forecasting, and the role of AI/ML in enhancing traditional time series forecasting methods. Note: Content is mostly material previously published in "practitioner" journals (Foresight and Journal of Business Forecasting), with a few articles from the academic International Journal of Forecasting. Some articles report on academic research, or include case studies, but most are thoughtful discussion of important business forecasting topics, such as the role of the sales force in forecasting, or the value of judgmental overrides to a statistical forecast, or how to determine what forecast error is "avoidable." Articles were chosen for their importance, influence, informativeness, and for being provocative -- leading the reader to new considerations and ideas.

A Gentle Introduction to Statistics Using SAS® Studio in the Cloud

By Ron Cody
Anticipated publication date: Second quarter 2021

A Gentle Introduction to Statistics Using SAS® Studio in the Cloud introduces crucial statistical concepts for those who are new to the field. Because this book uses SAS OnDemand for Academics, little programming is required, so readers can focus on learning a wide variety of statistical concepts, from sample size and study designs, through ANOVA, correlation and regressions. Readers will be walked through importing data and SAS Studio tasks and graphing. Each chapter will end with programming exercises with solutions to the odd-numbered exercises provided online to download.

Introduction to Statistical and Machine Learning Methods for Data Science

By Carlos Andre Reis Pinheiro and Mike Patetta
Anticipated publication date:  Second quarter 2021

This book provides a comprehensive overview of the statistical and machine learning techniques associated with data science initiatives. You will learn the most important techniques and methods related to data science and when to apply them for different business problems.

JMP® for Mixed Models

By Ruth M. Hummel, Elizabeth A. Claassen, and Russell D. Wolfinger
Anticipated publication date: Second quarter 2021

JMP® for Mixed Models is a comprehensive introduction to and reference manual on the use of mixed models with JMP software. The topics covered are 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. Mixed models are essential for researchers and data analysts, and JMP is the ideal visual and intuitive tool, with a unique approach to graphical statistics. 

Segmentation Analytics Using SAS® Viya®: A Practical Approach to Clustering and Visualization for Segmentation

By Randall S. Collica
Anticipated publication date: Second quarter 2021

Segmentation Analytics Using SAS® Viya®: A Practical Approach to Clustering and Visualization for Segmentation demonstrates 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.  This book will highlight the latest and greatest methods available that show the power of SAS Viya while also solving typical industry issues. This book will provide readers with practical methods of using SAS VDMML Model Studio and coding in SAS Studio for segmentation model development, monitoring, and profiling of segments. Understanding how customers behave is a primary objective of most organizations, and segmentation is a key analytic method for achieving that objective. 

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