Building Better Models with JMP Pro

Building Better Models with JMP® Pro provides an example-based introduction to business analytics, with a proven process that guides you in the application of modeling tools and concepts. It gives you the "what, why, and how" of using JMP® Pro for building and applying analytic models. This book is designed for business analysts, managers, and practitioners who may not have a solid statistical background, but need to be able to readily apply analytic methods to solve business problems. In addition, this book will greatly benefit faculty members who teach any of the following subjects at the lower to upper graduate level: predictive modeling, data mining, and business analytics. Novice to advanced users in business statistics, business analytics, and predictive modeling will find that it provides a peek inside the black box of algorithms and the methods used. Topics include: regression, logistic regression, classification and regression trees, neural networks, model cross-validation, model comparison and selection, and data reduction techniques. Full of rich examples, Building Better Models with JMP® Pro is an applied book on business analytics and modeling that introduces a simple methodology for managing and executing analytics projects. No prior experience with JMP® is needed. Make more informed decisions from your data using this newest JMP® book.

Jim Grayson, PhD, is a Professor of Management Science and Operations Management in the Hull College of Business Administration at Georgia Regents University. He currently teaches undergraduate and MBA courses in operations management and business analytics. Previously, Jim held managerial positions at Texas Instruments in quality and reliability assurance, supplier and subcontractor management, and software quality. He has a PhD in management science with an information systems minor from the University of North Texas, an MBA in marketing from the University of North Texas, and a BS from the United States Military Academy at West Point.

Sam Gardner is a Senior Research Scientist at Elanco, a business division of Eli Lilly and Company. He currently works as a statistician supporting manufacturing development for a variety of animal health products. He is recognized by the American Statistical Association as an Accredited Professional Statistician. He has an MS in mathematics from Creighton University and an MS in statistics from the University of Kentucky. He graduated from Purdue University with BS degrees in mathematics and chemistry. Gardner started his professional career as a military officer in the US Air Force, where he had roles that focused on modeling and simulation, operational flight test planning and analysis, and research and development. He also taught statistics at the Air Force Institute of Technology. After leaving the military, he began his work in the pharmaceutical industry as a statistician supporting the development and manufacturing of active pharmaceutical ingredients, and later transitioned to a role as a chemist in pharmaceutical manufacturing. He also worked as a statistician in pharmaceutical marketing with a focus on using statistical modeling to help solve business problems related to sales and marketing effectiveness. An avid user of statistical software, he spent several years working for JMP, a division of SAS, where he worked as a product expert and seminar speaker, with a focus on data visualization, applied statistics, and modern statistical modeling.

Mia L. Stephens is an Academic Ambassador with JMP, a division of SAS. Prior to joining SAS, she split her time between industrial consulting and teaching statistics at the University of New Hampshire. A coauthor of Visual Six Sigma: Making Data Analysis Lean and JMP Start Statistics: A Guide to Statistics and Data Analysis Using JMP, Fifth Edition, she has developed courses and training materials, taught, and consulted within a variety of manufacturing and service industries. Stephens holds an MS in statistics from the University of New Hampshire and is located in York Harbor, Maine.