Predictive Analytics for Business using R by Russell R Barton equips business professionals and students with the tools to forecast trends, classify outcomes, and analyze time-dependent data using R programming. Focused on real-world business applications, the book covers essential topics such as regression analysis, classification models, time series forecasting, and simulation. It emphasizes how to assess uncertainty and interpret model results with confidence. Designed for case-based learning, each chapter is paired with real data and accompanying R code — making it ideal for hands-on practice in academic or professional settings. Whether you're analyzing customer behavior, forecasting sales, or modeling system performance, this book offers the analytical foundation to make smarter, data-driven decisions. "A timely, comprehensive, and innovative monograph on a key topic in today's research and business activities... a cornerstone for anyone wanting to use analytics. For students, the book gently and wisely guides them on a journey from the basics to the more advanced aspects of predictive analytics. For educators, the book is an ideal teaching aid, given its extensive and careful selection of topics and the variety of tools it illustrates. For more experienced professionals, the book offers a wealth of fresh perspectives and guides them from framing analytics questions to communicating managerial insights. The coverage of topics is extensive: from a very clear introduction to the software R, to regression and classification with machine learning tools, up to the details of stochastic simulation." Emanuele Borgonovo (PhD, MIT) Director, Department of Decision Sciences, Bocconi University Co-Editor-in-chief, European Journal of Operational Research Russell Barton is Distinguished Professor of Supply Chain and Information Systems in the Smeal College of Business and a Certified Analytics Professional. He received his PhD in operations research from Cornell University. He began his career as an analytics consultant at RCA, followed by similar roles at Econ, Inc., The Mentoris Company, and Mathtech, before returning to RCA for eight years. Projects included predicting the impact of telecommuting on urban structure, modeling exoffender post-release behavior, analyzing workers' compensation claims for hundreds of thousands of records, constructing risk models for self-insurance options, simulating semiconductor manufacturing operations, predicting satellite communications reliability, measuring videodisc quality, characterizing image errors in video monitors, and visual representations of experiment results. His academic career began at Cornell University as a Visiting Associate Professor in the School of Operations Research and Industrial Engineering, and Laboratory Director for the Cornell Computational Optimization Project. Following Cornell, he was Professor of Industrial Engineering at Penn State. From 1998–1999 he was a Visiting Professor at Ècole Centrale Paris in the Production and Logistics Research Laboratory. He moved to Penn State's Smeal College of Business in 2002, and served as Associate Dean for Research and MS/PhD Programs, Co-Director of the Master's of Manufacturing Management degree program, and as Senior Associate Dean for Research and Faculty. served as Program Director for Manufacturing Enterprise Systems and Service Enterprise Systems at the US National Science Foundation from 2010–2012, and as an analytics consultant to firms including Fluke, Ford, GE, GM, Kodak, Lockheed-Martin, Textron, and Xerox.