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Forecasting and Predictive Analytics with Forecast X (TM)
Forecasting and Predictive Analytics with Forecast X (TM)

Forecasting and Predictive Analytics with Forecast X (TM), 7th Edition

ISBN10: 1259903915 | ISBN13: 9781259903915
By Barry Keating, J. Holton Wilson and John Solutions Inc.
© 2019

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* The estimated amount of time this product will be on the market is based on a number of factors, including faculty input to instructional design and the prior revision cycle and updates to academic research-which typically results in a revision cycle ranging from every two to four years for this product. Pricing subject to change at any time.

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Forecasting and Predictive Analytics, Seventh Edition, is the most practical forecasting book on the market with the most powerful software: ForecastX. This edition presents a broad-based survey of business forecasting methods, including subjective and objective approaches. The authors, Keating and Wilson, deliver practical how-to forecasting techniques, along with dozens of real-world data sets while holding theory and math to a minimum.


Today, most business planning routinely begins with a sales forecast. Whether you are an accountant, a marketer, a human resources manager, a data scientist, or a financial analyst, sooner or later, you will have to predict something. This book is designed to lead students through the most helpful techniques to use in any prediction effort.

New to This Edition
• Four new chapters focus on predictive analytics, recognizing the importance of these tools in today's prediction efforts.
• Examples of predictive analytics are based on historical data, much like what students may encounter in their own forecasts.
• Techniques are explained as procedures, allowing students to replicate them with their own data.• All chapters now include Learning Objectives.
• The ForecastX software sections are updated and clarified.Retained Features 
• Real-world data are used throughout the text, which consists almost entirely of real-world examples, showing readers how to deal with real-life problems and difficulties in a realistic environment.
• Extended examples are presented in each chapter. Two examples are used to demonstrate the progression of techniques needed to solve each issue; these examples provide a realistic illustration of the concepts that are relevant to the student used in the text. One example features sales data from The Gap, whereas another example uses domestic car sales data.
• By consistently providing all the data in Excel, the leading calculation software used in business, students recognize its compatibility with ForecastX software.
• Screenshots of problems help students better conceptualize problems. The edition includes many screenshots of actual problems, and the data for each of these examples and problems are included in the accompanying ForecastX software.

For Connect users, the free ReadAnywhere downloadable app is available on iOS and Android mobile devices. It gives students mobile freedom to access their ebook anywhere, even offline, on their smartphone or tablet. Once chapters are downloaded, students can use the same tools that are available in the ebook and any notes or highlights they make in the ebook will sync across platforms. Visit https://www.mheducation.com/highered/explore/readanywhere.html for more information.

1: Introduction to Business Forecasting and Predictive Analytics   
2: The Forecast Process, Data Considerations, and Model Selection  
3: Extrapolation 1. Moving Averages and Exponential Smoothing   
4: Extrapolation 2. Introduction to Forecasting with Regression Trend Models   
5: Explanatory Models 1. Forecasting with Multiple Regression Causal Models  
6: Explanatory Models 2. Time-Series Decomposition    
7: Explanatory Models 3. ARIMA (Box-Jenkins) Forecasting Models   
8: Predictive Analytics: Helping to Make Sense of Big Data   
9: Classification Models: The Most Used Models in Analytics
10:Ensemble Models and Clustering  
11: Text Mining
12: Forecast/Analytics Implementation  

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Main Features

  • LMS Integration
  • Print/Loose-Leaf Book Add-On Availability
  • Presentation Slides & Instructor Resources
  • Question & Test Banks
  • Adaptive Assignments
  • Student Progress Reporting & Analytics
  • Essay Prompts
  • Polling
  • Prebuilt Courses
  • Interactive Exercises
  • eBook Access (ReadAnywhere App)
  • Remote Proctoring (Proctorio)
  • Subject-Specific Tools

About the Author

Barry Keating

Barry Keating holds a Ph.D. from the University of Notre Dame, where he is a professor of economics and business analytics in the Department of Finance. He previously was on the faculty at Virginia Polytechnic and State University, served as the manager of the Journal of Public Choice, and was named the Salvatori Faculty Fellow at the Heritage Foundation in Washington, D.C. Dr. Keating has consulted for Accenture, the Institute of Business Forecasting, and ForecastX. He currently is studying how nonlinear models are used in supply chain management. His work has appeared in Public Choice, the Quarterly Review of Economics and Finance, and Applied Business Research, among other journals. Dr. Keating has been quoted in the Wall Street Journal and on NBC News. He can be seen as a primary instructor on Notre Dame's MOOC: Understanding Wireless

J. Holton Wilson

J. Holton Wilson holds a B.A. and B.S. from Otterbein College, an M.B.A. from Bowling Green State University, and a D.B.A. from Kent State University. Dr. Wilson is professor emeritus of marketing at Central Michigan University, where he taught marketing analysis, sales forecasting, and economic analysis for managers. He has published numerous articles in economic, forecasting, and marketing journals, as well as several textbooks in these disciplines. Dr. Wilson is a member of many professional associations, including the American Marketing Association, the American Economic Association, and the International Institute of Forecasters.

John Solutions Inc.

John Galt Solutions Inc. is the software company that produces ForecastX, which accompanies the seventh edition of Forecasting and Predictive Analytics.

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