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ISBN10: 1259903915 | ISBN13: 9781259903915
ISBN10: 1259903915
ISBN13: 9781259903915
* 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
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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