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Book: Modeling Techniques in Predictive Analytics: Business Problems and Solutions with R

Today, successful firms compete and win based on analytics. Modeling Techniques inPredictive Analytics brings together all the concepts, techniques, and R code you need to excel in any role involving analytics. Thomas W. Miller’s unique balanced approach combines business context and quantitative tools, appealing to managers, analysts, programmers, and students alike. Miller addresses multiple business challenges and business cases, including segmentation, brand positioning, product choice modeling, pricing research, finance, sports, text analytics, sentiment analysis, and social network analysis. He illuminates the use of cross-sectional data, time series, spatial, and even spatio-temporal data. For each problem, Miller explains why the problem matters, what data is relevant, how to explore your data once you’ve identified it, and then how to successfully model that data. You’ll learn how to model data conceptually, with words and figures; and then how to model it with realistic R programs that deliver actionable insights and knowledge. Miller walks you through model construction, explanatory variable subset selection, and validation, demonstrating best practices for improving out-of-sample predictive performance. He employs data visualization and statistical graphics in exploring data, presenting models, and evaluating performance. All example code is presented in R, today’s #1 system for applied statistics, statistical research, and predictive modeling; code is set apart from other text so it’s easy to find for those who want it (and easy to skip for those who don’t).

Modeling Techniques in Predictive Analytics: Business Problems and Solutions with R

About the Author

Thomas W. Miller is faculty director of the Predictive Analytics program at Northwestern University. He has designed courses for the program, including Marketing Analytics, Advanced Modeling Techniques, Data Visualization, and the capstone course. He has taught extensively in the program and works with more than forty other faculty members in delivering training in predictive analytics and data science.

 

Miller is also owner and president of Research Publishers LLC. He has consulted widely in the areas of retail site selection, product positioning, segmentation, and pricing in competitive markets, and has worked with predictive models for over 30 years.

 

Miller’s books include Data and Text Mining: A Business Applications ApproachResearch and Information Services: An Integrated Approach for Business, and a book about predictive modeling in sports, Without a Tout: How to Pick a Winning Team.

 

Before entering academia, Miller spent nearly 15 years in business IT in the computer and transportation industries. He also directed the A. C. Nielsen Center for Marketing Research and taught market research and business strategy at the University of Wisconsin–Madison.

 

He holds a Ph.D. in psychology (psychometrics), a master’s degree in statistics from the University of Minnesota, and an MBA and master’s degree in economics from the University of Oregon.

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Introduction to Modeling Techniques in Predictive Analytics: Busine...

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Table of Contents

Preface     v

Figures     ix

Tables     xiii

Exhibits     xv

1. Analytics and Data Science     1

2. Advertising and Promotion     15

3. Preference and Choice     29

4. Market Basket Analysis     37

5. Economic Data Analysis     53

6. Operations Management     67

7. Text Analytics     83

8. Sentiment Analysis     113

9. Sports Analytics     149

10. Brand and Price     173

11. Spatial Data Analysis     209

12. The Big Little Data Game     231

A. There's a Pack for That     237

B. Measurement     253

C. Code and Utilities     267

Bibliography     297

Index     327

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Replies to This Discussion

Very interesting! It will be quite useful, and may I'll start to look a little closer to business applications. Thanks.

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