Miller, Thomas W <br/><br/>
Modeling techniques in predictive analytics business problems and solutions with R 

 - Singapore  Pearson  2014 
 - xv, 330p.  24cm; Hard bound 
<br/><br/> Alpha 925/03-07-2014 
<br/><br/>Table of Contents<br/>Preface   v<br/>Figures   ix<br/>Tables   xiii<br/>Exhibits   xv<br/>1. Analytics and Data Science   1<br/>2. Advertising and Promotion   15<br/>3. Preference and Choice   29<br/>4. Market Basket Analysis   37<br/>5. Economic Data Analysis   53<br/>6. Operations Management   67<br/>7. Text Analytics   83<br/>8. Sentiment Analysis   113<br/>9. Sports Analytics   149<br/>10. Brand and Price   173<br/>11. Spatial Data Analysis   209<br/>12. The Big Little Data Game   231<br/>A. There's a Pack for That   237<br/>B. Measurement   253<br/>C. Code and Utilities   267<br/>Bibliography   297<br/>Index   327 
<br/><br/> Today, successful firms win by understanding their data more deeply than competitors do. In short, they compete based on analytics. Now, in Modeling Techniques in Predictive Analytics, the leader of Northwestern University’s prestigious analytics program brings together all the concepts, techniques, and R code you need to excel in analytics. Thomas W. Miller’s unique balanced approach combines business context and quantitative tools, appealing to managers, analysts, programmers, and students alike.<br/> <br/>Miller addresses multiple business challenges and business cases, including segmentation, brand positioning, product choice modeling, pricing research, finance, sports, Web and text analytics, 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: <br/>Why the problem is significant<br/>What data is relevant<br/>How to explore your data<br/>How to model your data – first conceptually, with words and figures; and then with mathematics and programs<br/>Miller walks 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. Extensive example code is presented in R, today’s #1 system for applied statistics, statistical research, and predictive modeling; all 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). 
<br/><br/><label>ISBN: </label>9780133412932 
<br/><br/><label>Subjects--Topical Terms: </label><br/>Management simulation methods<br/>Business forecasting<br/>Data processing<br/>Analytics
<br/><br/><label>Dewey Class. No.: </label>658.40352 MIL