Business Analytics, 4/e (Hardocver)

本書序言

●NEW ONLINE CHAPTER APPENDICES FOCUS ON HOW TO USE R SOFTWARE EFFECTIVELY. This edition's Chapters 1 through 9 offer online appendices detailing how to use the popular open-source software R. The appendices introduce R for descriptive statistics, data visualization, probability, regression and data mining. The authors use R Studio for an easier introduction for both you and your students. The R appendices for descriptive data mining (Ch. 4) and prescriptive data mining (Ch. 9) describe these methods using Rattle, an R package that provides a "point-and-click" graphical user interface as well native R commands.
●NEW SECTION HIGHLIGHTS LEGAL AND ETHICAL ISSUES IN THE USE OF DATA AND ANALYTICS. Chapter 1 includes a new section that addresses common legal and ethical issues related to the use of data and analytics. This new legal and ethical section discusses recent data privacy laws as well as ethical issues that both practitioners and consumers of analytics models should consider.
●NEW HOMEWORK PROBLEMS AND CASES HIGHLIGHT DATA MINING AND CUMULATIVE KNOWLEDGE. The chapters on data mining in this edition contain even more problems that do not require specialized software. This gives you the flexibility to introduce these important topics, even if you do not want students to have to learn additional software to solve the problems. This edition also introduces numerous additional cases throughout the text, including cases that integrate topics from multiple chapters to emphasize how various analytics topics interact and build upon each another.
●NEW ONLINE APPENDIX INTRODUCES HOW TO USE TABLEAU FOR DATA VISUALIZATION. This brand-new online appendix details how to maximize the features of Tableau for data visualization. The authors apply their proven, step-by-step presentation methods to clearly guide students through using this powerful software to produce useful charts for analytics.
●REVISED DATA MINING CHAPTERS OFFER CLEARER PRESENTATION OF CONCEPTS. The authors have reorganized and updated this edition's data mining chapters to ensure students thoroughly understand the presentation. The descriptive data mining chapter (Ch. 5) now appears after the probability chapter so that the data mining discussion can directly integrate notions of probability within the explanations.

本書特色

●ANALYTICS IN ACTION EFFECTIVELY DEMONSTRATE THE IMPORTANCE OF CONCEPTS IN BUSINESS TODAY. Each chapter contains an Analytics in Action feature that presents interesting examples of how professionals use business analytics in actual practice today. Engaging examples are drawn from organizations in a variety of areas, including healthcare, finance, manufacturing and marketing.
●PRACTICAL, RELEVANT PROBLEMS HELP STUDENTS MASTER CONCEPTS AND HANDS-ON SKILLS. Applications drawn from all functional business areas, including finance, marketing and operations, provide important practice at a variety of levels of difficulty. Time-saving data sets are available for most exercises and cases.
●STEP-BY-STEP INSTRUCTIONS EXPLAIN IMPORTANT ANALYTICAL STEPS. Clear instructions show students how to use a variety of leading software programs to perform the analyses discussed in the text.
●COMPLETELY INTEGRATED COVERAGE OF EXCEL DEMONSTRATES THE LATEST METHODS FOR SOLVING PRACTICAL PROBLEMS. Clear, step-by-step instructions teach students to use Excel as a tool for applying concepts in the book. The authors also include by-hand calculations to highlight specific analytical insights, when appropriate.
●ONLINE DATA FILES AND MODEL FILES SAVE TIME. All data sets used as examples and used within student exercises are provided online for convenient student download. DATAfiles are files that contain data that corresponds to examples and problems given in the text. MODELfiles contain additional modeling features that highlight the extensive use of Excel formulas or the use of other software.

1. Introduction.
2. Descriptive Statistics.
3. Data Visualization.
4. Probability: An Introduction to Modeling Uncertainty.
5. Descriptive Data Mining.
6. Statistical Inference.
7. Linear Regression.
8. Time Series Analysis and Forecasting.
9. Predictive Data Mining.
10. Spreadsheet Models.
11. Monte Carlo Simulation.
12. Linear Optimization Models.
13. Integer Linear Optimization Models.
14. Nonlinear Optimization Models.
15. Decision Analysis.
Appendix A: Basics of Excel.
Appendix B: Database Basics with Microsoft Access.
Appendix C: Solutions to Even-Numbered Questions (MindTap Reader).