Business Analytics, Global Edition

By (author) Evans, James R.
Description
For undergraduate or graduate business students. A balanced and holistic approach to business analytics Business Analytics, Second Edition teaches the fundamental concepts of the emerging field of business analytics and provides vital tools in understanding how data analysis works in today''s organizations. Students will learn to apply basic business analytics principles, communicate with analytics professionals, and effectively use and interpret analytic models to make better business decisions. Included access to commercial grade analytics software gives students real-world experience and career-focused value. Author James Evans takes a balanced, holistic approach and looks at business analytics from descriptive, and predictive perspectives.
Table of contents
PART 1: Foundations of Business Analytics 1. Introduction to Business Analytics 2. Analytics on Spreadsheets Part 2: Descriptive Analytics 3. Visualizing and Exploring Data 4. Descriptive Statistical Measures 5. Probability Distributions and Data Modeling 6. Sampling and Estimation 7. Statistical Inference Part 3: Predictive Analytics 8. Trendlines and Regression Analysis 9. Forecasting Techniques 10. Introduction to Data Mining 11. Spreadsheet Modeling and Analysis 12. Monte Carlo Simulation and Risk Analysis Part 4: Prescriptive Analytics 13. Linear Optimization 14. Applications of Linear Optimization 15. Integer Optimization 16. Decision Analysis Supplementary Chapter A (online): Nonlinear and Non-Smooth Optimization Supplementary Chapter B (online): Optimization Models with Uncertainty Appendix A Glossary Index  
Feature
Content is organized into five parts to guide students through the information: Part 1: Foundations of Business Analytics. The first two chapters provide the basic foundations needed to understand business analytics and Microsoft Excel, and show students how to manipulate data and develop simple spreadsheet models. Part 2: Descriptive Analytics. Chapters 3 through 7 focus on the fundamental tools and methods of data analysis and statistics, focusing on visual representations of data, descriptive statistical measures, probability distributions and data modeling, sampling and estimation, and statistical inference. Part 3: Predictive Analytics. Chapters 8 through 12 develop approaches for building and analyzing predictive models, applying regression and forecasting techniques, simulation and risk analysis, and an introduction to data mining. Part 4: Prescriptive Analytics. Chapters 13 through 17 explore linear, integer, and nonlinear optimization models and applications including optimization with uncertainty. Part 5: Making Decisions. Chapter 18 focuses on philosophies, tools, and techniques of decision analysis. In-text features aid in student understanding: Numbered Examples—these numerous, short examples appear throughout all chapters, illustrating key concepts and techniques. Analytics in Practice—this feature describes real applications in business. Learning Objectives—this feature lists the goals students should be able to achieve after studying the chapter. Key Terms— these words are bolded within the text and listed at the end of each chapter to assist students as they review the chapter and study for exams. Key terms and their definitions are contained in the Glossary at the end of the book. End-of-Chapter Problems and Exercises—these problems and exercises help to reinforce the material covered through the chapter. Integrated Case—this case encourages students to think independently and apply the tools at a higher level of learning. Data Sets and Excel Models—these files are used in examples and problems and are available to students at pearsonhighered.com/evans. Complete Software Support: While many different type