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 types of software packages are used in Business analytics applications in industry, this book uses Microsoft Excel 2013and Frontline Systems'' powerful Excel add-ins, Risk Solver Platform and XLMiner , which together provide extensive capabilities for business analytics. Frontline is used at over 7,500 companies. Integrated throughout the book, Frontline Systems'' Analytic Solver Platform for Education Excel add-in software provides a comprehensive basis to learn business analytics effectively, with real-world career value. It includes: Risk Solver Pro--This program is a tool for risk analysis, simulation, and optimization in Excel. XLMiner--This program is a data mining add-in for Excel.

Premium Solver Platform, a large superset of Premium Solver and by far the most powerful spreadsheet optimizer, with its PSI interpreter for model analysis and five built-in Solver Engines for linear, quadratic, SOCP, mixed-integer, nonlinear, non-smooth and global optimization. Ability to solve optimization models with uncertainty and recourse decisions, using simulation optimization, stochastic programming, robust optimization, and stochastic decomposition. New integrated sensitivity analysis and decision tree capabilities, developed in cooperation with Prof. Chris Albright (SolverTable), Profs. Stephen Powell and Ken Baker (Sensitivity Toolkit), and Prof. Mike Middleton (TreePlan). A special version of the Gurobi Solver--the ultra-high-performance linear mixed-integer optimizer created by the respected computational scientists at Gurobi Optimization.