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  

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 ul

New and Updated for this Edition Screenshots throughout the text are updated for Excel® 2013. The updated design strikes a better balance between the text and the image size. More problems have been added at the end of every chapter. There is a new case on Drout Advertising Research.   Content Updates New and Updated for this Edition Screenshots throughout the text are updated for Excel® 2013. The updated design strikes a better balance between the text and the image size. More problems have been added at the end of every chapter. There is a new case on Drout Advertising Research.   Content Updates Chapter 1 has been heavily revised with updated sections and new examples throughout Chapter 2 New objective–Use Excel functions for business intelligence queries in database New information on VLOOKUP function Chapter 3 New section on Data Visualization with Figures and Captures New examples: Data Visualization through formal conditioning Examples of Sparklines Using Slicers New Analytics in Practice Box: Driving Business Transformation with IBM Business Analytics Chapter 4 includes a revised section on the CORREL function Chapter 5 New section on Join and Marginal Probability New example–Applying Probability Rules to Joint Events Chapter 7 New section: Confidence Intervals and Hypothesis Tests New section: Cautions in Using the Chi-Square Test Chapter 8 New learning objective: List the Common Types of Mathematical Functions Used in Predictive Modeling New section: Practical Issues in Trendline and Regression Modeling Chapter 10 New section: Data Exploration New example: Using XLMiner to Sample from a Worksheet New example: A Boxplot for Credit Risk Data Chapter 11 New learning objective: Use Excel tools to create user-friendly Excel Models and Applications New section: Spreadsheet Applications in Business Analytics New section: Developing User-Friendly Excel Applications New example: Using Data Validation New example: Using Form Controls for the Outsourcing Decision Model Chapter 13 includes a new example: Multiple Parameter Analysis for the SSC Problem Chapter 14 New section: Solver Output and Data Visualization New example: Little Investment Advisers New section on scaling Chapter 16 New section: Risk and Variability