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• Half Title Page
• Title Page
• Copyright Page
• Brief Contents
• Contents
• Preface
• About the Author
• Credits
• Part 1: Foundations of Business Analytics
• Chapter 1: Introduction to Business Analytics
• Learning Objectives
• What Is Business Analytics?
• Using Business Analytics
• Impacts and Challenges
• Evolution of Business Analytics
• Analytic Foundations
• Modern Business Analytics
• Software Support and Spreadsheet Technology
• Analytics in Practice: Social Media Analytics
• Descriptive, Predictive, and Prescriptive Analytics
• Analytics in Practice: Analytics in the Home Lending and Mortgage Industry
• Data for Business Analytics
• Big Data
• Data Reliability and Validity
• Models in Business Analytics
• Descriptive Models
• Predictive Models
• Prescriptive Models
• Model Assumptions
• Uncertainty and Risk
• Problem Solving with Analytics
• Recognizing a Problem
• Defining the Problem
• Structuring the Problem
• Analyzing the Problem
• Interpreting Results and Making a Decision
• Implementing the Solution
• Analytics in Practice: Developing Effective Analytical Tools at Hewlett‐Packard
• Key Terms    
• Chapter 1 Technology Help
• Problems and Exercises
• Case: Performance Lawn Equipment
• Appendix A1: Basic Excel Skills
• Excel Formulas and Addressing
• Copying Formulas
• Useful Excel Tips
• Excel Functions
• Basic Excel Functions
• Functions for Specific Applications
• Insert Function
• Date and Time Functions
• Miscellaneous Excel Functions and Tools
• Range Names
• VALUE Function 
• Paste Special
• Concatenation
• Error Values
• Problems and Exercises
• Chapter 2: Database Analytics
• Learning Objectives
• Data Sets and Databases
• Using Range Names in Databases
• Analytics in Practice: Using Big Data to Monitor Water Usage in Cary, North Carolina
• Data Queries: Tables, Sorting, and Filtering
• Sorting Data in Excel
• Pareto Analysis
• Filtering Data
• Database Functions
• Analytics in Practice: Discovering the Value of Database Analytics at Allders International
• Logical Functions
• Lookup Functions for Database Queries
• Excel Template Design
• Data Validation Tools
• Form Controls
• PivotTables
• PivotTable Customization
• Slicers
• Key Terms  
• Chapter 2 Technology Help
• Problems and Exercises
• Case: People’s Choice Bank
• Case: Drout Advertising Research Project
• Part 2: Descriptive Analytics
• Chapter 3: Data Visualization
• Learning Objectives
• The Value of Data Visualization
• Tools and Software for Data Visualization
• Analytics in Practice: Data Visualization for the New York City Police Department’s Domain Awarene
• Creating Charts in Microsoft Excel
• Column and Bar Charts
• Data Label and Data Table Chart Options
• Line Charts
• Pie Charts
• Area Charts
• Scatter Charts and Orbit Charts
• Bubble Charts
• Combination Charts
• Radar Charts
• Stock Charts
• Charts from PivotTables
• Geographic Data
• Other Excel Data Visualization Tools
• Data Bars
• Color Scales
• Icon Sets
• Sparklines
• Dashboards
• Analytics in Practice: Driving Business Transformation with IBM Business Analytics
• Key Terms 
• Chapter 3 Technology Help
• Problems and Exercises
• Case: Performance Lawn Equipment
• Appendix A3: Additional Tools for Data Visualization
• Hierarchy Charts
• Waterfall Charts
• PivotCharts
• Tableau
• Problems and Exercises
• Chapter 4: Descriptive Statistics
• Learning Objectives
• Analytics in Practice: Applications of Statistics in Health care
• Metrics and Data Classification
• Frequency Distributions and Histograms
• Frequency Distributions for Categorical Data
• Relative Frequency Distributions
• Frequency Distributions for Numerical Data
• Grouped Frequency Distributions
• Cumulative Relative Frequency Distributions
• Constructing Frequency Distributions Using PivotTables
• Percentiles and Quartiles
• Cross‐Tabulations
• Descriptive Statistical Measures
• Populations and Samples
• Statistical Notation
• Measures of Location: Mean, Median, Mode, and Midrange
• Using Measures of Location in Business Decisions
• Measures of Dispersion: Range, Interquartile Range, Variance, and Standard Deviation
• Chebyshev’s Theorem and the Empirical Rules
• Standardized Values (Z‐Scores)
• Coefficient of Variation
• Measures of Shape
• Excel Descriptive Statistics Tool
• Computing Descriptive Statistics for Frequency Distributions
• Descriptive Statistics for Categorical Data: The Proportion
• Statistics in PivotTables
• Measures of Association
• Covariance
• Correlation
• Excel Correlation Tool
• Outliers
• Using Descriptive Statistics to Analyze Survey Data
• Statistical Thinking in Business Decisions
• Variability in Samples
• Analytics in Practice: Applying Statistical Thinking to Detecting Financial Problems
• Key Terms 
• Chapter 4 Technology Help
• Problems and Exercises
• Case: Drout Advertising Research Project
• Case: Performance Lawn Equipment
• Appendix A4: Additional Charts for Descriptive Statistics in Excel for Windows
• Problems and Exercises
• Chapter 5: Probability Distributions and Data Modeling
• Learning Objectives
• Basic Concepts of Probability
• Experiments and Sample Spaces
• Combinations and Permutations
• Probability Definitions
• Probability Rules and Formulas
• Joint and Marginal Probability
• Conditional Probability
• Random Variables and Probability Distributions
• Discrete Probability Distributions
• Expected Value of a Discrete Random Variable
• Using Expected Value in Making Decisions
• Variance of a Discrete Random Variable
• Bernoulli Distribution
• Binomial Distribution
• Poisson Distribution
• Analytics in Practice: Using the Poisson Distribution for Modeling Bids on Priceline
• Continuous Probability Distributions
• Properties of Probability Density Functions
• Uniform Distribution
• Normal Distribution
• The NORM.INV Function
• Standard Normal Distribution
• Using Standard Normal Distribution Tables
• Exponential Distribution
• Triangular Distribution
• Data Modeling and Distribution Fitting
• Goodness of Fit: Testing for Normality of an Empirical Distribution
• Analytics in Practice: The value of Good Data Modeling in Advertising
• Key Terms 
• Chapter 5 Technology Help
• Problems and Exercises
• Case: Performance Lawn Equipment
• Chapter 6: Sampling and Estimation
• Learning Objectives
• Statistical Sampling
• Sampling Methods
• Analytics in Practice: Using Sampling Techniques to Improve Distribution
• Estimating Population Parameters
• Unbiased Estimators
• Errors in Point Estimation
• Understanding Sampling Error
• Sampling Distributions
• Sampling Distribution of the Mean
• Applying the Sampling Distribution of the Mean
• Interval Estimates
• Confidence Intervals
• Confidence Interval for the Mean with Known Population Standard Deviation
• The t‐Distribution
• Confidence Interval for the Mean with Unknown Population Standard Deviation
• Confidence Interval for a Proportion
• Additional Types of Confidence Intervals
• Using Confidence Intervals for Decision Making
• Data Visualization for Confidence Interval Comparison
• Prediction Intervals
• Confidence Intervals and Sample Size
• Key Terms 
• Chapter 6 Technology Help
• Problems and Exercises
• Case: Drout Advertising Research Project
• Case: Performance Lawn Equipment
• Chapter 7: Statistical Inference
• Learning Objectives
• Hypothesis Testing
• Hypothesis‐Testing Procedure
• One‐Sample Hypothesis Tests
• Understanding Potential Errors in Hypothesis Testing
• Selecting the Test Statistic
• Finding Critical Values and Drawing a Conclusion
• Two‐Tailed Test of Hypothesis for the Mean
• Summary of One‐Sample Hypothesis Tests for the Mean
• p‐Values
• One‐Sample Tests for Proportions
• Confidence Intervals and Hypothesis Tests
• An Excel Template for One‐Sample Hypothesis Tests
• Two‐Sample Hypothesis Tests
• Two‐Sample Tests for Differences in Means
• Two‐Sample Test for Means with Paired Samples
• Two‐Sample Test for Equality of Variances
• Analysis of Variance (ANOVA)
• Assumptions of ANOVA
• Chi‐Square Test for Independence
• Cautions in Using the Chi‐Square Test
• Chi‐Square Goodness of Fit Test
• Analytics in Practice: Using Hypothesis Tests and Business Analytics in a Help Desk Service Improvem
• Key Terms 
• Chapter 7 Technology Help
• Problems and Exercises
• Case: Drout Advertising Research Project
• Case: Performance Lawn Equipment
• Part 3: Predictive Analytics
• Chapter 8: Trendlines and Regression Analysis
• Learning Objectives
• Modeling Relationships and Trends in Data
• Analytics in Practice: Using Predictive Trendline Models at Procter & Gamble
• Simple Linear Regression
• Finding the Best‐Fitting Regression Line
• Using Regression Models for Prediction
• Least‐Squares Regression
• Simple Linear Regression with Excel
• Regression as Analysis of Variance
• Testing Hypotheses for Regression Coefficients
• Confidence Intervals for Regression Coefficients
• Residual Analysis and Regression Assumptions
• Checking Assumptions
• Multiple Linear Regression
• Analytics in Practice: Using Linear Regression and Interactive Risk Simulators to Predict Performanc
• Building Good Regression Models
• Correlation and Multicollinearity
• Practical Issues in Trendline and Regression Modeling
• Regression with Categorical Independent Variables
• Categorical Variables with More Than Two Levels
• Regression Models with Nonlinear Terms
• Key Terms   
• Chapter 8 Technology Help
• Problems and Exercises
• Case: Performance Lawn Equipment
• Chapter 9: Forecasting Techniques
• Learning Objectives
• Analytics in Practice: Forecasting Call‐Center Demand at L.L. Bean
• Qualitative and Judgmental Forecasting
• Historical Analogy
• The Delphi Method
• Indicators and Indexes
• Statistical Forecasting Models
• Forecasting Models for Stationary Time Series
• Moving Average Models
• Error Metrics and Forecast Accuracy
• Exponential Smoothing Models
• Forecasting Models for Time Series with a Linear Trend
• Double Exponential Smoothing
• Regression‐Based Forecasting for Time Series with a Linear Trend
• Forecasting Time Series with Seasonality
• Regression‐Based Seasonal Forecasting Models
• Holt‐Winters Models for Forecasting Time Series with Seasonality and No Trend
• Holt‐Winters Models for Forecasting Time Series with Seasonality and Trend
• Selecting Appropriate Time‐Series‐Based Forecasting Models
• Regression Forecasting with Causal Variables
• The Practice of Forecasting
• Analytics in Practice: Forecasting at NBCUniversal
• Key Terms  
• Chapter 9 Technology Help
• Problems and Exercises
• Case: Performance Lawn Equipment
• Chapter 10: Introduction to Data Mining
• Learning Objectives
• The Scope of Data Mining
• Cluster Analysis
• Measuring Distance Between Objects
• Normalizing Distance Measures
• Clustering Methods
• Classification
• An Intuitive Explanation of Classification
• Measuring Classification Performance
• Classification Techniques
• Association
• Cause‐and‐Effect Modeling
• Analytics In Practice: Successful Business Applications of Data Mining
• Key Terms 
• Chapter 10 Technology Help
• Problems and Exercises 
• Case: Performance Lawn Equipment
• Chapter 11: Spreadsheet Modeling and Analysis
• Learning Objectives
• Analytics in Practice: Using Spreadsheet Modeling and Analysis at Nestlé
• Model‐Building Strategies
• Building Models Using Logic and Business Principles
• Building Models Using Influence Diagrams
• Building Models Using Historical Data
• Model Assumptions, Complexity, and Realism
• Implementing Models on Spreadsheets
• Spreadsheet Design
• Spreadsheet Quality
• Data Validation
• Analytics in Practice: Spreadsheet Engineering at Procter & Gamble
• Descriptive Spreadsheet Models
• Staffing Decisions
• Single‐Period Purchase Decisions
• Overbooking Decisions
• Analytics in Practice: Using an Overbooking Model at a Student Health Clinic
• Retail Markdown Decisions
• Predictive Spreadsheet Models
• New Product Development Model
• Cash Budgeting
• Retirement Planning
• Project Management
• Prescriptive Spreadsheet Models
• Portfolio Allocation
• Locating Central Facilities
• Job Sequencing
• Analyzing Uncertainty and Model Assumptions
• What‐If Analysis
• Data Tables
• Scenario Manager
• Goal Seek
• Key Terms 
• Chapter 11 Technology Help
• Problems and Exercises 
• Case: Performance Lawn Equipment
• Chapter 12: Simulation and Risk Analysis
• Learning Objectives
• Monte Carlo Simulation
• Random Sampling from Probability Distributions
• Generating Random Variates using Excel Functions
• Discrete Probability Distributions
• Uniform Distributions
• Exponential Distributions
• Normal Distributions
• Binomial Distributions
• Triangular Distributions
• Monte Carlo Simulation in Excel
• Profit Model Simulation
• New Product Development
• Retirement Planning
• Single‐Period Purchase Decisions
• Overbooking Decisions
• Project Management
• Analytics in Practice: Implementing Large‐Scale Monte Carlo Spreadsheet Models
• Dynamic Systems Simulation
• Simulating Waiting Lines
• Analytics in Practice: Using Systems Simulation for Agricultural Product Development
• Key Terms  
• Chapter 12 Technology Help
• Problems and Exercises 
• Case: Performance Lawn Equipment
• Part 4: Prescriptive Analytics
• Chapter 13: Linear Optimization
• Learning Objectives
• Optimization Models
• Analytics in Practice: Using Optimization Models for Sales Planning at NBC
• Developing Linear Optimization Models
• Identifying Decision Variables, the Objective, and Constraints
• Developing a Mathematical Model
• More About Constraints
• Implementing Linear Optimization Models on Spreadsheets
• Excel Functions to Avoid in Linear Optimization
• Solving Linear Optimization Models
• Solver Answer Report
• Graphical Interpretation of Linear Optimization with Two Variables
• How Solver Works
• How Solver Creates Names in Reports
• Solver Outcomes and Solution Messages
• Unique Optimal Solution
• Alternative (Multiple) Optimal Solutions
• Unbounded Solution
• Infeasibility
• Applications of Linear Optimization
• Blending Models
• Dealing with Infeasibility
• Portfolio Investment Models
• Scaling Issues in Using Solver
• Transportation Models
• Multiperiod Production Planning Models
• Multiperiod Financial Planning Models
• Analytics in Practice: Linear Optimization in Bank Financial Planning
• Key Terms 
• Chapter 13 Technology Help
• Problems and Exercises
• Case: Performance Lawn Equipment
• Chapter 14: Integer and Nonlinear Optimization
• Learning Objectives
• Integer Linear Optimization Models
• Models with General Integer Variables
• Workforce‐Scheduling Models
• Alternative Optimal Solutions
• Models with Binary Variables
• Using Binary Variables to Model Logical Constraints
• Applications in Supply Chain Optimization
• Analytics in Practice: Supply Chain Optimization at Procter & Gamble
• Nonlinear Optimization Models
• A Nonlinear Pricing Decision Model
• Quadratic Optimization
• Practical Issues Using Solver for Nonlinear Optimization
• Analytics in Practice: Applying Nonlinear Optimization at Prudential Securities
• Non‐Smooth Optimization
• Evolutionary Solver
• Evolutionary Solver for Sequencing and Scheduling Models
• The Traveling Salesperson Problem
• Key Terms 
• Chapter 14 Technology Help
• Problems and Exercises 
• Case: Performance Lawn Equipment
• Chapter 15: Optimization Analytics
• Learning Objectives
• What‐If Analysis for Optimization Models
• Solver Sensitivity Report
• Using the Sensitivity Report
• Degeneracy
• Interpreting Solver Reports for Nonlinear Optimization Models
• Models with Bounded Variables
• Auxiliary Variables for Bound Constraints
• What‐If Analysis for Integer Optimization Models
• Visualization of Solver Reports
• Using Sensitivity Information Correctly
• Key Terms 
• Chapter 15 Technology Help
• Problems and Exercises
• Case: Performance Lawn Equipment
• Part 5: Making Decisions
• Chapter 16: Decision Analysis
• Learning Objectives
• Formulating Decision Problems
• Decision Strategies Without Outcome Probabilities
• Decision Strategies for a Minimize Objective
• Decision Strategies for a Maximize Objective
• Decisions with Conflicting Objectives
• Decision Strategies with Outcome Probabilities
• Average Payoff Strategy
• Expected Value Strategy
• Evaluating Risk
• Decision Trees
• Decision Trees and Risk
• Sensitivity Analysis in Decision Trees
• The Value of Information
• Decisions with Sample Information
• Bayes’s Rule
• Utility and Decision Making
• Constructing a Utility Function
• Exponential Utility Functions
• Analytics in Practice: Using Decision Analysis in Drug Development
• Key Terms 
• Chapter 16 Technology Help
• Problems and Exercises
• Case: Performance Lawn Equipment
• Appendix A
• Glossary
• Index
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