1. Introduction to Business Analytics
1.1. Introduction
1.2. Overview of the Book
1.3. Introduction to Spreadsheet Modeling
1.4. Conclusion
Part 1: Data Analysis
2. Describing the Distribution of a Variable
2.1. Introduction
2.2. Basic Concepts
2.3. Summarizing Categorical Variables
2.4. Summarizing Numeric Variables
2.5. Time Series Data
2.6. Outliers and Missing Values
2.7. Excel Tables for Filtering, Sorting, and Summarizing
2.8. Conclusion
Appendix: Introduction to StatTools
3. Finding Relationships Among Variables
3.1. Introduction
3.2. Relationships Among Categorical Variables
3.3. Relationships Among Categorical Variables and a Numeric Variable
3.4. Relationships Among Numeric Variables
3.5. Pivot Tables
3.6. Conclusion
Appendix: Using StatTools to Find Relationships
4. Business Intelligence (BI) Tools for Importing and Transforming Data: Power Query
4.1. Introduction
4.2. Introduction to Relational Databases
4.3. Excel’s Data Model
4.4. Using Power Query in Excel
4.5. Using Power Query in Power BI Desktop
4.6. Conclusion
5. Business Intelligence (BI) Tools for Reports and Visualizations: Power Pivot
5.1. Introduction
5.2. Data Analysis with Power Pivot
5.3. Data Visualization
5.4. Conclusion
Part 2: Probability and Decision Making under Uncertainty
6. Probability and Probability Distributions
6.1. Introduction
6.2. Probability Essentials
6.3. Probability Distribution of a Random Variable
6.4. The Normal Distribution
6.5. The Binomial Distribution
6.6. The Poisson and Exponential Distributions
6.7. Conclusion
7. Decision Making Under Uncertainty
7.1. Introduction
7.2. Elements of a Decision Analysis
7.3. EMV and Decision Trees
7.4. One-Stage Decision Problems
7.5. The PrecisionTree Add-In
7.6. Multistage Decision Problems
7.7. The Role of Risk Aversion
7.8. Conclusion
Part 3: Statistical Inference, Regression Analysis, and Time Series Forecasting
8. Statistical Inference
8.1. Introduction
8.2. Why Random Sampling?
8.3. Sampling Distributions
8.4. Concepts in Hypothesis Testing and Confidence Interval Estimation
8.5. Examples of Hypothesis Testing and Confidence Interval Estimation
8.6. Conclusion
9. Regression Analysis: Estimating Relationships
9.1. Introduction
9.2. Scatterplots: Graphing Relationships
9.3. Correlations: Indicators of Linear Relationships
9.4. Simple Linear Regression
9.5. Multiple Regression
9.6. Modeling Possibilities
9.7. Validation of the Fit
9.8. Conclusion
10. Regression Analysis: Statistical Inference
10.1. Introduction
10.2. The Statistical Model
10.3. Inferences About the Regression Coefficients
10.4. Multicollinearity
10.5. Include/Exclude Decisions
10.6. Stepwise Regression
10.7. Outliers
10.8. Violations of Regression Assumptions
10.9. Prediction
10.10. Conclusion
11. Time Series Analysis and Forecasting
11.1. Introduction
11.2. Forecasting Methods: An Overview
11.3. Testing for Randomness
11.4. Regression-Based Trend Models
11.5. The Random Walk Model
11.6. Moving Averages Forecasts
11.7. Exponential Smoothing Forecasts
11.8. Seasonal Models
11.9. Conclusion
Part 4: Optimization and Simulation Modeling
12. Introduction to Optimization Modeling
12.1. Introduction
12.2. Introduction to Optimization
12.3. A Two-Variable Product Mix Model
12.4. Sensitivity Analysis
12.5. Properties of Linear Models
12.6. Infeasibility and Unboundedness
12.7. A Larger Product Mix Model
12.8. A Multiperiod Production Model
12.9. A Comparison of Algebraic and Spreadsheet Models
12.10. A Decision Support System
12.11. Conclusion
13. Optimization Models
13.1. Introduction
13.2. Employee Scheduling Models
13.3. Blending Models
13.4. Logistics Models
13.5. Aggregate Planning Models
13.6. Financial Models
13.7. Integer Optimization Models
13.8. Nonlinear Optimization Models
13.9. Conclusion
14. Introduction to Simulation Modeling
14.1. Introduction
14.2. Probability Distributions for Input Variables
14.3. Simulation and the Flaw of Averages
14.4. Simulation with Built-In Excel Tools
14.5. Simulation with @RISK
14.6. The Effects of Input Distributions on Results
14.7. Conclusion
15. Simulation Models
15.1. Introduction
15.2. Operations Models
15.3. Financial Models
15.4. Marketing Models
15.5. Simulating Games of Chance
15.6. Conclusion
Part 5: Advanced Data Analysis
16. Data Mining: Classification
16.1. Introduction
16.2. Classification Methods
16.3. Conclusion
17. Data Mining: Clustering and Association Rules
17.1. Introduction
17.2. Clustering Methods
17.3. Association Rules
17.4. Conclusion
Appendix A: Quantitative Reporting
Bonus Online Material
18. Analysis of Variance and Experimental Design
18.1. Introduction
18.2. One-Way ANOVA
18.3. Using Regression to Perform ANOVA
18.4. The Multiple Comparison Problem
18.5. Two-Way ANOVA
18.6. More About Experimental Design
18.7. Conclusion
19. Statistical Process Control
19.1. Introduction
19.2. Deming’s 14 Points
19.3. Introduction to Control Charts
19.4. Control Charts for Variables
19.5. Control Charts for Attributes
19.6. Process Capability
19.7. Conclusion