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 Data Analysis
4.1. Introduction
4.2. Importing Data into Excel with Power Query
4.3. Data Analysis with Power Pivot
4.4. Data Visualization with Tableau Public
4.5. Data Cleansing
4.6. Conclusion
Part 2: Probability and Decision Making under Uncertainty
5. Probability and Probability Distributions
5.1. Introduction
5.2. Probability Essentials
5.3. Probability Distribution of a Random Variable
5.4. The Normal Distribution
5.5. The Binomial Distribution
5.6. The Poisson and Exponential Distributions
5.7. Conclusion
6. Decision Making Under Uncertainty
6.1. Introduction
6.2. Elements of a Decision Analysis
6.3. EMV and Decision Trees
6.4. One-Stage Decision Problems
6.5. The PrecisionTree Add-In
6.6. Multistage Decision Problems
6.7. The Role of Risk Aversion
6.8. Conclusion
Part 3: Statistical Inference
7. Sampling and Sampling Distributions
7.1. Introduction
7.2. Sampling Terminology
7.3. Methods for Selecting Random Samples
7.4. Introduction to Estimation
7.5. Conclusion
8. Confidence Interval Estimation
8.1. Introduction
8.2. Sampling Distributions
8.3. Confidence Interval for a Mean
8.4. Confidence Interval for a Total
8.5. Confidence Interval for a Proportion
8.6. Confidence Interval for a Standard Deviation
8.7. Confidence Interval for the Difference Between Means
8.8. Confidence Interval for the Difference Between Proportions
8.9. Sample Size Selection
8.10. Conclusion
9. Hypothesis Testing
9.1. Introduction
9.2. Concepts in Hypothesis Testing
9.3. Hypothesis Tests for a Population Mean
9.4. Hypothesis Tests for Other Parameters
9.5. Tests for Normality
9.6. Chi-Square Test for Independence
9.7. Conclusion
Part 4: Regression Analysis and Time Series Forecasting
10. Regression Analysis: Estimating Relationships
10.1. Introduction
10.2. Scatterplots: Graphing Relationships
10.3. Correlations: Indicators of Linear Relationships
10.4. Simple Linear Regression
10.5. Multiple Regression
10.6. Modeling Possibilities
10.7. Validation of the Fit
10.8. Conclusion
11. Regression Analysis: Statistical Inference
11.1. Introduction
11.2. The Statistical Model
11.3. Inferences About the Regression Coefficients
11.4. Multicollinearity
11.5. Include/Exclude Decisions
11.6. Stepwise Regression
11.7. Outliers
11.8. Violations of Regression Assumptions
11.9. Prediction
11.10. Conclusion
12. Time Series Analysis and Forecasting
12.1. Introduction
12.2. Forecasting Methods: An Overview
12.3. Testing for Randomness
12.4. Regression-Based Trend Models
12.5. The Random Walk Model
12.6. Moving Averages Forecasts
12.7. Exponential Smoothing Forecasts
12.8. Seasonal Models
12.9. Conclusion
Part 5: Optimization and Simulation Modeling
13. Introduction to Optimization Modeling
13.1. Introduction
13.2. Introduction to Optimization
13.3. A Two-Variable Product Mix Model
13.4. Sensitivity Analysis
13.5. Properties of Linear Models
13.6. Infeasibility and Unboundedness
13.7. A Larger Product Mix Model
13.8. A Multiperiod Production Model
13.9. A Comparison of Algebraic and Spreadsheet Models
13.10. A Decision Support System
13.11. Conclusion
14. Optimization Models
14.1. Introduction
14.2. Employee Scheduling Models
14.3. Blending Models
14.4. Logistics Models
14.5. Aggregate Planning Models
14.6. Financial Models
14.7. Integer Optimization Models
14.8. Nonlinear Optimization Models
14.9. Conclusion
15. Introduction to Simulation Modeling
15.1. Introduction
15.2. Probability Distributions for Input Variables
15.3. Simulation and the Flaw of Averages
15.4. Simulation with Built-In Excel Tools
15.5. Simulation with @RISK
15.6. The Effects of Input Distributions on Results
15.7. Conclusion
16. Simulation Models
16.1. Introduction
16.2. Operations Models
16.3. Financial Models
16.4. Marketing Models
16.5. Simulating Games of Chance
16.6. Conclusion
Part 6: Advanced Data Analysis
17. Data Mining
17.1. Introduction
17.2. Classification Methods
17.3. Clustering
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