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 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 Methods
17-4 Conclusion
18 Analysis of Variance and Experimental Design (MindTap Reader only)
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 (MindTap Reader only)
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
APPENDIX A: Quantitative Reporting (MindTap Reader only)
A-1 Introduction
A-2 Suggestions for Good Quantitative Reporting
A-3 Examples of Quantitative Reports
A-4 Conclusion
References
Index
