Business Analytics Data Analysis and Decision Making 5th Edition by Christian Albright, Wayne Winston ISBN 1133588263 978-1133588269

Ch 1: Introduction to Data Analysis and Decision Making

1-1: Introduction

1-2: Overview of the Book

1-3: Modeling and Models

1-4: Conclusion

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Part 1: Exploring Data

Ch 2: Describing the Distribution of a Single Variable

2-1: Introduction

2-2: Basic Concepts

2-3: Descriptive Measures for Categorical Variables

2-4: Descriptive Measures for Numerical Variables

2-5: Time Series Data

2-6: Outliers and Missing Values

2-7: Excel Tables for Filtering, Sorting, and Summarizing

2-8: Conclusion

Ch 3: Finding Relationships among Variables

3-1: Introduction

3-2: Relationships among Categorical Variables

3-3: Relationships among Categorical Variables and a Numerical Variable

3-4: Relationships among Numerical Variables

3-5: Pivot Tables

3-6: Conclusion

Part 2: Probability and Decision Making under Uncertainty

Ch 4: Probability and Probability Distributions

4-1: Introduction

4-2: Probability Essentials

4-3: Probability Distribution of a Single Random Variable

4-4: Introduction to Simulation

4-5: Conclusion

Ch 5: Normal, Binomial, Poisson, and Exponential Distributions

5-1: Introduction

5-2: The Normal Distribution

5-3: Applications of the Normal Distribution

5-4: The Binomial Distribution

5-5: Applications of the Binomial Distribution

5-6: The Poisson and Exponential Distributions

5-7: Conclusion

Ch 6: Decision Making under Uncertainty

6-1: Introduction

6-2: Elements of Decision Analysis

6-3: The Precisiontree Add-In

6-4: Bayes’ Rule

6-5: Multistage Decision Problems and the Value of Information

6-6: Risk Aversion and Expected Utility

6-7: Conclusion

Part 3: Statistical Inference

Ch 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

Ch 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

Ch 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

Ch 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

Ch 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

Ch 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

Ch 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

Ch 14: Optimization Models

14-1: Introduction

14-2: Worker 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

Ch 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: Introduction to the @RISK

15-6: The Effects of Input Distributions on Results

15-7: Conclusion

Ch 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: An Automated Template for @RISK Models

16-7: Conclusion

Part 6: Advanced Data Analysis

Ch 17: Data Mining

17-1: Introduction

17-2: Data Exploration and Visualization

17-3: Microsoft Data Mining Add-Ins for Excel

17-4: Classification Methods

17-5: Clustering

17-6: Conclusion

Part 7: Bonus Online Material

Ch 18: Importing Data into Excel

18-1: Introduction

18-2: Rearranging Excel Data

18-3: Importing Text Data

18-4: Importing Relational Database Data

18-5: Web Queries

18-6: Cleansing Data

18-7: Conclusion

Ch 19: Analysis of Variance and Experimental Design

19-1: Introduction

19-2: One-Way ANOVA

19-3: Using Regression to Perform ANOVA

19-4: The Multiple Comparison Problem

19-5: Two-Way ANOVA

19-6: More about Experimental Design

19-7: Conclusion

Ch 20: Statistical Process Control

20-1: Introduction

20-2: Deming’s 14 Points

20-3: Introduction to Control Charts

20-4: Control Charts for Variables

20-5: Control Charts for Attributes

20-6: Process Capability

20-7: Conclusion

Appendix A: Statistical Reporting

A-1: Introduction

A-2: Suggestions for Good Statistical Reporting

A-3: Examples of Statistical Reports

A-4: Conclusion

References

Index