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Business Analytics

By Jeffrey D. Camm, James J. Cochran, Michael J. Fry, Jeffrey W. Ohlmann

6th Edition © 2027
Discipline:Decision Sciences
Course:Business Statistics & Analytics
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Present the full range of analytics -- from descriptive and predictive to prescriptive analytics to generative AI -- with Camm/Cochran/Fry/Ohlmann's market-leading "Business Analytics," 6th Edition. Step-by-step instructions teach students how to use Excel, Tableau, Power BI, R and Python to solve advanced analytics concepts. Choose your preferred software for teaching concepts. Solutions to problems and cases save grading time and provide students with practice. Updates cover topics beyond the traditional quantitative concepts such as data wrangling, data visualization, machine learning and AI, which are increasingly important in today's analytical problem solving. MindTap and WebAssign customizable online learning platforms offer an interactive eBook, auto-graded exercises, algorithmic practice problems and Exploring Analytics visualizations to strengthen students' understanding.

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About this product

Present the full range of analytics -- from descriptive and predictive to prescriptive analytics to generative AI -- with Camm/Cochran/Fry/Ohlmann's market-leading "Business Analytics," 6th Edition. Step-by-step instructions teach students how to use Excel, Tableau, Power BI, R and Python to solve advanced analytics concepts. Choose your preferred software for teaching concepts. Solutions to problems and cases save grading time and provide students with practice. Updates cover topics beyond the traditional quantitative concepts such as data wrangling, data visualization, machine learning and AI, which are increasingly important in today's analytical problem solving. MindTap and WebAssign customizable online learning platforms offer an interactive eBook, auto-graded exercises, algorithmic practice problems and Exploring Analytics visualizations to strengthen students' understanding.

Practical, relevant problems across functional business areas provide ample practice opportunities at various difficulty levels, helping students master concepts and skills.

Analytics in Action features in each chapter show how professionals use analytics in real-world settings across healthcare, finance, marketing and manufacturing.

Online DATAfiles and MODELfiles save time, offering downloadable data, Excel models and R/Python scripts for hands-on practice with machine learning tools.

Table of contents

Chapters

Cover Page
Title Page
Copyright Page
About the Authors
Preface
Acknowledgments
Chapter 1. Introduction to Business Analytics*Preview this chapter
Chapter 2. Descriptive Statistics
Chapter 3. Data Visualization
Chapter 4. Data Wrangling: Data Management and Data Cleaning Strategies
Chapter 5. Probability: An Introduction to Modeling Uncertainty
Chapter 6. Unsupervised Machine Learning
Chapter 7. Statistical Inference
Chapter 8. Linear Regression
Chapter 9. Time Series Analysis and Forecasting
Chapter 10. Supervised Machine Learning: Regression Tasks
Chapter 11. Supervised Machine Learning: Classification Tasks
Chapter 12. Spreadsheet Models
Chapter 13. Monte Carlo Simulation
Chapter 14. Linear Optimization Models
Chapter 15. Integer Linear Optimization Models
Chapter 16. Nonlinear Optimization Models
Chapter 17. Decision Analysis
Chapter 18. Artificial Intelligence
Case Problem: Capital State University Game-Day Magazines (Inventory Management, Sports)
Case Problem: Hanover Inc. (Manufacturing, Supply Chain)
Appendix A. Basics of Excel
Appendix B. Database Basics with Microsoft Access
Appendix C. Solutions to Selected Even-Numbered Problems
Appendix D. Microsoft Excel Online and Tools for Statistical Analysis
References
Show more

Chapter 1. Introduction to Business AnalyticsPreview this chapter ↗

1.1
Business Analytics and Decision Making
Preview this section ↗
1.1.1. A Structured Process for Decision Making Using Business Analytics ↗
1.2
Using Analytics for Improved Decision Making and Problem Solving
Preview this section ↗
1.2.1. Descriptive Analytics ↗
1.2.2. Predictive Analytics ↗
1.2.3. Prescriptive Analytics ↗
1.3
Big Data
Preview this section ↗
1.3.1. Volume ↗
1.3.2. Velocity ↗
1.3.3. Variety ↗
1.3.4. Veracity ↗
1.4
Artificial Intelligence
Preview this section ↗
Google Manages Traffic Flow ↗
1.5
Business Analytics in Practice
Preview this section ↗
1.5.1. Accounting Analytics ↗
1.5.2. Financial Analytics ↗
1.5.3. Human Resource (HR) Analytics ↗
1.5.4. Marketing Analytics ↗
1.5.5. Healthcare Analytics ↗
1.5.6. Supply Chain Analytics ↗
1.5.7. Analytics for Government and Nonprofits ↗
1.5.8. Sports Analytics ↗
1.5.9. Web Analytics ↗
1.6
Legal and Ethical Issues in the Use of Data and Analytics ↗
Summary ↗
Glossary ↗
Conceptual Problems ↗
Appendix. Getting Started with R and RStudio ↗
Appendix. Basic Data Manipulation with R ↗
Show more

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