PART I: EXPLORING AND COLLECTING DATA

1. 1. Data and Decisions (H&M)
2. 2. Visualizing and Describing Categorical Data (Dalia Research)
3. 3. Describing, Displaying, and Visualizing Quantitative Data (AIG)
4. 4. Correlation and Linear Regression (Zillow.com)

PART II: MODELING AND PROBABILITY

1. 5. Randomness and Probability (Credit Reports, the Fair Isaacs Corporation, and Equifax)
2. 6. Random Variables and Probability Models (Metropolitan Life Insurance Company)
3. 7. The Normal and Other Continuous Distributions (The NYSE)

PART III: GATHERING DATA

1. 8. Data Sources: Observational Studies and Surveys (Roper Polls)
2. 9. Data Sources: Experiments (Capital One)

PART IV: INFERENCE FOR DECISION MAKING

1. 10. Sampling Distributions and Confidence Intervals for Proportions (Marketing Credit Cards: The MBNA Story)
2. 11. Confidence Intervals for Means (Guinness & Co.)
3. 12. Testing Hypotheses (Casting Ingots)
4. 13. More About Tests and Intervals (Traveler's Insurance)
5. 14. Comparing Two Means (Visa Global Organization)
6. 15. Inference for Counts: Chi-Square Tests (SAC Capital)

PART V: MODELS FOR DECISION MAKING

1. 16. Inference for Regression (Nambé Mills)
2. 17. Understanding Residuals (Kellogg's)
3. 18. Multiple Regression (Zillow.com)
4. 19. Building Multiple Regression Models (Bolliger and Mabillard)
5. 20. Time Series Analysis (Whole Foods Market®)

PART VI: ANALYTICS

1. 21. Introduction to Big Data and Data Mining (Paralyzed Veterans of America)

PART VII: ONLINE TOPICS

1. 22. Quality Control (Sony)
2. 23. Nonparametric Methods (i4cp)
3. 24. Decision Making and Risk (Data Description, Inc.)
4. 25. Analysis of Experiments and Observational Studies

• Emphasis on better statistical thinking trains students to apply statistics correctly.
  • Focus on checking assumptions and conditions when using statistical procedures is emphasized throughout.
  • Ethics in Action illustrate the judgment needed in statistical analysis. Questions are included for study and reflection.
  • What Can Go Wrong? equips students with the tools to detect common statistical errors and offers practice in debunking misuses of statistics.
• Emphasis on graphing and exploring data helps students uncover structures, patterns, and anomalies. This helps them to raise new questions, perform statistical analysis, and make the best business decision.
• A flexible syllabus allows instructors to select and sequence chapters to suit their needs.

• An improved organization or "data first" presentation of topics motivates students, providing a foundation in real business decisions on which to build their understanding.
• A streamlined design clarifies the purpose of each text element.
• Updated examples reflect the changing world and real-life business challenges: Chapter-Opening Vignettes present scenarios using real-world information and well-known companies such as Amazon, Zillow, Keen Inc., and Whole Foods Market, to illustrate a managerial statistical issue. End-of-Chapter Brief Cases use real data and ask students to investigate a question or make a business decision. More in-depth Case Studies provide realistically large datasets and challenge students to respond to open-ended business questions using the data.
• An increased focus on central ideas and core material: Influenced by the GAISE 2016 Report, this revision tightens discussions on statistical methods, how to apply them, and the assumptions and conditions that make them work.
• Updated exercises, applications and examples use real and recent data to tie concepts to the way statistics is used to make better business decisions.
• Updated Ethics in Action features illustrate the judgment needed in statistical analysis. Questions are included for study and reflection.