Narrows the gap between theory and practice Business Statistics focuses on relevant statistical methods, empowering business students to make good, data-driven decisions.
Using the latest GAISE (Guidelines for Assessment and Instruction in Statistics Education) report, which included extensive revisions to reflect both the evolution of technology and new wisdom on statistics education, this edition brings a modern edge to teaching business statistics. This includes a focus on the report's key recommendations: teaching statistical thinking, focusing on conceptual understanding, integrating real data with a context and a purpose, fostering active learning, using technology to explore concepts and analyse data, and using assessments to improve and evaluate student learning. By presenting statistics in the context of real-world businesses and by emphasising analysis and understanding over computation, this book helps students be more analytical, prepares them to make better business decisions, and shows them how to effectively communicate results.
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By:
Norean Sharpe,
Richard De Veaux,
Paul Velleman,
Paul Velleman,
Paul Velleman
Imprint: Pearson Education Limited
Country of Publication: United Kingdom
Edition: 4th edition
Dimensions:
Height: 274mm,
Width: 216mm,
Spine: 28mm
Weight: 1.760kg
ISBN: 9781292269313
ISBN 10: 1292269316
Pages: 904
Publication Date: 13 October 2020
Audience:
Professional and scholarly
,
Undergraduate
Format: Paperback
Publisher's Status: Active
PART I: EXPLORING AND COLLECTING DATA 1. Data and Decisions (H&M) 2. Visualizing and Describing Categorical Data (Dalia Research) 3. Describing, Displaying, and Visualizing Quantitative Data (AIG) 4. Correlation and Linear Regression (Zillow.com) PART II: MODELING AND PROBABILITY 5. Randomness and Probability (Credit Reports, the Fair Isaacs Corporation, and Equifax) 6. Random Variables and Probability Models (Metropolitan Life Insurance Company) 7. The Normal and Other Continuous Distributions (The NYSE) PART III: GATHERING DATA 8. Data Sources: Observational Studies and Surveys (Roper Polls) 9. Data Sources: Experiments (Capital One) PART IV: INFERENCE FOR DECISION MAKING 10. Sampling Distributions and Confidence Intervals for Proportions (Marketing Credit Cards: The MBNA Story) 11. Confidence Intervals for Means (Guinness & Co.) 12. Testing Hypotheses (Casting Ingots) 13. More About Tests and Intervals (Traveler's Insurance) 14. Comparing Two Means (Visa Global Organization) 15. Inference for Counts: Chi-Square Tests (SAC Capital) PART V: MODELS FOR DECISION MAKING 16. Inference for Regression (Nambé Mills) 17. Understanding Residuals (Kellogg's) 18. Multiple Regression (Zillow.com) 19. Building Multiple Regression Models (Bolliger and Mabillard) 20. Time Series Analysis (Whole Foods Market) PART VI: ANALYTICS 21. Introduction to Big Data and Data Mining (Paralyzed Veterans of America) PART VII: ONLINE TOPICS 22. Quality Control (Sony) 23. Nonparametric Methods (i4cp) 24. Decision Making and Risk (Data Description, Inc.) 25. Analysis of Experiments and Observational Studies Appendix A. Answers Appendix B. Tables and Selected Formulas Appendix C. Credits
