Business Statistics, 4th edition
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Hallmark features of this title
- 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.
Published by Pearson (May 24th 2023) - Copyright © 2023
Subject: Advanced Statistics
Category: Introduction to Business Statistics
Table of contents
PART I: EXPLORING AND COLLECTING DATA
- Data and Decisions (H&M)
- Visualizing and Describing Categorical Data (Dalia Research)
- Describing, Displaying, and Visualizing Quantitative Data (AIG)
- Correlation and Linear Regression (Zillow.com)
PART II: MODELING AND PROBABILITY
- Randomness and Probability (Credit Reports, the Fair Isaacs Corporation, and Equifax)
- Random Variables and Probability Models (Metropolitan Life Insurance Company)
- The Normal and Other Continuous Distributions (The NYSE)
PART III: GATHERING DATA
- Data Sources: Observational Studies and Surveys (Roper Polls)
- Data Sources: Experiments (Capital One)
PART IV: INFERENCE FOR DECISION MAKING
- Sampling Distributions and Confidence Intervals for Proportions (Marketing Credit Cards: The MBNA Story)
- Confidence Intervals for Means (Guinness & Co.)
- Testing Hypotheses (Casting Ingots)
- More About Tests and Intervals (Traveler’s Insurance)
- Comparing Two Means (Visa Global Organization)
- Inference for Counts: Chi-Square Tests (SAC Capital)
PART V: MODELS FOR DECISION MAKING
- Inference for Regression (Nambé Mills)
- Understanding Residuals (Kellogg’s)
- Multiple Regression (Zillow.com)
- Building Multiple Regression Models (Bolliger and Mabillard)
- Time Series Analysis (Whole Foods Market®)
PART VI: ANALYTICS
- Introduction to Big Data and Data Mining (Paralyzed Veterans of America)
PART VII: ONLINE TOPICS
- Quality Control (Sony)
- Nonparametric Methods (i4cp)
- Decision Making and Risk (Data Description, Inc.)
- Analysis of Experiments and Observational Studies
Appendix A. Answers
Appendix B. Tables and Selected Formulas
Appendix C. Credits
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