Statistics for Business: Decision making and analysis

The 3rd Edition of statistics for business: decision making and analysis emphasizes an application-based approach, in which students learn how to work with data to make decisions. In this contemporary presentation of business Statistics, students learn how to approach business decisions through a 4m analytics decision making strategy–motivation, method, mechanics and message–to better understand how a business context motivates the statistical process and how the results inform a course of action. Each Chapter includes hints on using Excel, Minitab express, and JMP for calculations, pointing the student in the right direction to get started with analysis of data. features: 1. Each Chapter opens with a motivating business example that frames a question and motivates the contents of the Chapter the authors return to the example throughout the Chapter, as the statistical methods are presented and provides answers to the question posed in the opening example. 2. 4-M analytics examples (motivation, method, mechanics, message) provide a consistent methodology used for worked-out examples. This approach gives students a consistent structure for solving problems and presenting their findings in the appropriate context. 3. Each Chapter includes software hints on using Excel, Minitab, and JMP for calculations and to generate graphs. These hints give students a jumping off point for getting started doing statistical analysis with software. 4. Statistics in action case studies follow each of the four parts of the book. Each case provides an in-depth look at a business application of statistics, uses real data, and takes students through the details of using that data to address a business question. table of Contents: I. Variation 1. Introduction 2. Data 3. Describing categorical data 4. Describing numerical data 5. Association between categorical variables 6. Association between Quantitative variables II. Probability 7. Probability 8. conditional probability 9. Random variables 10. Association between random variables 11. Probability models for counts 12. The normal probability model III. Inference 13. Samples and surveys 14. Sampling variation and quality 15. Confidence intervals 16. Statistical tests 17. Comparison 18. Inference for counts IV. Regression models 19. Linear patterns 20. Curved patterns 21. The simple regression model 22. Regression diagnostics 23. Multiple regression 24. Building regression models 25. Categorical explanatory variables 26. Analysis of variance 27. Time series.