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Practical Business Statistics, Seventh Edition , provides a conceptual, realistic, and matter-of-fact approach to managerial statistics that carefully maintains, but does not overemphasize mathematical correctness. The book provides deep understanding of how to learn from data and how to deal with uncertainty while promoting the use of practical computer applications. This valuable, accessible approach teaches present and future managers how to use and understand statistics without an overdose of technical detail, enabling them to better understand the concepts at hand and to interpret results.

The text uses excellent examples with real world data relating to business sector functional areas such as finance, accounting, and marketing. Written in an engaging style, this timely revision is class-tested and designed to help students gain a solid understanding of fundamental statistical principles without bogging them down with excess mathematical details.

Provides users with a conceptual, realistic, and matter-of-fact approach to managerial statistics
Offers an accessible approach to teach present and future managers how to use and understand statistics without an overdose of technical detail, enabling them to better understand concepts and to interpret results
Features updated examples and graphics (200+ figures) to illustrate important applied uses and current business trends
Includes robust ancillary instructional materials such as an instructor´s manual, lecture slides, and data files to save you time when preparing for class

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Part I: Introduction and Descriptive Statistics 1. Introduction: Defining the Role of Statistics in Business 2. Data Structures: Classifying the Various Types of Data Sets 3. Histograms: Looking at the Distribution of Data 4. Landmark Summaries: Interpreting Typical Values and Percentiles 5. Variability: Dealing with Diversity

Part II: Probability 6. Probability: Understanding Random Situations 7. Random Variables: Working with Uncertain Numbers

Part III: Statistical Inference 8. Random Sampling: Planning Ahead for Data Gathering 9. Confidence Intervals: Admitting That Estimates Are Not Exact 10. Hypothesis Testing: Deciding Between Reality and Coincidence

Part IV: Regression and Time Series 11. Correlation and Regression: Measuring and Predicting Relationships 12. Multiple Regression: Predicting One Variable From Several Others 13. Report Writing: Communicating the Results of a Multiple Regression 14. Time Series: Understanding Changes Over Time

Part V: Methods and Applications 15. ANOVA: Testing for Differences Among Many Samples and Much More 16. Nonparametrics: Testing with Ordinal Data or Nonnormal Distributions 17. Chi-Squared Analysis: Testing for Patterns in Qualitative Data 18. Quality Control: Recognizing and Managing Variation

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