Stats: Modeling the World, 5th edition

  • David E. Bock
  • Paul F. Velleman
  • Richard D. De Veaux
  • Floyd Bullard

Stats: Modeling the World

ISBN-13:  9780134685762

Hardcover
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  • Hardcover

    You'll get a bound printed text.

Overview

For courses in Introductory Statistics (algebra-based).

 

Gets students thinking statistically from the start

Stats: Modeling the World by Bock, Velleman, DeVeaux, and Bullard engages students by opening with practical data analysis and graphics. Chapters begin with new, motivating examples and follow through with analyses of the data; then, real-world examples provide a basis for sample problems and exercises.


The authors’ colloquial, approachable writing style reads unlike any other statistics text. Their signature Think, Show, and Tell problem-solving method guides students through learning what we can find in data, why we find it interesting, and how to report it to others. Focused lessons are shorter than in most other texts, and are more manageable for both students and instructors. The 5th Edition extends the innovations of this acclaimed text, with important revisions and additions that support the teaching of Statistics and statistical thinking as it is practiced today. 


Also available with MyLab Statistics

By combining trusted author content with digital tools and a flexible platform, MyLab Statistics personalizes the learning experience and improves results for each student. With MyLab Statistics and StatCrunch®, an integrated web-based statistical software program, students learn the skills they need to interact with data in the real world.


Note: You are purchasing a standalone product; MyLab Statistics does not come packaged with this content. Students, if interested in purchasing this title with MyLab Statistics, ask your instructor to confirm the correct package ISBN and Course ID. Instructors, contact your Pearson representative for more information.


If you would like to purchase both the physical text and MyLab Statistics, search for:


0135168473 / 9780135168479  Stats: Modeling the World Plus MyLab Statistics with Pearson eText - Access Card Package, 5/e

Package consists of:

  • 0134685768 / 9780134685762  Stats: Modeling the World, 5/e
  • 0134782917 / 9780134782911  MyLab Statistics with Pearson eText - Standalone Access Card - for Stats: Modeling the World

 

Table of contents

  • I. EXPLORING AND UNDERSTANDING DATA
  • 1. Stats Starts Here 
  • 2. Displaying and Describing Categorical Data 
  • 3. Displaying and Summarizing Quantitative Data 
  • 4. Understanding and Comparing Distributions 
  • 5. The Standard Deviation as a Ruler and the Normal Model 
  • Review of Part I: Exploring and Understanding Data
  • II. EXPLORING RELATIONSHIPS BETWEEN VARIABLES
  • 6. Scatterplots, Association, and Correlation 
  • 7. Linear Regression 
  • 8. Regression Wisdom 
  • 9. Re-expressing Data: Get It Straight! 
  • Review of Part II: Exploring Relationships Between Variables
  • III. GATHERING DATA
  • 10. Understanding Randomness 
  • 11. Sample Surveys 
  • 12. Experiments and Observational Studies 
  • Review of Part III: Gathering Data
  • IV. RANDOMNESS AND PROBABILITY
  • 13. From Randomness to Probability 
  • 14. Probability Rules! 
  • 15. Random Variables 
  • 16. Probability Models 
  • Review of Part IV: Randomness and Probability
  • V. FROM THE DATA AT HAND TO THE WORLD AT LARGE
  • 17. Sampling Distribution Models 
  • 18. Confidence Intervals for Proportions 
  • 19. Testing Hypotheses About Proportions 
  • 20. More About Tests and Intervals 
  • 21. Comparing Two Proportions 
  • Review of Part V: From the Data at Hand to the World at Large
  • VI. LEARNING ABOUT THE WORLD
  • 22. Inferences About Means 
  • 23. Comparing Means 
  • 24. Paired Samples and Blocks 
  • Review of Part VI: Learning About the World
  • VII. INFERENCE WHEN VARIABLES ARE RELATED
  • 25. Comparing Counts 
  • 26. Inferences for Regression 
  • Review of Part VII: Inference When Variables Are Related 
  • 27. Analysi

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Published by Pearson (May 4th 2018) - Copyright © 2019