Stats: Data and Models, Global Edition + MyLab Statistics with eText, 5th Edition

Richard De Veaux all
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Stats: Data and Models, Global Edition + MyLab Statistics with eText, 5th Edition

By Richard De Veaux, Paul Velleman, David E. Bock
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This pack contains 1 copy of Stats: Data and Models, Global Edition and 1 printed access card to MyLab Statistics

For courses in Introductory Statistics.

Encourages statistical thinking using technology, innovative methods, and a sense of humour

Inspired by the 2016 GAISE Report revision, Stats: Data and Models, 5th Edition by De Veaux, Velleman, and Bock uses innovative strategies to help students think critically about data, while maintaining the book's core concepts, coverage, and most importantly, readability.

The authors make it easier for instructors to teach and for students to understand more complicated statistical concepts later in the course (such as the Central Limit Theorem). In addition, students get more exposure to large data sets and multivariate thinking, which better prepares them to be critical consumers of statistics in the 21st century.

The 5th Edition’s approach to teaching Stats: Data and Models is revolutionary, yet it retains the book's lively tone and hallmark pedagogical features such as its Think/Show/Tell Step-by-Step Examples.

Pearson MyLab™ is the world's leading online self-study, homework, tutorial and assessment product designed with a single purpose in mind: to improve the results of all higher education students, one student at a time.

Please note: The duration of access to a MyLab is set by your instructor for your specific unit of study. To access the MyLab you need a Course ID from your instructor.


Download the detailed table of contents

Preview sample pages from Stats: Data and Models, Global Edition

  • Random Matters, a new feature, encourages a gradual, cumulative understanding of randomness. Random Matters boxes in each chapter foster inferential thinking through a variety of methods, such as drawing histograms of sample means, introducing the thinking involved in permutation tests, and encouraging judgement about how likely the observed statistic seems when viewed against the simulated sampling distribution of the null hypothesis.
  • Streamlined coverage of descriptive statistics helps students progress more quickly through the first part of the book. Random variables and probability distributions are now covered later in the text to allow for more time to be spent on the more critical statistical concepts, as per GAISE recommendations.
  • Introduction of a third variable in calculations, with the discussion of contingency tables and mosaic plots, in Chapter 3 gives students early experience with multivariable thinking
Pack items
Table of contents
  • Part I: Exploring and Understanding Data
  • 1. Stats Starts Here
  • 2. Displaying and Describing Data
  • 3. Relationships Between Categorical Variables—Contingency Tables
  • 4. Understanding and Comparing Distributions
  • 5. The Standard Deviation as a Ruler and the Normal Model
  • Part II: Exploring Relationships Between Variables
  • 6. Scatterplots, Association, and Correlation
  • 7. Linear Regression
  • 8. Regression Wisdom
  • 9. Multiple Regression
  • Part III: Gathering Data
  • 10. Sample Surveys
  • 11. Experiments and Observational Studies
  • Part IV: Randomness and Probability
  • 12. From Randomness to Probability
  • 13. Probability Rules!
  • 14. Random Variables
  • 15. Probability Models
  • Part V: Inference for One Parameter
  • 16. Sampling Distribution Models and Confidence Intervals for Proportions
  • 17. Confidence Intervals for Means
  • 18. Testing Hypotheses
  • 19. More About Tests and Intervals
  • Part VI: Inference for Relationships
  • 20. Comparing Groups
  • 21. Paired Samples and Blocks
  • 22. Comparing Counts
  • 23. Inferences for Regression
  • Part VII: Inference When Variables Are Related 24. Multiple Regression Wisdom
  • 25. Analysis of Variance
  • 26. Multifactor Analysis of Variance
  • 27. Introduction to Statistical Learning and Data Science