- Richard D. De Veaux |
- Paul F. Velleman |
- David E. Bock |
Title overview
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.
Hallmark features of this title
- Where Are We Going? chapter openers give context for the work students are about to begin within the broader course.
- Reality Checks ask students to think about whether their answers make sense before interpreting their results.
- Notation Alerts appear whenever special notation is introduced.
- The Tech Support section provides instructions for applying the topics covered by the chapter within each of the supported statistics packages.
- Focused examples are provided as each important concept is introduced, applying the concept usually with real, up-to-the-minute data.
- Just Checking questions are quick checks throughout the chapter that involve minimal calculation and encourage students to pause and think about what they've just read.
New and updated features of this title
- Random Matters: This new feature encourages a gradual, cumulative understanding of randomization.
- Streamlined coverage of descriptive statistics helps students progress more quickly through the first part of the book.
- For 2 of the most difficult concepts in the introductory course, technology is utilized to improve learning: the idea of a sampling distribution and the reasoning of statistical inference.
- A third variable is introduced with contingency tables and mosaic plots in Chapter 3 to give students earlier experience with multivariable thinking. Then, following the discussion of correlation and regression as a tool (without inference) in Chapters 6, 7 and 8, multiple regression is introduced in Chapter 9.
- Expanded and revised Think/Show/Tell Step-by-Step Examples guide students through the process of analyzing a problem through worked examples.
- New Web tools provide interactive versions of the distribution tables at the back of the book, and tools for randomization inference methods such as the bootstrap and for repeated sampling from larger populations now can be found online.
Key features
Features of MyLab Statistics for the 5th Edition
- StatCrunch® Projects provide opportunities for students to explore data beyond the classroom. In each project, students analyze a large data set in StatCrunch and answer corresponding, assignable questions for immediate feedback. StatCrunch Projects span the entire curriculum or focus on certain key concepts. Questions from each project can also be assigned individually.
- MyLab Statistics exercises are newly mapped to improve student learning outcomes. Homework reinforces and supports students' understanding of key statistics topics.
- Updated Think/Show/Tell Step-by-Step Example videos guide students through the process of analyzing a problem using the “Think, Show, and Tell” strategy from the textbook.
- Simulation Applets use technology to help students learn and visualize a wide range of topics covered in introductory statistics.
- Learning Catalytics is a student response tool that uses students' smartphones, tablets, or laptops to engage them in more interactive tasks and thinking. It helps to foster student engagement and peer-to-peer learning, generate class discussion, and guide lectures with real-time analytics. Now access pre-built exercises created by leading Pearson authors.
Table of contents
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
II. EXPLORING RELATIONSHIPS BETWEEN VARIABLES
- 6. Scatterplots, Association, and Correlation
- 7. Linear Regression
- 8. Regression Wisdom
- 9. Multiple Regression
III. GATHERING DATA
- 10. Sample Surveys
- 11. Experiments and Observational Studies
IV. RANDOMNESS AND PROBABILITY
- 12. From Randomness to Probability
- 13.Probability Rules!
- 14. Random Variables
- 15. Probability Models
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
VI. INFERENCE FOR RELATIONSHIPS
- 20. Comparing Groups
- 21. Paired Samples and Blocks
- 22. Comparing Counts
- 23. Inferences for Regression
VII. INFERENCE WHEN VARIABLES ARE RELATED
- 24. Multiple Regression Wisdom
- 25. Analysis of Variance
- 26. Multifactor Analysis of Variance
- 27. Statistics and Data Science
Author bios
About our authors
Richard D. De Veaux is an internationally known educator and consultant. He has taught at the Wharton School and the Princeton University School of Engineering, where he won a Lifetime Award for Dedication and Excellence in Teaching. He is the C. Carlisle and M. Tippit Professor of Statistics at Williams College, where he has taught since 1994. Dick has won both the Wilcoxon and Shewell awards from the American Society for Quality. He is a fellow of the American Statistical Association (ASA) and an elected member of the International Statistical Institute (ISI). In 2008, he was named Statistician of the Year by the Boston Chapter of the ASA, and was the 2018-2021 Vice-President of the ASA. Dick is also well known in industry, where for more than 30 years he has consulted for such Fortune 500 companies as American Express, Hewlett-Packard, Alcoa, DuPont, Pillsbury, General Electric, and Chemical Bank. Because he consulted with Mickey Hart on his book Planet Drum, he has also sometimes been called the "Official Statistician for the Grateful Dead." His real-world experiences and anecdotes illustrate many of this book's chapters.
Dick holds degrees from Princeton University in Civil Engineering (B.S.E.) and Mathematics (A.B.) and from Stanford University in Dance Education (M.A.) and Statistics (Ph.D.), where he studied dance with Inga Weiss and Statistics with Persi Diaconis. His research focuses on the analysis of large data sets and data mining in science and industry.
In his spare time, he is an avid cyclist and swimmer. He also is the founder of the "Diminished Faculty," an a cappella Doo-Wop quartet at Williams College, and sings bass in the college concert choir and with the Choeur Vittoria of Paris. Dick is the father of 4 children.
Paul F. Velleman has an international reputation for innovative Statistics education. He is the author and designer of the multimedia Statistics program ActivStats, for which he was awarded the EDUCOM Medal for innovative uses of computers in teaching statistics, and the ICTCM Award for Innovation in Using Technology in College Mathematics. He also developed the award-winning statistics program Data Desk, the Internet site Data and Story Library (DASL) which provides data sets for teaching Statistics, and the tools referenced in the text for simulation and bootstrapping. Paul's understanding of using and teaching with technology informs much of this book's approach.
Paul taught Statistics at Cornell University, where he was awarded the MacIntyre Award for Exemplary Teaching. He is Emeritus Professor of Statistical Science from Cornell and lives in Maine with his wife, Sue Michlovitz. He holds an A.B. from Dartmouth College in Mathematics and Social Science, and M.S. and Ph.D. degrees in Statistics from Princeton University, where he studied with John Tukey. His research often deals with statistical graphics and data analysis methods. Paul co-authored (with David Hoaglin) ABCs of Exploratory Data Analysis. Paul is a Fellow of the American Statistical Association and of the American Association for the Advancement of Science. Paul is the father of 2 boys. In his spare time he sings with the acapella group VoXX and studies tai chi.
David E. Bock taught mathematics at Ithaca High School for 35 years. He has taught Statistics at Ithaca High School, Tompkins-Cortland Community College, Ithaca College, and Cornell University. Dave has won numerous teaching awards, including the MAA's Edyth May Sliffe Award for Distinguished High School Mathematics Teaching (2 times), Cornell University's Outstanding Educator Award (3 times), and has been a finalist for New York State Teacher of the Year.
Dave holds degrees from the University at Albany in Mathematics (B.A.) and Statistics/Education (M.S.). Dave has been a reader and table leader for the AP Statistics exam and a Statistics consultant to the College Board, leading workshops and institutes for AP Statistics teachers. His understanding of how students learn informs much of this book's approach.