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Intro Stats, 6th edition

Published by Pearson (August 15, 2021) © 2022

  • Richard D. De Veaux Williams College
  • Paul F. Velleman Cornell University
  • David E. Bock Ithaca High School (Retired) , Cornell University

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For courses in Introductory Statistics.

Innovative methods, technology and humor encourage statistical thinking

Intro Stats uses inventive strategies to help students think critically about data. The use of technology and simulations to demonstrate variability at critical points makes it easier for instructors to teach and for students to understand more complex statistical concepts later in the course. The 6th Edition enriches material by augmenting the authors' signature tools for teaching about randomness, sampling distribution models and inference. Discussions of ethical issues are added throughout, and a new student project after each chapter can be used for collaborative work.

Hallmark features of this title

  • Where Are We Going? chapter openers provide context for the material to be covered within the broader course.
  • Think, Show, Tell examples guide students through analyzing a problem using worked examples, from thinking about a statistics question (Think) to reporting findings (Tell). The Show step contains the mechanics of calculating results.
  • Reality Checks ask students to consider if their answers make sense before interpreting results.
  • Notation Alerts appear whenever special notation is introduced.
  • Focused examples appear as each key concept is introduced, usually applying the concept with real, current data.
  • Tech Support sections give instructions for applying the topics covered by the chapter within each of the supported statistics packages.

New and updated features of this title

  • Increased discussion of ethics reflects the GAISE recommendation to make ethics discussions an integral part of the course. New Ethics Matters features appear in each chapter; topics are motivated by current events and issues students will know about.
  • New Student Projects at the end of each chapter can be the basis for more extensive investigations by students working on their own, but can also support team efforts.
  • Expanded Random Matters boxes lead students through bootstrap calculations and comparing bootstrap results to classical inference. The Random Matters elements have been rewritten to provide step-by-step guidance.
  • More extensive use of special applications demonstrates properties of randomness, illustrates the concept of a sampling distribution, and offers bootstrap methods for inference. These applications can be found in MyLab Statistics.
  • Data Desk RP is a statistics program with a graphical interface that is easy to learn and use. A student version is available at datadesk.com.
    • Students beginning with the R statistics language may find it helpful to use Data Desk's ability to write out R code for plots and analyses such as those used in the text. Additionally, students accessing datasets at DASL will find a quick link to Data Desk.

Features of MyLab Statistics for the 6th Edition

  • Jittering: Jittering is widely accepted in the statistical community as changing the value of a number past the last significant digit. The authors have hand-selected data sets to include in MyLab Statistics to jitter in order to increase the use of real data in the course while still algorithmically regenerating exercises.
  • New exercises incorporate real data. MyLab Statistics exercises have been updated to include real data so students can understand the real-world implications of data analysis. Homework reinforces and supports students' understanding of key statistics topics within a real-world context.
  • Diverse and relevant data and applications: Revised Exercises throughout the text and MyLab Statistics help students understand how statistics applies inclusively to everyday life.
  • New applets aid visualization and statistical understanding. Applets are available in the Resource Library and have corresponding MyLab homework items.
  • *Indicates optional section

I: EXPLORING AND UNDERSTANDING DATA

  1. Stats Starts Here
    • 1.1 What Is Statistics?
    • 1.2 Data
    • 1.3 Variables
    • 1.4 Models
  2. Displaying and Describing Data
    • 2.1 Summarizing and Displaying a Categorical Variable
    • 2.2 Displaying a Quantitative Variable
    • 2.3 Shape
    • 2.4 Center
    • 2.5 Spread
  3. Relationships Between Categorical Variables: Contingency Tables
    • 3.1 Contingency Tables
    • 3.2 Conditional Distributions
    • 3.3 Displaying Contingency Tables
    • 3.4 Three Categorical Variables
  4. Understanding and Comparing Distributions
    • 4.1 Displays for Comparing Groups
    • 4.2 Outliers
    • 4.3 Re-Expressing Data: A First Look
  5. The Standard Deviation as a Ruler and the Normal Model
    • 5.1 Using the standard deviation to Standardize Values
    • 5.2 Shifting and Scaling
    • 5.3 Normal Models
    • 5.4 Working with Normal Percentiles
    • 5.5 Normal Probability Plots
    • Review of Part I: Exploring and Understanding Data

II: EXPLORING RELATIONSHIPS BETWEEN VARIABLES

  1. Scatterplots, Association, and Correlation
    • 6.1 Scatterplots
    • 6.2 Correlation
    • 6.3 Warning: Correlation ≠ Causation
    • 6.4 *Straightening Scatterplots
  2. Linear Regression
    • 7.1 Least Squares: The Line of "Best Fit"
    • 7.2 The Linear Model
    • 7.3 Finding the Least Squares Line
    • 7.4 Regression to the Mean
    • 7.5 Examining the Residuals
    • 7.6 R2: The Variation Accounted for by the Model
    • 7.7 Regression Assumptions and Conditions
  3. Regression Wisdom
    • 8.1 Examining Residuals
    • 8.2 Extrapolation: Reaching Beyond the Data
    • 8.3 Outliers, Leverage, and Influence
    • 8.4 Lurking Variables and Causation
    • 8.5 Working with Summary Values
    • 8.6 * Straightening Scatterplots: The Three Goals
    • 8.7 * Finding a Good Re-Expression
  4. Multiple Regression
    • 9.1 What Is Multiple Regression?
    • 9.2 Interpreting Multiple Regression Coefficients
    • 9.3 The Multiple Regression Model: Assumptions and Conditions
    • 9.4 Partial Regression Plots
    • 9.5 * Indicator Variables
    • Review of Part II: Exploring Relationships Between Variables

III: GATHERING DATA

  1. Sample Surveys
    • 10.1 The Three Big Ideas of Sampling
    • 10.2 Populations and Parameters
    • 10.3 Simple Random Samples
    • 10.4 Other Sampling Designs
    • 10.5 From the Population to the Sample: You Can't Always Get What You Want
    • 10.6 The Valid Survey
    • 10.7 Common Sampling Mistakes, or How to Sample Badly
  2. Experiments and Observational Studies
    • 11.1 Observational Studies
    • 11.2 Randomized, Comparative Experiments
    • 11.3 The Four Principles of Experimental Design
    • 11.4 Control Groups
    • 11.5 Blocking
    • 11.6 Confounding
    • Review of Part III: Gathering Data

IV: FROM THE DATA AT HAND TO THE WORLD AT LARGE

  1. From Randomness to Probability
    • 12.1 Random Phenomena
    • 12.2 Modeling Probability
    • 12.3 Formal Probability
    • 12.4 Conditional Probability and the General Multiplication Rule
    • 12.5 Independence
    • 12.6 Picturing Probability: Tables, Venn Diagrams, and Trees
    • 12.7 Reversing the Conditioning and Bayes' Rule
  2. Sampling Distributions and Confidence Intervals for Proportions
    • 13.1 The Sampling Distribution for a Proportion
    • 13.2 When Does the Normal Model Work? Assumptions and Conditions
    • 13.3 A Confidence Interval for a Proportion
    • 13.4 Interpreting Confidence Intervals: What Does 95% Confidence Really Mean?
    • 13.5 Margin of Error: Certainty vs. Precision
    • 13.6 * Choosing the Sample Size
  3. Confidence Intervals for Means
    • 14.1 The Central Limit Theorem
    • 14.2 A Confidence interval for the Mean
    • 14.3 Interpreting confidence intervals
    • 14.4 * Picking our Interval Up by our Bootstraps
    • 14.5 Thoughts about Confidence Intervals
  4. Testing Hypotheses
    • 15.1 Hypotheses
    • 15.2 P-values
    • 15.3 The Reasoning of Hypothesis Testing
    • 15.4 A Hypothesis Test for the Mean
    • 15.5 Intervals and Tests
    • 15.6 P-Values and Decisions: What to Tell About a Hypothesis Test
  5. More About Tests and Intervals
    • 16.1 Interpreting P-values
    • 16.2 Alpha Levels and Critical Values
    • 16.3 Practical vs. Statistical Significance
    • 16.4 Errors
    • Review of Part IV: From the Data at Hand to the World at Large

V: INFERENCE FOR RELATIONSHIPS

  1. Comparing Groups
    • 17.1 A Confidence Interval for the Difference Between Two Proportions
    • 17.2 Assumptions and Conditions for Comparing Proportions
    • 17.3 The Two-Sample z-Test: Testing the Difference Between Proportions
    • 17.4 A Confidence Interval for the Difference Between Two Means
    • 17.5 The Two-Sample t-Test: Testing for the Difference Between Two Means
    • 17.6 * Randomization-Based Tests and Confidence Intervals for Two Means
    • 17.7 * Pooling
    • 17.8 * The Standard Deviation of a Difference
  2. Paired Samples and Blocks
    • 18.1 Paired Data
    • 18.2 The Paired t-Test
    • 18.3 Confidence Intervals for Matched Pairs
    • 18.4 Blocking
  3. Comparing Counts
    • 19.1 Goodness-of-Fit Tests
    • 19.2 Chi-Square Tests of Homogeneity
    • 19.3 Examining the Residuals
    • 19.4 Chi-Square Test of Independence
  4. Inferences for Regression
    • 20.1 The Regression Model
    • 20.2 Assumptions and Conditions
    • 20.3 Regression Inference and Intuition
    • 20.4 The Regression Table
    • 20.5 Multiple Regression Inference
    • 20.6 Confidence and Prediction Intervals
    • 20.7 * Logistic Regression
    • 20.8 * More About Regression
    • Review of Part V: Inference for Relationships

Parts I–V Cumulative Review Exercises

Appendixes:

  1. Answers
  2. Credits
  3. Indexes
  4. Tables and Selected Formulas

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 (twice), 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.

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