Intro Stats: Pearson New International Edition

Richard D. De Veaux / Paul F. Velleman / David E. Bock  
Total pages
July 2013
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Intro Stats: Pearson New International Edition
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Richard De Veaux, Paul Velleman, and David Bock wrote Intro Stats with the goal that students and instructors have as much fun reading it as they did writing it. Maintaining a conversational, humorous, and informal writing style, this new edition engages students from the first page.


The authors focus on statistical thinking throughout the text and rely on technology for calculations. As a result, students can focus on developing their conceptual understanding. Innovative Think/Show/Tell examples give students a problem-solving framework and, more importantly, a way to think through any statistics problem and present their results.


New to the Fourth Edition is a streamlined presentation that keeps students focused on what’s most important, while including out helpful features. An updated organization divides chapters into sections, with specific learning objectives to keep students on track. A detailed table of contents assists with navigation through this new layout. Single-concept exercises complement the existing mid- to hard-level exercises for basic skill development.


This text is also available with MyStatLab­—please see the Features section or visit for more information.


Road Map to Success—from the familiar writing style to the helpful features included at key points of each chapter, the author team gives students the tools they need to succeed.

  • Readability: the authors use a colloquial and informal style to engage students to actually read the book.
  • Where Are We Going? chapter openers give a context for the work students are about to begin within the broader course.
  • NEW! Chapter outlines have been added to each chapter opener to call out major topics.
  • What Have We Learned? summaries conclude each chapter
  • Innovative What Can Go Wrong? sections highlight the most common mistakes and misconceptions about statistics, arming students with the tools to detect statistical errors.
  • By Hand boxes break calculations down into simple steps.
  • A Reality Check asks students to think about whether their answers make sense before interpreting their results.
  • Notation Alerts appear whenever special notation is introduced.
  • Connections feature links key terms and concepts with previous discussions.


Building an Understanding—to guide students in mastering the objectives presented in each chapter, the authors consistently model examples and provide opportunities for students to test their comprehension as they progress through the course.

  • For Example appears after each important concept is introduced, applying that concept in a focused example—often with real and updated data. Many For Examples carry the discussion through the chapter.
  • Step-by-Step examples repeat the mantra of Think, Show, and Tell in every chapter. These longer, worked examples guide students through the process of analyzing a problem with the general explanation on the left and the worked-out problem on the right. They emphasize the importance of thinking about a statistics question and reporting the findings (the Tell step). The Show step contains the mechanics of calculating results. In the fourth edition, the authors have updated Think/Show/Tell Step-by-Step examples with new applications and data.
  • Just Checking questions throughout the chapter encourage students to pause and think about what they’ve just read. Just Checkinganswers are at the end of the exercise sets so students can easily assess themselves.
  • Exercises have been updated with the most recent data.Many are based on news stories and recent research articles.
  • NEW! Single-concept exercises have been added at the beginning of each exercise set so students can be sure they have a clear understanding of important topics in every section before working comprehensive exercises.


Explore real data using modern technology—this text assumes the use of technology, with little emphasis on calculating answers by hand. To help students become comfortable using statistical software and analyzing output, guidance and support are offered through in-text tools and the accompanying MyStatLab course.

  • The DVD-ROM bound into new copies of the book includes ActivStats. Occasional pointers to ActivStats activities have been included when they parallel discussions in the book.
  • Data: Many examples and exercises are based on real data. Datasets are included on the DVD-ROM in multiple formats.
  • On the Computer—at the end of applicable chapters, the authors summarize technology steps for the most common statistical packages. Specific guidance is included for several of the most common packages (Data Desk, Excel®, JMP®, Minitab®, R®, SPSS®, StatCrunch®, and TI-83/84 Plus).
  • MyStatLab online course management system provides engaging experiences that personalize, stimulate, and measure learning for each student. In addition to the resources below, each course includes a full interactive online version of the accompanying textbook.
    • StatCrunch: MyStatLab integrates the web-based statistical software, StatCrunch, within the online assessment platform so that students can easily analyze data sets from exercises and the text. In addition, MyStatLab includes access to
    • Statistical Software Support: Knowing that students often use external statistical software, we make it easy to copy our data sets, both from the ebook and the MyStatLab questions, into software such as StatCrunch, Minitab, Excel, and more. Students have access to a variety of support tools—Technology Tutorial Videos, Technology Study Cards, and Technology Manuals for select titles—to learn how to effectively use most statistical software.

New to this Edition

  • Revised sections throughout the book are clearer and more interesting for readers.
  • Rewritten examples with real and updated data open many chapters, motivating students to delve into the analyses that follow.
  • A number of new organizational features make it even easier for students to connect the concepts
    • Section heads are reorganized and reworded to be clearer and more specific.
    • Chapter study materials now include Learning Objectives as well as key terms.
    • Single-concept exercises have been added for each major section to assess students’ knowledge of the chapter’s basic concepts.
    • A redesigned text layout clarifies the purpose of each element.
  • The content has been reorganized to shorten the book from 27 to 23 chapters. Each chapter is still a focused discussion. Topics have been combined that are conceptually similar to reduce time spent on secondary topics.
  • StatTalk Videos: 24 Conceptual Videos to Help You Actually Understand Statistics. Fun-loving statistician Andrew Vickers takes to the streets of Brooklyn, NY to demonstrate important statistical concepts through interesting stories and real-life events. These fun and engaging videos, available through the accompanying MyStatLab course, will help students actually understand statistical concepts. Available with an instructors user guide and assessment questions.



Content changes:

  • Chapter 1 now gets down to business immediately rather than just providing an introduction to the book’s features.
  • The discussion of Randomness is now in Chapter 9—two chapters earlier than the previous edition.
  • The discussions of probability and random variables are more concise—and a chapter shorter.
  • The discussion of inference for means is now earlier. Although the discussion still opens with inference for proportions (for reasons explained in the Instructors Resource Guide), it now turns immediately to inference for means so students can see the methods side-by-side. Students can then also see that the reasoning is really the same.
  • The discussion of paired samples and blocks is also earlier as it builds naturally on inference for means.
  • Most exercises that use real data have been updated.


Table of Contents


Index of Applications


Part I. Exploring and Understanding Data


1. Stats Starts Here!

1.1 What Is Statistics?

1.2 Data

1.3 Variables


2. Displaying and Describing Categorical Data

2.1 Summarizing and Displaying a Single Categorical Variable

2.2 Exploring the Relationship Between Two Categorical Variables


3. Displaying and Summarizing Quantitative Data

3.1 Displaying Quantitative Variables

3.2 Shape

3.3 Center

3.4 Spread

3.5 Boxplots and 5-Number Summaries

3.6 The Center of Symmetric Distributions: The Mean

3.7 The Spread of Symmetric Distributions: The Standard Deviation

3.8 Summary—What to Tell About a Quantitative Variable


4. Understanding and Comparing Distributions

4.1 Comparing Groups with Histograms

4.2 Comparing Groups with Boxplots

4.3 Outliers

4.4 Timeplots: Order, Please!

4.5 Re-expressing Data: A First Look


5. The Standard Deviation as a Ruler and the Normal Model

5.1 Standardizing with z-Scores

5.2 Shifting and Scaling

5.3 Normal Models

5.4 Finding Normal Percentiles

5.5 Normal Probability Plots


Review of Part I: Exploring and Understanding Data


Part II. Exploring Relationships Between Variables


6. Scatterplots, Association, and Correlation

6.1 Scatterplots

6.2 Correlation

6.3 Warning: Correlation ≠ Causation

6.4 Straightening Scatterplots


7. 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


8. 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


Review of Part II: Exploring Relationships Between Variables


Part III. Gathering Data


9. Understanding Randomness

9.1 What is Randomness?

9.2 Simulating By Hand


10. 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


11. Experiments and Observational Studies

11.1 Observational Studies

11.2 Randomized, Comparative Experiments

11.3 The Four Principles of Experimental Design

11.4 Control Treatments

11.5 Blocking

11.6 Confounding


Review of Part III: Gathering Data


Part IV. Randomness and Probability


12. From Randomness to Probability

12.1 Random Phenomena

12.2 Modeling Probability

12.3 Formal Probability


13. Probability Rules!

13.1 The General Addition Rule

13.2   Conditional Probability and the General Multiplication Rule

13.3 Independence

13.4 Picturing Probability: Tables, Venn Diagrams and Trees

13.5 Reversing the Conditioning and Bayes' Rule


14. Random Variables and Probability Models

14.1 Expected Value: Center

14.2 Standard Deviation

14.3 Combining Random Variables

14.4 The Binomial Model

14.5 Modeling the Binomial with a Normal Model

*14.6 The Poisson Model

14.7 Continuous Random Variables


Review of Part IV: Randomness and Probability


Part V. From the Data at Hand to the World at Large


15. Sampling Distribution Models

15.1  Sampling Distribution of a Proportion

15.2 When Does the Normal Model Work? Assumptions and Conditions

15.3 The Sampling Distribution of Other Statistics

15.4 The Central Limit Theorem: The Fundamental Theorem of Statistics

15.5 Sampling Distributions: A Summary


16. Confidence Intervals for Proportions

16.1 A Confidence Interval

16.2 Interpreting Confidence Intervals: What Does 95% Confidence Really Mean?

16.3 Margin of Error: Certainty vs. Precision

16.4 Assumptions and Conditions


17. Testing Hypotheses About Proportions

17.1 Hypotheses

17.2 P-Values

17.3 The Reasoning of Hypothesis Testing

17.4 Alternative Alternatives

17.5 P-Values and Decisions: What to Tell About a Hypothesis Test


18. Inferences About Means

18.1: Getting Started: The Central Limit Theorem (Again)

18.2: Gosset's t

18.3 Interpreting Confidence Intervals

18.4 A Hypothesis Test for the Mean

18.5 Choosing the Sample Size


19. More About Tests and Intervals

19.1 Choosing Hypotheses

19.2 How to Think About P Values

19.3 Alpha Levels

19.4 Practical vs. Statistical Significance

19.5 Critical Values Again

19.6 Errors

19.7 Power


Review of Part V: From the Data at Hand to the World at Large


Part VI. Learning About the World


20. Comparing Groups

20.1 The Variance of a Difference

20.2 The Standard Deviation of the Difference Between Two Proportions

20.3 Assumptions and Conditions for Comparing Proportions

20.4 The Sampling Distribution of the Difference between Two Proportions

20.5 Comparing Two Means

20.6 The Two-Sample t-Test: Testing for the Difference Between Two Means

20.7 The Two Sample z-Test: Testing for the Difference between Proportions

20.8 The Pooled t-Test: Everyone into the Pool?

20.9 Pooling


21. Paired Samples and Blocks

21.1 Paired Data

21.2 Assumptions and Conditions

21.3 Confidence Intervals for Matched Pairs

21.4 Blocking


22. Comparing Counts

22.1 Goodness-of-Fit Tests

22.2 Chi-Square Test of Homogeneity

22.3 Examining the Residuals

22.4 Chi-Square Test of Independence


23. Inferences for Regression

23.1 The Population and the Sample

23.2 Assumptions and Conditions

23.3 Intuition About Regression Inference

23.4 Regression Inference

23.5 Standard Errors for Predicted Values

23.6 Confidence Intervals for

Predicted Values

*23.7 Logistic Regression


Review of Part VI: Learning About the World


Part VII. Inference When Variables Are Related


24. Analysis of Variance

24.1 Testing Whether the Means of Several Groups Are Equal

24.2 The ANOVA Table

24.3 Plot the Data…

24.4 Comparing Means



A. Answers

B. Photo Acknowledgments

C. Index

D. Tables and Selected Formulas


*Indicates an optional section