Intro Stats

Dick De Veaux (Williams College) is an award-winning teacher and consultant to major corporations. His real-world experiences and anecdotes illustrate many of the chapters. Dick has taught business students at Wharton, engineering students at Princeton, and liberal arts students at Williams. Dick was named the 2008 Mosteller Statistician of the Year, awarded by the Boston chapter of the American Statistical Association for exceptional contributions to the field of statistics and outstanding service to the statistical community. To learn more, please go to: http://www.williams.edu/admin/news/releases/1624/. Paul Velleman (Cornell University) is the only statistician to win the EDUCAUSE award for innovating technology for learning. The developer of ActivStats (R) multimedia software, Data Desk (R) statistics software, and the DASL online archive of teaching datasets, his understanding of using and teaching with technology informs much of the book's approach. David Bock (Cornell University) won awards as a high school teacher of AP calculus and statistics and was a grader for the AP Statistics program from its inception. He is now the chief extension officer for the Cornell University mathematics department in charge of outreach to K-12 teachers. Dave's wisdom about how students learn helps to shape the book's pedagogy.

Preface

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 SummaryWhat 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 R2The 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