Intro Stats

Richard De Veaux, Paul Velleman, and David Bock wrote Intro Statswith the goal that you have as much fun reading it as they did in writing it. Maintaining a conversational, humorous, and informal writing style, this new edition engages readers 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 provide a problem-solving framework and, more importantly, a way to think through any statistics problem and present their results. New to the Fourth Editionis 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.

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® multimedia software, Data Desk® 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 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

25. Multiple Regression

25.1 Two Predictors

25.2 What Multiple Regression Coefficients Mean

25.3 The Multiple Regression Model

25.4 Multiple Regression Inference

25.5 Comparing Multiple Regression Models

Appendices

A. Answers

B. Photo Acknowledgments

C. Index

D. Tables and Selected Formulas

*Indicates an optional section

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Summary

Author Biography

Table of Contents

Supplemental Materials