Bookstore

Richard De Veaux, Paul Velleman, and David Bock wrote Intro Stats with 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 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.

Intro Stats

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 Modelin