Intro Stats: Pearson New International Edition

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.

Table of Contents:
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