For courses in Introductory Statistics. Encourages statistical thinking using technology, innovative methods, and a sense of humor Inspired by the 2016 GAISE Report revision, Intro Stats, 5th Edition by De Veaux/Velleman/Bock uses innovative strategies to help students think critically about data, while maintaining the book’s core concepts, coverage, and most importantly, readability. By using technology and simulations to demonstrate variability at critical points throughout the course, the authors make it easier for instructors to teach and for students to understand more complicated statistical concepts later in the course (such as the Central Limit Theorem). In addition, students get more exposure to large data sets and multivariate thinking, which better prepares them to be critical consumers of statistics in the 21st century. The 5th Edition’s approach to teaching intro stats is revolutionary, yet it retains the book's lively tone and hallmark pedagogical features such as Think/Show/Tell examples. Also available with MyLab Statistics Used by nearly one million students a year, MyLab™ Statistics is the world’s leading online program for teaching and learning statistics. MyLab Statistics delivers assessment, tutorials, and multimedia resources that provide engaging and personalized experiences for each student, so learning can happen in any environment. Each course is developed to accompany Pearson’s best-selling content, authored by thought leaders across the statistics curriculum, and can be easily customized to fit any course format.

Table of Contents:
PART I: EXPLORING AND UNDERSTANDING DATA 1. Stats Starts here 1.1 What Is Statistics? 1.2. Data 1.3 Variables 1.4 Models 2. Displaying and Describing Data 2.1 Summarizing and Displaying a Categorical Variable 2.2 Displaying a Quantitative variable 2.3 Shape 2.4 Center 2.5 Spread 3. Relationships Between Categorical Variables — Contingency Tables 3.1 Contingency tables 3.2 Conditional distributions 3.3 Displaying Contingency Tables 3.4 Three Categorical Variables 4. Understanding and Comparing Distributions 4.1 Displays for Comparing Groups 4.2 Outliers 4.3 Re-Expressing Data: A First Look 5. The Standard Deviation as a Ruler and the Normal Model 5.1 Using the standard deviation to Standardize Values 5.2 Shifting and scaling 5.3 Normal models 5.4 Working with Normal Percentiles 5.5 Normal Probability Plots Part I Review 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 R 2–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 8.6 * Straightening Scatterplots–The Three Goals 8.7 * Finding a Good Re-Expression 9. Multiple Regression 9.1 What Is Multiple Regression? 9.2 Interpreting Multiple Regression Coefficients 9.3 The Multiple Regression Model–Assumptions and Conditions 9.4 Partial Regression Plots 9.5 Indicator Variables Part II Review PART III: GATHERING DATA 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 Experiment Design 11.4 Control Groups 11.5 Blocking 11.6 Confounding Part III Review PART IV  INFERENCE FOR ONE PARAMETER 12. From Randomness to Probability  12.1 Random phenomena 12.2 Modeling Probability 12.3 Formal Probability 12.4. Conditional Probability and the General Multiplication Rule 12.5 Independence 12.6 Picturing Probability: Tables, Venn Diagrams, and Trees 12.7 *Reversing the Conditioning: Bayes’ Rule 13. Sampling Distributions and Confidence Intervals for Proportions 13.1 The Sampling Distribution for a Proportion 13.2 When Does the Normal Model Work? Assumptions and Conditions 13.3 A Confidence Interval for a Proportion 13.4 Interpreting Confidence Intervals: What Does 95% Confidence Really Mean? 13.5 Margin of Error: Certainty vs. Precision  13.6 *Choosing your Sample Size 14. Confidence Intervals for Means 14.1 The Central Limit Theorem 14.2 A Confidence interval for the Mean 14.3 Interpreting confidence intervals 14.4 *Picking our Interval up by our Bootstraps 14.5 Thoughts about Confidence Intervals  15. Testing Hypotheses 15.1 Hypotheses 15.2 P-values 15.3 The Reasoning of Hypothesis Testing 15.4 A Hypothesis Test for the Mean 15.5 Intervals and Tests 15.6 P-Values and Decisions: What to Tell About a Hypothesis Test 16. More About Tests and Intervals  16.1 Interpreting P-values 16.2 Alpha Levels and Critical Values 16.3 Practical vs Statistical Significance 16.4 Errors Part IV Review PART  V: INFERENCE FOR RELATIONSHIPS 17. Comparing Groups 17.1 A Confidence Interval for the Difference Between Two Proportions 17.2 Assumptions and Conditions for Comparing Proportions 17.3 The Two-Sample z-Test: Testing the Difference Between Proportions 17.4 A Confidence Interval for the Difference Between Two Means 17.5 The Two-Sample t-Test: Testing for the Difference Between Two Means 17.6 Randomization-Based Tests and Confidence Intervals for Two Means 17.7 *Pooling 17.8 *The Standard Deviation of a Difference 18. Paired Samples and Blocks 18.1 Paired Data 18.2 Assumptions and Conditions 18.3 Confidence Intervals for Matched Pairs 18.4 Blocking 19. Comparing Counts 19.1 Goodness-of-Fit Tests 19.2 Chi-Square Tests of Homogeneity 19.3 Examining the Residuals 19.4 Chi-Square Test of Independence 20. Inferences for Regression 20.1 The Regression Model 20.2 Assumptions and Conditions 20.3 Regression Inference and Intuition 20.4 The Regression Table  20.5 Multiple Regression Inference 20.6 Confidence and Prediction Intervals 20.7 *Logistic Regression Part V Review * Indicates optional section