Intro Stats, Global Edition

For courses in Introductory Statistics. Innovative methods, technology, and humor encourage statistical thinking Intro Stats, 6th Edition by De Veaux/Velleman/Bock uses inventive strategies to help students think critically about data, while maintaining the book's core concepts, coverage, and 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. This revision includes several enhancements, enriching material with greater use of the authors' signature tools for teaching about randomness, sampling distribution models, and inference. Current discussions of ethical issues have been added throughout, and each chapter now ends with a student project that can be used for collaborative work.

Table of Contents:
* Indicates optional section I: EXPLORING AND UNDERSTANDING DATA Stats Starts Here 1.1 What Is Statistics? 1.2 Data 1.3 Variables 1.4 Models 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 Relationships Between Categorical Variables: Contingency Tables 3.1 Contingency Tables 3.2 Conditional Distributions 3.3 Displaying Contingency Tables 3.4 Three Categorical Variables Understanding and Comparing Distributions 4.1 Displays for Comparing Groups 4.2 Outliers 4.3 Re-Expressing Data: A First Look 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 Review of Part I: Exploring and Understanding Data II: EXPLORING RELATIONSHIPS BETWEEN VARIABLES Scatterplots, Association, and Correlation 6.1 Scatterplots 6.2 Correlation 6.3 Warning: Correlation ≠ Causation 6.4 *Straightening Scatterplots 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 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 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 Review of Part II: Exploring Relationships Between Variables III: GATHERING DATA 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 Experiments and Observational Studies 11.1 Observational Studies 11.2 Randomized, Comparative Experiments 11.3 The Four Principles of Experimental Design 11.4 Control Groups 11.5 Blocking 11.6 Confounding Review of Part III: Gathering Data IV: FROM THE DATA AT HAND TO THE WORLD AT LARGE 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 and Bayes' Rule 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 the Sample Size 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 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 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 Review of Part IV: From the Data at Hand to the World at Large V: INFERENCE FOR RELATIONSHIPS 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 Paired Samples and Blocks 18.1 Paired Data 18.2 The Paired t-Test 18.3 Confidence Intervals for Matched Pairs 18.4 Blocking 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 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 20.8 * More About Regression Review of Part V: Inference for Relationships Parts I–V Cumulative Review Exercises Appendixes: Answers Credits Indexes Tables and Selected Formulas