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Full Description
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.
Contents
Preface Index of ApplicationsPart I. Exploring and Understanding Data1. Stats Starts Here!1.1 What Is Statistics?1.2 Data1.3 Variables2. Displaying and Describing Categorical Data 2.1 Summarizing and Displaying a Single Categorical Variable2.2 Exploring the Relationship Between Two Categorical Variables3. Displaying and Summarizing Quantitative Data3.1 Displaying Quantitative Variables3.2 Shape3.3 Center3.4 Spread3.5 Boxplots and 5-Number Summaries3.6 The Center of Symmetric Distributions: The Mean3.7 The Spread of Symmetric Distributions: The Standard Deviation3.8 Summary-What to Tell About a Quantitative Variable4. Understanding and Comparing Distributions4.1 Comparing Groups with Histograms4.2 Comparing Groups with Boxplots4.3 Outliers4.4 Timeplots: Order, Please!4.5 Re-expressing Data: A First Look5. The Standard Deviation as a Ruler and the Normal Model5.1 Standardizing with z-Scores5.2 Shifting and Scaling 5.3 Normal Models5.4 Finding Normal Percentiles5.5 Normal Probability PlotsReview of Part I: Exploring and Understanding DataPart II. Exploring Relationships Between Variables6. Scatterplots, Association, and Correlation6.1 Scatterplots6.2 Correlation6.3 Warning: Correlation Causation6.4 Straightening Scatterplots 7. Linear Regression7.1 Least Squares: The Line of "Best Fit"7.2 The Linear Model7.3 Finding the Least Squares Line7.4 Regression to the Mean7.5 Examining the Residuals7.6 R2-The Variation Accounted for by the Model7.7 Regression Assumptions and Conditions8. Regression Wisdom8.1 Examining Residuals8.2 Extrapolation: Reaching Beyond the Data8.3 Outliers, Leverage, and Influence8.4 Lurking Variables and Causation8.5 Working with Summary ValuesReview of Part II: Exploring Relationships Between VariablesPart III. Gathering Data9. Understanding Randomness9.1 What is Randomness?9.2 Simulating By Hand10. Sample Surveys10.1 The Three Big Ideas of Sampling10.2 Populations and Parameters10.3 Simple Random Samples10.4 Other Sampling Designs10.5 From the Population to the Sample: You Can't Always Get What You Want10.6 The Valid Survey10.7 Common Sampling Mistakes, or How to Sample Badly11. Experiments and Observational Studies11.1 Observational Studies11.2 Randomized, Comparative Experiments11.3 The Four Principles of Experimental Design11.4 Control Treatments11.5 Blocking11.6 ConfoundingReview of Part III: Gathering DataPart IV. Randomness and Probability12. From Randomness to Probability12.1 Random Phenomena12.2 Modeling Probability12.3 Formal Probability13. Probability Rules!13.1 The General Addition Rule13.2 Conditional Probability and the General Multiplication Rule13.3 Independence13.4 Picturing Probability: Tables, Venn Diagrams and Trees13.5 Reversing the Conditioning and Bayes' Rule14. Random Variables and Probability Models14.1 Expected Value: Center14.2 Standard Deviation14.3 Combining Random Variables14.4 The Binomial Model14.5 Modeling the Binomial with a Normal Model*14.6 The Poisson Model14.7 Continuous Random VariablesReview of Part IV: Randomness and ProbabilityPart V. From the Data at Hand to the World at Large15. Sampling Distribution Models15.1 Sampling Distribution of a Proportion15.2 When Does the Normal Model Work? Assumptions and Conditions15.3 The Sampling Distribution of Other Statistics15.4 The Central Limit Theorem: The Fundamental Theorem of Statistics15.5 Sampling Distributions: A Summary16. Confidence Intervals for Proportions16.1 A Confidence Interval16.2 Interpreting Confidence Intervals: What Does 95% Confidence Really Mean?16.3 Margin of Error: Certainty vs. Precision16.4 Assumptions and Conditions17. Testing Hypotheses About Proportions17.1 Hypotheses17.2 P-Values17.3 The Reasoning of Hypothesis Testing17.4 Alternative Alternatives17.5 P-Values and Decisions: What to Tell About a Hypothesis Test18. Inferences About Means18.1: Getting Started: The Central Limit Theorem (Again)18.2: Gosset's t18.3 Interpreting Confidence Intervals18.4 A Hypothesis Test for the Mean18.5 Choosing the Sample Size19. More About Tests and Intervals19.1 Choosing Hypotheses19.2 How to Think About P Values19.3 Alpha Levels19.4 Practical vs. Statistical Significance19.5 Critical Values Again19.6 Errors19.7 PowerReview of Part V: From the Data at Hand to the World at LargePart VI. Learning About the World20. Comparing Groups20.1 The Variance of a Difference20.2 The Standard Deviation of the Difference Between Two Proportions20.3 Assumptions and Conditions for Comparing Proportions20.4 The Sampling Distribution of the Difference between Two Proportions20.5 Comparing Two Means20.6 The Two-Sample t-Test: Testing for the Difference Between Two Means20.7 The Two Sample z-Test: Testing for the Difference between Proportions20.8 The Pooled t-Test: Everyone into the Pool?20.9 Pooling21. Paired Samples and Blocks21.1 Paired Data21.2 Assumptions and Conditions21.3 Confidence Intervals for Matched Pairs21.4 Blocking22. Comparing Counts22.1 Goodness-of-Fit Tests22.2 Chi-Square Test of Homogeneity22.3 Examining the Residuals22.4 Chi-Square Test of Independence23. Inferences for Regression23.1 The Population and the Sample23.2 Assumptions and Conditions23.3 Intuition About Regression Inference23.4 Regression Inference23.5 Standard Errors for Predicted Values23.6 Confidence Intervals forPredicted Values*23.7 Logistic RegressionReview of Part VI: Learning About the WorldPart VII. Inference When Variables Are Related24. Analysis of Variance24.1 Testing Whether the Means of Several Groups Are Equal24.2 The ANOVA Table24.3 Plot the Data...24.4 Comparing Means25. Multiple Regression25.1 Two Predictors25.2 What Multiple Regression Coefficients Mean25.3 The Multiple Regression Model25.4 Multiple Regression Inference25.5 Comparing Multiple Regression ModelsAppendicesA. AnswersB. Photo Acknowledgments C. Index D. Tables and Selected Formulas *Indicates an optional section
