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Preface | |

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Index of Applications | |

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Exploring and Understanding Data | |

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Stats Starts Here! | |

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What Is Statistics? | |

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Data | |

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Variables | |

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Displaying and Describing Categorical Data | |

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Summarizing and Displaying a Single Categorical Variable | |

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Exploring the Relationship Between Two Categorical Variables | |

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Displaying and Summarizing Quantitative Data | |

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Displaying Quantitative Variables | |

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Shape | |

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Center | |

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Spread | |

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Boxplots and 5-Number Summaries | |

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The Center of Symmetric Distributions: The Mean | |

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The Spread of Symmetric Distributions: The Standard Deviation | |

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Summary-What to Tell About a Quantitative Variable | |

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Understanding and Comparing Distributions | |

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Comparing Groups with Histograms | |

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Comparing Groups with Boxplots | |

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Outliers | |

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Timeplots: Order, Please! | |

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Re-expressing Data: A First Look | |

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The Standard Deviation as a Ruler and the Normal Model | |

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Standardizing with z-Scores | |

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Shifting and Scaling | |

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Normal Models | |

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Finding Normal Percentiles | |

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Normal Probability Plots | |

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Exploring and Understanding Data | |

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Exploring Relationships Between Variables | |

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Scatterplots, Association, and Correlation | |

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Scatterplots | |

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Correlation | |

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Warning: Correlation ≠ Causation | |

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Straightening Scatterplots | |

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Linear Regression | |

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Least Squares: The Line of "Best Fit" | |

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The Linear Model | |

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Finding the Least Squares Line | |

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Regression to the Mean | |

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Examining the Residuals | |

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R2-The Variation Accounted for by the Model | |

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Regression Assumptions and Conditions | |

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Regression Wisdom | |

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Examining Residuals | |

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Extrapolation: Reaching Beyond the Data | |

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Outliers, Leverage, and Influence | |

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Lurking Variables and Causation | |

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Working with Summary Values | |

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Exploring Relationships Between Variables | |

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Gathering Data | |

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Understanding Randomness | |

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What is Randomness? | |

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Simulating By Hand | |

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Sample Surveys | |

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The Three Big Ideas of Sampling | |

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Populations and Parameters | |

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Simple Random Samples | |

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Other Sampling Designs | |

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From the Population to the Sample: You Can't Always Get What You Want | |

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The Valid Survey | |

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Common Sampling Mistakes, or How to Sample Badly | |

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Experiments and Observational Studies | |

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Observational Studies | |

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Randomized, Comparative Experiments | |

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The Four Principles of Experimental Design | |

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Control Treatments | |

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Blocking | |

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Confounding | |

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Gathering Data | |

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Randomness and Probability | |

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From Randomness to Probability | |

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Random Phenomena | |

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Modeling Probability | |

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Formal Probability | |

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Probability Rules! | |

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The General Addition Rule | |

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Conditional Probability and the General Multiplication Rule | |

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Independence | |

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Picturing Probability: Tables, Venn Diagrams and Trees | |

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Reversing the Conditioning and Bayes' Rule | |

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Random Variables and Probability Models | |

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Expected Value: Center | |

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Standard Deviation | |

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Combining Random Variables | |

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The Binomial Model | |

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Modeling the Binomial with a Normal Model | |

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The Poisson Model | |

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Continuous Random Variables | |

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Randomness and Probability | |

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From the Data at Hand to the World at Large | |

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Sampling Distribution Models | |

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Sampling Distribution of a Proportion | |

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When Does the Normal Model Work? Assumptions and Conditions | |

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The Sampling Distribution of Other Statistics | |

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The Central Limit Theorem: The Fundamental Theorem of Statistics | |

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Sampling Distributions: A Summary | |

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Confidence Intervals for Proportions | |

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A Confidence Interval | |

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Interpreting Confidence Intervals: What Does 95% Confidence Really Mean? | |

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Margin of Error: Certainty vs. Precision | |

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Assumptions and Conditions | |

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Testing Hypotheses About Proportions | |

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Hypotheses | |

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P-Values | |

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The Reasoning of Hypothesis Testing | |

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Alternative Alternatives | |

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P-Values and Decisions: What to Tell About a Hypothesis Test | |

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Inferences About Means | |

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Getting Started: The Central Limit Theorem (Again) | |

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Gosset's t | |

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Interpreting Confidence Intervals | |

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A Hypothesis Test for the Mean | |

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Choosing the Sample Size | |

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More About Tests and Intervals | |

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Choosing Hypotheses | |

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How to Think About P Values | |

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Alpha Levels | |

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Practical vs. Statistical Significance | |

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Critical Values Again | |

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Errors | |

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Power | |

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From the Data at Hand to the World at Large | |

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Learning About the World | |

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Comparing Groups | |

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The Variance of a Difference | |

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The Standard Deviation of the Difference Between Two Proportions | |

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Assumptions and Conditions for Comparing Proportions | |

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The Sampling Distribution of the Difference between Two Proportions | |

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Comparing Two Means | |

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The Two-Sample t-Test: Testing for the Difference Between Two Means | |

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The Two Sample z-Test: Testing for the Difference between Proportions | |

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The Pooled t-Test: Everyone into the Pool? | |

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Pooling | |

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Paired Samples and Blocks | |

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Paired Data | |

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Assumptions and Conditions | |

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Confidence Intervals for Matched Pairs | |

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Blocking | |

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Comparing Counts | |

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Goodness-of-Fit Tests | |

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Chi-Square Test of Homogeneity | |

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Examining the Residuals | |

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Chi-Square Test of Independence | |

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Inferences for Regression | |

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The Population and the Sample | |

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Assumptions and Conditions | |

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Intuition About Regression Inference | |

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Regression Inference | |

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Standard Errors for Predicted Values | |

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Confidence Intervals for Predicted Values | |

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Logistic Regression | |

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Learning About the World | |

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Inference When Variables Are Related | |

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Analysis of Variance | |

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Testing Whether the Means of Several Groups Are Equal | |

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The ANOVA Table | |

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Plot the Data… | |

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Comparing Means | |

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Multiple Regression | |

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Two Predictors | |

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What Multiple Regression Coefficients Mean | |

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The Multiple Regression Model | |

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Multiple Regression Inference | |

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Comparing Multiple Regression Models | |

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Appendices | |

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Answers | |

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Photo Acknowledgments | |

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Index | |

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Tables and Selected Formulas | |