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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 | |