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| Preface | |
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| Data and Case Studies | |
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| Case Study: Flight Delays | |
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| Case Study: Birth Weights of Babies | |
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| Case Study: Verizon Repair Times | |
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| Sampling | |
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| Parameters and Statistics | |
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| Case Study: General Social Survey | |
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| Sample Surveys | |
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| Case Study: Beer and Hot Wings | |
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| Case Study: Black Spruce Seedlings | |
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| Studies | |
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| Exercises | |
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| Exploratory Data Analysis | |
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| Basic Plots | |
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| Numeric Summaries | |
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| Center | |
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| Spread | |
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| Shape | |
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| Boxplots | |
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| Quantiles and Normal Quantile Plots | |
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| Empirical Cumulative Distribution Functions | |
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| Scatter Plots | |
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| Skewness and Kurtosis | |
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| Exercises | |
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| Hypothesis Testing | |
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| Introduction to Hypothesis Testing | |
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| Hypotheses | |
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| Permutation Tests | |
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| Implementation Issues | |
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| One-Sided and Two-Sided Tests | |
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| Other Statistics | |
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| Assumptions | |
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| Contingency Tables | |
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| Permutation Test for Independence | |
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| Chi-Square Reference Distribution | |
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| Chi-Square Test of Independence | |
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| Test of Homogeneity | |
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| Goodness-of-Fit: All Parameters Known | |
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| Goodness-of-Fit: Some Parameters Estimated | |
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| Exercises | |
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| Sampling Distributions | |
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| Sampling Distributions | |
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| Calculating Sampling Distributions | |
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| The Central Limit Theorem | |
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| CLT for Binomial Data | |
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| Continuity Correction for Discrete Random Variables | |
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| Accuracy of the Central Limit Theorem | |
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| CLT for Sampling Without Replacement | |
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| Exercises | |
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| The Bootstrap | |
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| Introduction to the Bootstrap | |
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| The Plug-In Principle | |
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| Estimating the Population Distribution | |
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| How Useful Is the Bootstrap Distribution? | |
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| Bootstrap Percentile Intervals | |
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| Two Sample Bootstrap | |
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| The Two Independent Populations Assumption | |
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| Other Statistics | |
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| Bias | |
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| Monte Carlo Sampling: The "Second Bootstrap Principle" | |
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| Accuracy of Bootstrap Distributions | |
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| Samnle Mean: Large Sample Size | |
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| Sample Mean: Small Sample Size | |
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| Sample Median | |
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| How Many Bootstrap Samples are Needed? | |
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| Exercises | |
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| Estimation | |
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| Maximum Likelihood Estimation | |
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| Maximum Likelihood for Discrete Distributions | |
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| Maximum Likelihood for Continuous Distributions | |
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| Maximum Likelihood for Multiple Parameters | |
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| Method of Moments | |
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| Properties of Estimators | |
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| Unbiasedness | |
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| Efficiency | |
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| Mean Square Error | |
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| Consistency | |
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| Transformation Invariance | |
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| Exercises | |
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| Classical Inference: Confidence Intervals | |
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| Confidence Intervals for Means | |
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| Confidence Intervals for a Mean, $$$ Known | |
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| Confidence Intervals for a Mean, $$$ Unknown | |
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| Confidence Intervals for a Difference in Means | |
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| Confidence Intervals in General | |
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| Location and Scale Parameters | |
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| One-Sided Confidence Intervals | |
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| Confidence Intervals for Proportions | |
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| The Agresti-Coull Interval for a Proportion | |
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| Confidence Interval for the Difference of Proportions | |
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| Bootstrap t Confidence Intervals | |
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| Comparing Bootstrap t and Formula t Confidence Intervals | |
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| Exercises | |
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| Classical Inference: Hypothesis Testing | |
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| Hypothesis Tests for Means and Proportions | |
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| One Population | |
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| Comparing Two Populations | |
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| Type I and Type II Errors | |
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| Type I Errors | |
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| Type II Errors and Power | |
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| More on Testing | |
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| On Significance | |
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| Adjustments for Multiple Testing | |
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| P-values Versus Critical Regions | |
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| Likelihood Ratio Tests | |
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| Simple Hypotheses and the Neyman-Pearson Lemma | |
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| Generalized Likelihood Ratio Tests | |
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| Exercises | |
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| Regression | |
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| Covariance | |
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| Correlation | |
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| Least-Squares Regression | |
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| Regression Toward the Mean | |
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| Variation | |
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| Diagnostics | |
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| Multiple Regression | |
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| The Simple Linear Model | |
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| Inference for � and � | |
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| Inference for the Response | |
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| Comments About Assumptions for the Linear Model | |
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| Resampling Correlation and Regression | |
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| Permutation Tests | |
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| Bootstrap Case Study: Bushmeat | |
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| Logistic Regression | |
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| Inference for Logistic Regression | |
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| Exercises | |
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| Bayesian Methods | |
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| Bayes' Theorem | |
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| Binomial Data, Discrete Prior Distributions | |
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| Binomial Data, Continuous Prior Distributions | |
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| Continuous Data | |
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| Sequential Data | |
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| Exercises | |
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| Additional Topics | |
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| Smoothed Bootstrap | |
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| Kernel Density Estimate | |
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| Parametric Bootstrap | |
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| The Delta Method | |
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| Stratified Sampling | |
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| Computational Issues in Bayesian Analysis | |
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| Monte Carlo Integration | |
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| Importance Sampling | |
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| Ratio Estimate for Importance Sampling | |
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| Importance Sampling in Bayesian Applications | |
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| Exercises | |
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| Review of Probability | |
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| Basic Probability | |
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| Mean and Variance | |
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| The Mean of a Sample of Random Variables | |
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| The Law of Averages | |
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| The Normal Distribution | |
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| Sums of Normal Random Variables | |
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| Higher Moments and the Moment Generating Function | |
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| Probability Distributions | |
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| The Bernoulli and Binomial Distributions | |
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| The Multinomial Distribution | |
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| The Geometric Distribution | |
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| The Negative Binomial Distribution | |
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| The Hypergeometric Distribution | |
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| The Poisson Distribution | |
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| The Uniform Distribution | |
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| The Exponential Distribution | |
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| The Gamma Distribution | |
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| The Chi-Square Distribution | |
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| The Student's t Distribution | |
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| The Beta Distribution | |
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| The F Distribution | |
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| Exercises | |
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| Distributions Quick Reference | |
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| Solutions to Odd-Numbered Exercises | |
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| Bibliography | |
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| Index | |