Essential Statistics, Global Edition

<ul> <li>Preface </li> <li>Index of Applications </li> <li>1. Introduction to Data </li> <li>Case Study–Deadly Cell Phones? </li> <li>1.1 What Are Data? </li> <li>1.2 Classifying and Storing Data </li> <li>1.3 Organizing Categorical Data </li> <li>1.4 Collecting Data to Understand Causality </li> <li>Exploring Statistics–Collecting a Table of Different Kinds of Data </li> <li>2. Picturing Variation with Graphs </li> <li>Case Study–Student-to-Teacher Ratio at Colleges </li> <li>2.1 Visualizing Variation in Numerical Data </li> <li>2.2 Summarizing Important Features of a Numerical Distribution </li> <li>2.3 Visualizing Variation in Categorical Variables </li> <li>2.4 Summarizing Categorical Distributions </li> <li>2.5 Interpreting Graphs </li> <li>Exploring Statistics–Personal Distance </li> <li>3. Numerical Summaries of Center and Variation </li> <li>Case Study–Living in a Risky World </li> <li>3.1 Summaries for Symmetric Distributions </li> <li>3.2 What’s Unusual? The Empirical Rule and z-Scores </li> <li>3.3 Summaries for Skewed Distributions </li> <li>3.4 Comparing Measures of Center </li> <li>3.5 Using Boxplots for Displaying Summaries </li> <li>Exploring Statistics–Does Reaction Distance Depend on Gender? </li> <li>4. Regression Analysis: Exploring Associations between Variables </li> <li>Case Study–Catching Meter Thieves </li> <li>4.1 Visualizing Variability with a Scatterplot </li> <li>4.2 Measuring Strength of Association with Correlation </li> <li>4.3 Modeling Linear Trends </li> <li>4.4 Evaluating the Linear Model </li> <li>Exploring Statistics–Guessing the Age of Famous People </li> <li>5. Modeling Variation with Probability </li> <li>Case Study–SIDS or Murder? </li> <li>5.1 What Is Randomness? </li> <li>5.2 Finding Theoretical Probabilities </li> <li>5.3 Associations in Categorical Variables </li> <li>5.4 Finding Empirical Probabilities </li> <li>Exploring Statistics–Let’s Make a Deal: Stay or Switch? </li> <li>6. Modeling Random Events: The Normal and Binomial Models </li> <li>Case Study–You Sometimes Get More Than You Pay For </li> <li>6.1 Probability Distributions Are Models of Random Experiments </li> <li>6.2 The Normal Model </li> <li>6.3 The Binomial Model (optional) </li> <li>Exploring Statistics–ESP with Coin Flipping </li> <li>7. Survey Sampling and Inference </li> <li>Case Study–Spring Break Fever: Just What the Doctors Ordered? </li> <li>7.1 Learning about the World through Surveys </li> <li>7.2 Measuring the Quality of a Survey </li> <li>7.3 The Central Limit Theorem for Sample Proportions </li> <li>7.4 Estimating the Population Proportion with Confidence Intervals </li> <li>7.5 Comparing Two Population Proportions with Confidence </li> <li>Exploring Statistics–Simple Random Sampling Prevents Bias </li> <li>8. Hypothesis Testing for Population Proportions </li> <li>Case Study–Dodging the Question </li> <li>8.1 The Essential Ingredients of Hypothesis Testing </li> <li>8.2 Hypothesis Testing in Four Steps </li> <li>8.3 Hypothesis Tests in Detail </li> <li>8.4 Comparing Proportions from Two Populations </li> <li>Exploring Statistics–Identifying Flavors of Gum through Smell </li> <li>9. Inferring Population Means </li> <li>Case Study–Epilepsy Drugs and Children </li> <li>9.1 Sample Means of Random Samples </li> <li>9.2 The Central Limit Theorem for Sample Means </li> <li>9.3 Answering Questions about the Mean of a Population </li> <li>9.4 Hypothesis Testing for Means </li> <li>9.5 Comparing Two Population </li> </ul>