MyLab Statistics with Pearson eText Access Code (24 Months) for Introductory Statistics

<div class="c-section-headers-number-list_container"> <h3>1. Introduction to Data</h3> <ul> <li>1.1 What Are Data?</li> <li>1.2 Classifying and Storing Data</li> <li>1.3 Investigating Data</li> <li>1.4 Organizing Categorical Data</li> <li>1.5 Collecting Data to Understand Causality</li> </ul> <h3>2. Picturing Variation with Graphs</h3> <ul> <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> </ul> <h3>3. Numerical Summaries of Center and Variation</h3> <ul> <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&lt;</li> </ul> <h3>4. Regression Analysis: Exploring Associations between Variables</h3> <ul> <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> </ul> <h3>5. Modeling Variation with Probability</h3> <ul> <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> </ul> <h3>6. Modeling Rando Events: The Normal and Binomial Models</h3> <ul> <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> </ul> <h3>7. Survey Sampling and Inference</h3> <ul> <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> </ul> <h3>8. Hypothesis Testing for Population Proportions</h3> <ul> <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> </ul> <h3>9. Inferring Population Means</h3> <ul> <li>9.1 Sample Means of Rando 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 Means</li> <li>9.6 Overview of Analyzing Means</li> </ul> <h3>10. Associations between Categorical Variables</h3> <ul> <li>10.1 The Basic Ingredients for Testing with Categorical Variables</li> <li>10.2 The Chi-Square Test for Goodness of Fit</li> <li>10.3 Chi-Square Tests for Associations between Categorical Variables</li> <li>10.4 Hypothesis Tests When Sample Sizes Are Small</li> </ul> <h3>11. Multiple Comparisons and Analysis of Variance</h3> <ul> <li>11.1 Multiple Comparisons</li> <li>11.2 The Analysis of Variance</li> <li>11.3 The ANOVA Test</li> <li>11.4 Post-Hoc Procedures</li> </ul> <h3>12. Experimental Design: Controlling Variation</h3> <ul> <li>12.1 Variation Out of Control</li> <li>12.2 Controlling Variation in Surveys</li> <li>12.3 Reading Research Papers</li> </ul> <h3>13. Inference without Normality</h3> <ul> <li>13.1 Transforming Data</li> <li>13.2 The Sign Test for Paired Data</li> <li>13.3 Mann-Whitney Test for Two Independent Groups</li> <li>13.4 Randomization Tests</li> </ul> <h3>14. Inference for Regression</h3> <ul> <li>14.1 The Linear Regression Model</li> <li>14.2 Using the Linear Model</li> <li>14.3 Predicting Values and Estimating Means</li> </ul> </div>