Essential Statistics

Chapter 1: Introduction to Data

• • Case Study—Deadly Cell Phones?
• • 1.1 What Are Data?
• • 1.2 Classifying and Storing Data
• • 1.3 Organizing Categorical Data
• • 1.4 Collecting Data to Understand Causality
• • Data Project–How Are Data Stored?

Chapter 2: Picturing Variation with Graphs

• • Case Study—Student-to-Teacher Ratio at Colleges
• • 2.1 Visualizing Variation in Numerical Data
• • 2.2 Summarizing Important Features of a Numerical Distribution
• • 2.3 Visualizing Variation in Categorical Variables
• • 2.4 Summarizing Categorical Distributions
• • 2.5 Interpreting Graphs
• • Data Project–Asking Questions

Chapter 3: Numerical Summaries of Center and Variation

• • Case Study—Living in a Risky World
• • 3.1 Summaries for Symmetric Distributions
• • 3.2 What’s Unusual? The Empirical Rule and z-Scores
• • 3.3 Summaries for Skewed Distributions
• • 3.4 Comparing Measures of Center
• • 3.5 Using Boxplots for Displaying Summaries
• • Data Project–The Statistical Investigation Cycle

Chapter 4: Regression Analysis: Exploring Associations between Variables

• • Case Study—Forecasting Home Prices
• • 4.1 Visualizing Variability with a Scatterplot
• • 4.2 Measuring Strength of Association with Correlation
• • 4.3 Modeling Linear Trends
• • 4.4 Evaluating the Linear Model
• • Data Project–Data Moves

Chapter 5: Modeling Variation with Probability

• • Case Study—SIDS or Murder?
• • 5.1 What Is Randomness?
• • 5.2 Finding Theoretical Probabilities
• • 5.3 Associations in Categorical Variables
• • 5.4 Finding Empirical Probabilities
• • Data Project–Submitting Data

Chapter 6: Modeling Random Events: The Normal and Binomial Models

• • Case Study—You Sometimes Get More Than You Pay For
• • 6.1 Probability Distributions Are Models of Random Experiments
• • 6.2 The Normal Model
• • 6.3 The Binomial Model (optional)
• • Data Project–Generating Random Numbers

Chapter 7: Survey Sampling and Inference

• • Case Study—Spring Break Fever: Just What the Doctors Ordered?
• • 7.1 Learning about the World through Surveys
• • 7.2 Measuring the Quality of a Survey
• • 7.3 The Central Limit Theorem for Sample Proportions
• • 7.4 Estimating the Population Proportion with Confidence Intervals
• • 7.5 Comparing Two Population Proportions with Confidence
• • Data Project–Population Proportions

Chapter 8: Hypothesis Testing for Population Proportions

• • Case Study—Dodging the Question
• • 8.1 The Essential Ingredients of Hypothesis Testing
• • 8.2 Hypothesis Testing in Four Steps
• • 8.3 Hypothesis Tests in Detail
• • 8.4 Comparing Proportions from Two Populations
• • Data Project–Dates as Data

Chapter 9: Inferring Population Means

• • Case Study—You Look Sick! Are You Sick?
• • 9.1 Sample Means of Random Samples
• • 9.2 The Central Limit Theorem for Sample Means
• • 9.3 Answering Questions about the Mean of a Population
• • 9.4 Hypothesis Testing for Means
• • 9.5 Comparing Two Population Means
• • 9.6 Overview of Analyzing Means
• • Data Project–Data Structures

Chapter 10: Analyzing Categorical Variables and Interpreting Research

• • Case Study—Popping Better Popcorn
• • 10.1 The Basic Ingredients for Testing with Categorical Variables
• • 10.2 Chi-Square Tests for Associations between Categorical Variables
• • 10.3 Reading Research Papers
• • Data Project–Think Small

Chapter Appendix A: Tables

Chapter Appendix B: Check Your Tech Answers

Chapter Appendix C: Credits