Essential Statistics, Global Edition, 2nd edition

Published by Pearson (November 14, 2016) © 2017

  • Robert N. Gould University of California, Los Angeles
  • Colleen Ryan California Lutheran University
  • Rebecca Wong West Valley College
  • Rebecca Wong West Valley College
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Exploring the World through Data

We live in a data-driven world, and the goal of this text is to teach students how to access and analyse these data critically. Authors Rob Gould, Colleen Ryan, and Rebecca Wong want students to develop a 'data habit of mind' because learning statistics is an essential life skill that extends beyond the classroom. Regardless of their math backgrounds, students will learn how to think about data and how to reason using data. With a clear, unintimidating writing style and carefully chosen pedagogy, this text makes data analysis accessible to all students.

  • Preface
  • Index of Applications
  • 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
  • Exploring Statistics–Collecting a Table of Different Kinds of Data
  • 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
  • Exploring Statistics–Personal Distance
  • 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
  • Exploring Statistics–Does Reaction Distance Depend on Gender?
  • 4. Regression Analysis: Exploring Associations between Variables
  • Case Study–Catching Meter Thieves
  • 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
  • Exploring Statistics–Guessing the Age of Famous People
  • 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
  • Exploring Statistics–Let’s Make a Deal: Stay or Switch?
  • 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)
  • Exploring Statistics–ESP with Coin Flipping
  • 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
  • Exploring Statistics–Simple Random Sampling Prevents Bias
  • 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
  • Exploring Statistics–Identifying Flavors of Gum through Smell
  • 9. Inferring Population Means
  • Case Study–Epilepsy Drugs and Children
  • 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

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