Introductory Statistics: Exploring the World Through Data, 3rd edition

Published by Pearson (July 15, 2020) © 2020

  • Robert N. Gould University of California, Los Angeles
  • Rebecca Wong West Valley College
  • Colleen Ryan California Lutheran University

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ISBN-13: 9780136880882 (2020 update)
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Data in the real world can be dynamic and sometimes messy. This complexity might intimidate students who are new to math and statistics, but it's also what makes statistics so interesting! Introductory Statistics: Exploring the World Through Data teaches you how to explore and analyze real data to answer real-world problems. Crafted by authors who are active in the classroom and in the statistics education community, the 3rd Edition pairs a clear, conversational writing style with new and frequent opportunities to apply statistical thinking. Its tone and learning aids are designed to equip all students to analyze, interpret and tell a story about modern data, regardless of mathematical background.

1. Introduction to Data

  • 1.1 What Are Data?
  • 1.2 Classifying and Storing Data
  • 1.3 Investigating Data
  • 1.4 Organizing Categorical Data
  • 1.5 Collecting Data to Understand Causality

2. Picturing Variation with Graphs

  • 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

3. Numerical Summaries of Center and Variation

  • 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<

4. Regression Analysis: Exploring Associations between Variables

  • 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

5. Modeling Variation with Probability

  • 5.1 What Is Randomness?
  • 5.2 Finding Theoretical Probabilities
  • 5.3 Associations in Categorical Variables
  • 5.4 Finding Empirical Probabilities

6. Modeling Rando Events: The Normal and Binomial Models

  • 6.1 Probability Distributions Are Models of Random Experiments
  • 6.2 The Normal Model
  • 6.3 The Binomial Model (Optional)

7. Survey Sampling and Inference

  • 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

8. Hypothesis Testing for Population Proportions

  • 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

9. Inferring Population Means

  • 9.1 Sample Means of Rando 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

10. Associations between Categorical Variables

  • 10.1 The Basic Ingredients for Testing with Categorical Variables
  • 10.2 The Chi-Square Test for Goodness of Fit
  • 10.3 Chi-Square Tests for Associations between Categorical Variables
  • 10.4 Hypothesis Tests When Sample Sizes Are Small

11. Multiple Comparisons and Analysis of Variance

  • 11.1 Multiple Comparisons
  • 11.2 The Analysis of Variance
  • 11.3 The ANOVA Test
  • 11.4 Post-Hoc Procedures

12. Experimental Design: Controlling Variation

  • 12.1 Variation Out of Control
  • 12.2 Controlling Variation in Surveys
  • 12.3 Reading Research Papers

13. Inference without Normality

  • 13.1 Transforming Data
  • 13.2 The Sign Test for Paired Data
  • 13.3 Mann-Whitney Test for Two Independent Groups
  • 13.4 Randomization Tests

14. Inference for Regression

  • 14.1 The Linear Regression Model
  • 14.2 Using the Linear Model
  • 14.3 Predicting Values and Estimating Means

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