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

Published by Pearson (January 1, 2019) © 2020

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

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For courses in Introductory Statistics.

Data analysis for everyone

Introductory Statistics: Exploring the World Through Data teaches students 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 any student to analyze, interpret, and tell a story about modern data, regardless of the student's mathematical proficiency.

Hallmark features of this title

  • Large data sets throughout focus on different variables, illustrating how data “moves” depending on the concept or question explored.
  • Snapshots break down the statistical concepts introduced, quickly summarizing the concept or procedure and indicating when and how it should be used.
  • Guided Exercises step students through solving a problem if they need extra help while doing homework.
  • Access to some technology or statistical software package is assumed in all procedures and concepts.
  • TechTips outline steps for performing calculations using TI-83/84-Plus® graphing calculators, Excel®, Minitab®, and StatCrunch. Whenever a new method or procedure is introduced, an icon refers students to the TechTips section at the end of the chapter. 

New and updated features of this title

  • Data Moves give students the original version of a dataset used in the book and explain how it was manipulated to answer the question at hand. This prepares students to do their own data manipulation in the end-of-chapter Data Projects. 
  • End-of-chapter Data Projects emphasize critical thinking and data analysis skills, asking students to move through the entire data cycle in order to make a data-informed decision and communicate their findings.
    • Data Projects are assignable in MyLab Statistics and assume the use of StatCrunch® or another statistical software package.  
  • Tighter integration of the stages of the Data Cycle guides students more clearly through the investigative process, from data collection to data analysis. 
  • New and updated topics and examples help students apply what they're learning to the real world.
  • An updated Case Study opens each chapter, showing a real-world application of the concepts. At the end of the chapter, the case study is revisited to show how the statistical techniques covered in the chapter help solve the problem presented.

Features of MyLab Statistics for the 3rd Edition

  • Assessment questions are tied to Data Cycle videos. Data Cycle videos demonstrate for students that data collection and data analysis can be applied to answer questions about everyday life.
  • Interactive Applet Modules help students visualize statistical concepts and apply them to real-world situations. Modules introduce students to a concept, walk them through an example, and close by asking them to answer a series of application questions. Interactive Applet Modules are assignable along with 13 existing standalone StatCrunch applets.
  • Chapter Review Videos by coauthor Rebecca Wong and Carrie Grant (Flagler College) walk students through key examples from the text.
  • Updated question types provide more opportunities to practice statistical thinking and a streamlined organization that makes them easier to add to assignments.
    • The Conceptual Question Library is now correlated by chapter, making it easier to include those questions in assignments.
    • New StatCrunch Projects and end-of-chapter Data Projects provide students with opportunities to analyze and interpret data. Each project consists of a series of questions about a large data set in StatCrunch.

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

About our authors

Robert L. Gould (Ph.D., University of California - San Diego) is a leader in the statistics education community. He has served as chair of the AMATYC/ASA joint committee, was co-leader of the Two-Year College Data Science Summit hosted by the American Statistical Association, served as chair of the ASA's Statistics Education Section, and was a co-author of the 2005 Guidelines for Assessment in Instruction on Statistics Education (GAISE) College Report. While serving as the Associate Director of Professional Development for CAUSE (Consortium for the Advancement of Undergraduate Statistics Education), he worked closely with the American Mathematical Association of Two-Year Colleges (AMATYC) to provide traveling workshops and summer institutes in statistics. He was the lead principal investigator of the NSF-funded Mobilize Project, which developed and implemented the first high-school level data science course. For over 20 years, he has served as Vice-Chair of Undergraduate Studies at the UCLA Department of Statistics, and is Director of the UCLA Center for the Teaching of Statistics. In 2012, Rob was elected Fellow of the American Statistical Association.

Colleen N. Ryan  has taught statistics, chemistry and physics to diverse community college students for decades. She taught at Oxnard College from 1975 to 2006, where she earned the Teacher of the Year Award. Colleen currently teaches statistics part-time at California Lutheran University. She often designs her own lab activities. Her passion is to discover new ways to make statistical theory practical, easy to understand, and sometimes even fun. Colleen earned a B.A. in physics from Wellesley College, an M.A.T. in physics from Harvard University, and an M.A. in chemistry from Wellesley College. Her first exposure to statistics was with Frederick Mosteller at Harvard. In her spare time, she sings with the Oaks Chamber Singers and enjoys time with her family.

Rebecca K. Wong has taught mathematics and statistics at West Valley College for more than 20 years. She enjoys designing activities to help students actively explore statistical concepts and encouraging students to apply those concepts to areas of personal interest. Rebecca earned a B.A. in mathematics and psychology from the University of California - Santa Barbara, an M.S.T. in mathematics from Santa Clara University, and an Ed.D. in Educational Leadership from San Francisco State University. She has been recognized for outstanding teaching by the National Institute of Staff and Organizational Development and the California Mathematics Council of Community Colleges. When not teaching, Rebecca is an avid reader and enjoys hiking trails with friends.

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