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Essential Statistics, 3rd edition

Published by Pearson (February 1, 2020) © 2021

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

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For 1-semester courses in Introductory Statistics.

Data analysis for everyone

In a data-driven world students must learn to think critically about data, communicate their findings, and evaluate others′ arguments. The first two-thirds of Essential Statistics cover the fundamental concepts of exploratory data analysis (data collection and summary) and inferential statistics. The remaining third returns to themes covered earlier and presents them in a new context, introducing additional statistical methods such as estimating population means and analyzing categorical variables. The 3rd Edition reflects the rise of data science, with new features to prepare students for working with complex data.

Hallmark features of this title

  • TechTips outline steps for performing calculations using TI-84® graphing calculators, Excel®, Minitab®, and StatCrunch.
  • A real-world Case Study begins each chapter; the end of the chapter shows how techniques covered helped solve the problem presented in the Case Study.
  • Key Points highlight concepts essential for progress.
  • Snapshots break down key concepts, summarizing each concept or procedure and indicating when/how it should be used.
  • Abundant worked-out examples model solutions to real-world problems, and each example is tied to an end-of-chapter exercise.
  • The "Try" icon within the exercise sets indicates which problems are tied to worked-out examples in that chapter, and the numbers of those examples are indicated.

New and updated features of this title

  • New and updated data sets are found throughout, with the inclusion of more large data. All data sets used in the exposition and exercises are available here.
  • New Data Projects end each chapter. These are activities designed for students to work alone or in pairs. These sections, which grow increasingly more complex, are intended to guide students through basic "data moves" to help them find insight in complex data.
  • New Data Moves icon: Some examples and data sets are based on extracts of data from much larger data sets. The Data Moves icon points students to these data sets and also indicates the "data moves" used to extract the data.
  • New Emphasis on the Data Cycle to guide students through the statistical investigation process. Four phases include Ask Questions, Consider Data, Analyze Data and Interpret Data. A new marginal icon indicates when the Data Cycle is particularly relevant.
  • Many new and updated exercises are provided. The wide range of exercises includes Section Exercises that strengthen recall and assess basic knowledge; paired Chapter Review Exercises that emphasize good statistical practice, Challenging Exercises that ask open-ended questions, and Guided Exercises that offer problem-solving help.

Features of MyLab Statistics for the 3rd Edition

  • Integrated Review ensures students are caught up on prior skills.
    • Integrated Review provides embedded and personalized review of prerequisite topics within relevant chapters. Students can check their prerequisite skills and receive personalized practice on the topics they need to focus on, with study aids like worksheets and videos also available to help. 
    • Integrated Review assignments, including a Skills Check and personalized homework for the review topics, are premade and available to assign in the Assignment Manager. The review homework includes videos and worksheets as learning aids for relevant exercises to give students remediation right where they need it. 
    • Integrated Review can support a corequisite course model, or any course where students would benefit from review.
  • Each chapter has a set of Data Project questions in the MyLab. These activities, which grow increasingly more complex, are intended to guide students through basic "data moves" to help find insight in complex data.

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 Data Project: How Are Data Stored?

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

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

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

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

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

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

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

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

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

Appendices

  1. Tables 
  2. Check Your Tech Answers 
  3. Credits Index

About our authors

Robert L. Gould  (PhD, University of California, Los Angeles) is a leader in the statistics education community. He has served as chair of the American Statistical Association′s (ASA) Statistics Education Section, chair of the American Mathematical Association of Two-Year Colleges/ASA Joint Committee, and has served on the National Council of Teacher of Mathematics/ASA Joint Committee. He served on a panel of co-authors for the 2005 Guidelines for Assessment and Instruction in Statistics Education (GAISE) College Report and is co-author on the revision for the GAISE K-12 Report. As lead principal investigator of the NSF-funded Mobilize Project, he led the development of the first high school level data science course, which is taught in the Los Angeles Unified School District and several other districts.

Rob teaches in the Department of Statistics at UCLA, where he directs the undergraduate statistics program and is director of the UCLA Center for Teaching Statistics. In recognition for his activities in statistics education, in 2012 Rob was elected Fellow of the American Statistical Association. He is the 2019 recipient of the ASA Waller Distinguished Teaching Award and the USCOTS Lifetime Achievement Award. In his free time, Rob plays the cello and enjoys attending concerts of all types and styles.

Rebecca K. Wong has taught mathematics and statistics at West Valley College for more than 20 years. She enjoys designing activities to help students explore statistical concepts and encouraging students to apply those concepts to areas of personal interest.

Rebecca earned at BA in mathematics and psychology from the University of California, Santa Barbara, an MST in mathematics from Santa Clara University, and an EdD 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.

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 Moorpark Community College. 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 BA in physics from Wellesley College, an MAT in physics from Harvard University, and an MA in chemistry from Wellesley College. Her first exposure to statistics was with Frederick Mosteller at Harvard. In her spare time, Colleen sings, has been an avid skier, and enjoys time with her family.

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