Essential Statistics, 3rd edition

Published by Pearson (25 January 2021) © 2022

  • Robert N. Gould University of California, Los Angeles
  • Rebecca Wong West Valley College
  • Colleen Ryan California Lutheran University
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In this eTextbook — More ways to learn

  • More flexible. Start learning right away, on any device.
  • More supportive. Get AI explanations and practice questions (select titles).
  • More interactive. Bring learning to life with audio, videos, and diagrams.
  • More memorable. Make concepts stick with highlights, search, notes, and flashcards.
  • More understandable. Translate text into 100+ languages with one tap.
Products list
$64.98
Instant access

In this eTextbook — More ways to learn

  • More flexible. Start learning right away, on any device.
  • More supportive. Get AI explanations and practice questions (select titles).
  • More interactive. Bring learning to life with audio, videos, and diagrams.
  • More memorable. Make concepts stick with highlights, search, notes, and flashcards.
  • More understandable. Translate text into 100+ languages with one tap.

Title overview

Essential Statistics, 3rd Edition is the textbook that covers the fundamental concepts of introductory statistics using real data. Authors Robert Gould and Rebecca Wong first explain exploratory data analysis and inferential statistics. In the final chapters, they present themes covered earlier in a new context and introduce additional statistical methods, including estimating population means and analyzing categorical variables.

To prepare you for success in our data-driven world, the textbook shows how to summarize data sets, run and interpret hypothesis tests, build confidence intervals, and analyze data with StatCrunch, Excel, or a TI calculator. The authors provide detailed instruction in probability, hypothesis testing, goodness-of-fit, inferences from two samples, discrete and normal probability distributions, and correlation and regression, along with describing and exploring data and estimating parameters and confidence intervals.

Inspired by the Guidelines for Assessment and Instruction in Statistics Education (GAISE), the 3rd Edition reflects the rise of data science, with new features to prepare you for working with complex data.

Table of contents

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

Author bios

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