# Essential Statistics, 3rd edition

• Robert N. Gould,
• Rebecca Wong,
• Colleen Ryan

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## Overview

Essential Statistics presents data analysis for everyone. In our data-driven world, you must learn to think critically with and about data, communicate your findings, and carefully evaluate others′ arguments. The first two-thirds of this text 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 including estimating population means and analyzing categorical variables. Inspired by the Guidelines for Assessment and Instruction in Statistics Education (GAISE), the authors have crafted the 3rd Edition to reflect the rise of data science, with new features to prepare you for working with complex data.

ISBN-13: 9780135964705

Subject: Introductory Statistics

Category: Essential Statistics

## Overview

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
• 9.4 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

#### C: Credits Index

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