Introduction to the Practice of Statistics
NINTH EDITION
David S. Moore George P. McCabe Bruce A. Craig Purdue University
Vice President, STEM: Ben Roberts Publisher: Terri Ward Senior Acquisitions Editor: Karen Carson Marketing Manager: Tom DeMarco Marketing Assistant: Cate McCaffery Development Editor: Jorge Amaral Senior Media Editor: Catriona Kaplan Assistant Media Editor: Emily Tenenbaum Director of Digital Production: Keri deManigold Senior Media Producer: Alison Lorber Associate Editor: Victoria Garvey Editorial Assistant: Katharine Munz Photo Editor: Cecilia Varas Photo Researcher: Candice Cheesman Director of Design, Content Management: Diana Blume Text and Cover Designer: Blake Logan Project Editor: Edward Dionne, MPS North America LLC Illustrations: MPS North America LLC Production Manager: Susan Wein Composition: MPS North America LLC Printing and Binding: LSC Communications Cover Illustration: Drawing Water: Spring 2011 detail (Midwest) by David Wicks “Look Back” Arrow: NewCorner/Shutterstock
Library of Congress Control Number: 2016946039
Student Edition Hardcover: ISBN-13: 978-1-319-01338-7 ISBN-10: 1-319-01338-4
Student Edition Loose-leaf: ISBN-13: 978-1-319-01362-2 ISBN-10: 1-319-01362-7
Instructor Complimentary Copy: ISBN-13: 978-1-319-01428-5 ISBN-10: 1-319-01428-3
© 2017, 2014, 2012, 2009 by W. H. Freeman and Company All rights reserved Printed in the United States of America First printing
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Brief Contents
To Teachers: About This Book To Students: What Is Statistics? About the Authors Data Table Index Beyond the Basics Index
PART I Looking at Data CHAPTER 1 Looking at Data—Distributions
CHAPTER 2 Looking at Data—Relationships
CHAPTER 3 Producing Data
PART II Probability and Inference CHAPTER 4 Probability: The Study of Randomness
CHAPTER 5 Sampling Distributions
CHAPTER 6 Introduction to Inference
CHAPTER 7 Inference for Means
CHAPTER 8 Inference for Proportions
PART III Topics in Inference CHAPTER 9 Inference for Categorical Data
CHAPTER 10 Inference for Regression
CHAPTER 11 Multiple Regression
CHAPTER 12 One-Way Analysis of Variance
CHAPTER 13 Two-Way Analysis of Variance Tables Answers to Odd-Numbered Exercises Notes and Data Sources Index
Contents
To Teachers: About This Book To Students: What Is Statistics? About the Authors Data Table Index Beyond the Basics Index
PART I Looking at Data CHAPTER 1 Looking at Data—Distributions Introduction
1.1 Data Key characteristics of a data set
Section 1.1 Summary Section 1.1 Exercises 1.2 Displaying Distributions with Graphs
Categorical variables: Bar graphs and pie charts Quantitative variables: Stemplots and histograms Histograms Data analysis in action: Don’t hang up on me Examining distributions Dealing with outliers Time plots
Section 1.2 Summary Section 1.2 Exercises 1.3 Describing Distributions with Numbers
Measuring center: The mean Measuring center: The median Mean versus median Measuring spread: The quartiles The five-number summary and boxplots The 1.5 × IQR rule for suspected outliers Measuring spread: The standard deviation Properties of the standard deviation Choosing measures of center and spread Changing the unit of measurement
Section 1.3 Summary Section 1.3 Exercises 1.4 Density Curves and Normal Distributions
Density curves
Measuring center and spread for density curves Normal distributions The 68–95–99.7 rule Standardizing observations Normal distribution calculations Using the standard Normal table Inverse Normal calculations Normal quantile plots
Beyond the Basics: Density estimation Section 1.4 Summary Section 1.4 Exercises Chapter 1 Exercises
CHAPTER 2 Looking at Data—Relationships Introduction
2.1 Relationships Examining relationships
Section 2.1 Summary Section 2.1 Exercises 2.2 Scatterplots
Interpreting scatterplots The log transformation Adding categorical variables to scatterplots Scatterplot smoothers Categorical explanatory variables
Section 2.2 Summary Section 2.2 Exercises 2.3 Correlation
The correlation r Properties of correlation
Section 2.3 Summary Section 2.3 Exercises 2.4 Least-Squares Regression
Fitting a line to data Prediction Least-squares regression Interpreting the regression line Facts about least-squares regression Correlation and regression Another view of r2
Section 2.4 Summary Section 2.4 Exercises 2.5 Cautions about Correlation and Regression
Residuals Outliers and influential observations
Beware of the lurking variable Beware of correlations based on averaged data Beware of restricted ranges
Beyond the Basics: Data mining Section 2.5 Summary Section 2.5 Exercises 2.6 Data Analysis for Two-Way Tables
The two-way table Joint distribution Marginal distributions Describing relations in two-way tables Conditional distributions Simpson’s paradox
Section 2.6 Summary Section 2.6 Exercises 2.7 The Question of Causation
Explaining association Establishing causation
Section 2.7 Summary Section 2.7 Exercises Chapter 2 Exercises
CHAPTER 3 Producing Data Introduction
3.1 Sources of Data Anecdotal data Available data Sample surveys and experiments
Section 3.1 Summary Section 3.1 Exercises 3.2 Design of Experiments
Comparative experiments Randomization Randomized comparative experiments How to randomize Randomization using software Randomization using random digits Cautions about experimentation Matched pairs designs Block designs
Section 3.2 Summary Section 3.2 Exercises 3.3 Sampling Design
Simple random samples How to select a simple random sample
Stratified random samples Multistage random samples Cautions about sample surveys
Beyond the Basics: Capture-recapture sampling Section 3.3 Summary Section 3.3 Exercises 3.4 Ethics
Institutional review boards Informed consent Confidentiality Clinical trials Behavioral and social science experiments
Section 3.4 Summary Section 3.4 Exercises Chapter 3 Exercises
PART II Probability and Inference CHAPTER 4 Probability: The Study of Randomness Introduction
4.1 Randomness The language of probability Thinking about randomness The uses of probability
Section 4.1 Summary Section 4.1 Exercises 4.2 Probability Models
Sample spaces Probability rules Assigning probabilities: Finite number of outcomes Assigning probabilities: Equally likely outcomes Independence and the multiplication rule Applying the probability rules
Section 4.2 Summary Section 4.2 Exercises 4.3 Random Variables
Discrete random variables Continuous random variables Normal distributions as probability distributions
Section 4.3 Summary Section 4.3 Exercises 4.4 Means and Variances of Random Variables
The mean of a random variable Statistical estimation and the law of large numbers
Thinking about the law of large numbers Beyond the Basics: More laws of large numbers
Rules for means The variance of a random variable Rules for variances and standard deviations
Section 4.4 Summary Section 4.4 Exercises 4.5 General Probability Rules
General addition rules Conditional probability General multiplication rules Tree diagrams Bayes’s rule Independence again
Section 4.5 Summary Section 4.5 Exercises Chapter 4 Exercises
CHAPTER 5 Sampling Distributions Introduction
5.1 Toward Statistical Inference Sampling variability Sampling distributions Bias and variability Sampling from large populations Why randomize?
Section 5.1 Summary Section 5.1 Exercises 5.2 The Sampling Distribution of a Sample Mean
The mean and standard deviation of x̅ The central limit theorem A few more facts
Beyond the Basics: Weibull distributions Section 5.2 Summary Section 5.2 Exercises 5.3 Sampling Distributions for Counts and Proportions
The binomial distributions for sample counts Binomial distributions in statistical sampling Finding binomial probabilities Binomial mean and standard deviation Sample proportions Normal approximation for counts and proportions The continuity correction Binomial formula The Poisson distributions
Section 5.3 Summary
Section 5.3 Exercises Chapter 5 Exercises
CHAPTER 6 Introduction to Inference Introduction Overview of inference 6.1 Estimating with