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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
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Marketing Assistant: Cate McCaffery
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Photo Researcher: Candice Cheesman
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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

W. H. Freeman and Company
One New York Plaza
Suite 4500
New York, NY 10004-1562
www.macmillanlearning.com

http://www.macmillanlearning.com

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 Confidence

Statistical confidence
Confidence intervals
Confidence interval for a population mean
How confidence intervals behave
Choosing the sample size
Some cautions

Section 6.1 Summary
Section 6.1 Exercises
6.2 Tests of Significance

The reasoning of significance tests
Stating hypotheses
Test statistics
P-values
Statistical significance
Tests for a population mean
Two-sided significance tests and confidence intervals
The P-value versus a statement of significance

Section 6.2 Summary
Section 6.2 Exercises
6.3 Use and Abuse of Tests

Choosing a level of significance
What statistical significance does not mean
Don’t ignore lack of significance
Statistical inference is not valid for all sets of data
Beware of searching for significance

Section 6.3 Summary
Section 6.3 Exercises
6.4 Power and Inference as a Decision

Power
Increasing the power
Inference as decision
Two types of error
Error probabilities
The common practice of testing hypotheses

Section 6.4 Summary
Section 6.4 Exercises
Chapter 6 Exercises

CHAPTER 7 Inference for Means

Introduction

7.1 Inference for the Mean of a Population
The t distributions
The one-sample t confidence interval
The one-sample t test
Matched pairs t procedures
Robustness of the t procedures

Beyond the Basics: The bootstrap
Section 7.1 Summary
Section 7.1 Exercises
7.2 Comparing Two Means

The two-sample z statistic
The two-sample t procedures
The two-sample t confidence interval
The two-sample t significance test
Robustness of the two-sample procedures
Inference for small samples
Software approximation for the degrees of freedom
The pooled two-sample t procedures

Section 7.2 Summary
Section 7.2 Exercises
7.3 Additional Topics on Inference

Choosing the sample size
Inference for non-Normal populations

Section 7.3 Summary
Section 7.3 Exercises
Chapter 7 Exercises

CHAPTER 8 Inference for Proportions
Introduction

8.1 Inference for a Single Proportion
Large-sample confidence interval for a single proportion

Beyond the Basics: The plus four confidence interval for a single proportion
Significance test for a single proportion
Choosing a sample size for a confidence interval
Choosing a sample size for a significance test

Section 8.1 Summary
Section 8.1 Exercises
8.2 Comparing Two Proportions

Large-sample confidence interval for a difference in proportions
Beyond the Basics: The plus four confidence interval for a difference in proportions

Significance test for a difference in proportions
Choosing a sample size for two sample proportions

Beyond the Basics: Relative risk
Section 8.2 Summary

Section 8.2 Exercises
Chapter 8 Exercises

PART III Topics in Inference
CHAPTER 9 Inference for Categorical Data
Introduction

9.1 Inference for Two-Way Tables
The hypothesis: No association
Expected cell counts
The chi-square test
Computations
Computing conditional distributions
The chi-square test and the z test

Beyond the Basics: Meta-analysis
Section 9.1 Summary
Section 9.1 Exercises
9.2 Goodness of Fit
Section 9.2 Summary
Section 9.2 Exercises
Chapter 9 Exercises

CHAPTER 10 Inference for Regression
Introduction

10.1 Simple Linear Regression
Statistical model for linear regression
Preliminary data analysis and inference considerations
Estimating the regression parameters
Checking model assumptions
Confidence intervals and significance tests
Confidence intervals for mean response
Prediction intervals
Transforming variables

Beyond the Basics: Nonlinear regression
Section 10.1 Summary
Section 10.1 Exercises
10.2 More Detail about Simple Linear Regression

Analysis of variance for regression
The ANOVA F test
Calculations for regression inference
Inference for correlation

Section 10.2 Summary
Section 10.2 Exercises
Chapter 10 Exercises

CHAPTER 11 Multiple Regression
Introduction

11.1 Inference for Multiple Regression
Population multiple regression equation
Data for multiple regression
Multiple linear regression model
Estimation of the multiple regression parameters
Confidence intervals and significance tests for regression coefficients
ANOVA table for multiple regression
Squared multiple correlation R2

Section 11.1 Summary
Section 11.1 Exercises
11.2 A Case Study

Preliminary analysis
Relationships between pairs of variables
Regression on high school grades
Interpretation of results
Examining the residuals
Refining the model
Regression on SAT scores
Regression using all variables
Test for a collection of regression coefficients

Beyond the Basics: Multiple logistic regression
Section 11.2 Summary
Section 11.2 Exercises
Chapter 11 Exercises

CHAPTER 12 One-Way Analysis of Variance
Introduction

12.1 Inference for One-Way Analysis of Variance
Data for one-way ANOVA
Comparing means
The two-sample t statistic
An overview of ANOVA
The ANOVA model
Estimates of population parameters
Testing hypotheses in one-way ANOVA
The ANOVA table
The F test
Software

Beyond the Basics: Testing the equality of spread
Section 12.1 Summary
Section 12.1 Exercises
12.2 Comparing the Means

Contrasts

Multiple comparisons
Power

Section 12.2 Summary
Section 12.2 Exercises
Chapter 12 Exercises

CHAPTER 13 Two-Way Analysis of Variance
Introduction

13.1 The Two-Way ANOVA Model
Advantages of two-way ANOVA
The two-way ANOVA model
Main effects and interactions

13.2 Inference for Two-Way ANOVA
The ANOVA table for two-way ANOVA

Chapter 13 Summary
Chapter 13 Exercises

Tables
Answers to Odd-Numbered Exercises
Notes and Data Sources
Index

To Teachers: About This Book

Statistics is the science of data. Introduction to the Practice of Statistics (IPS) is an introductory text based on this
principle. We present methods of basic statistics in a way that emphasizes working with data and mastering statistical
reasoning. IPS is elementary in mathematical level but conceptually rich in statistical ideas. After completing a course
based on our text, we would like students to be able to think objectively about conclusions drawn from data and use
statistical methods in their own work.

In IPS, we combine attention to basic statistical concepts with a comprehensive presentation of the elementary
statistical methods that students will find useful in their work. IPS has been successful for several reasons:

1. IPS examines the nature of modern statistical practice at a level suitable for beginners. We focus on the
production and analysis of data as well as the traditional topics of probability and inference.

2. IPS has a logical overall progression, so data production and data analysis are a major focus, while inference is
treated as a tool that helps us draw conclusions from data in an appropriate way.

3. IPS presents data analysis as more than a collection of techniques for exploring data. We emphasize systematic
ways of thinking about data. Simple principles guide the analysis: always plot your data; look for overall patterns
and deviations from them; when looking at the overall pattern of a distribution for one variable, consider shape,
center, and spread; for relations between two variables, consider form, direction, and strength; always ask
whether a relationship between variables is influenced by other variables lurking in the background. We warn
students about pitfalls in clear cautionary discussions.

4. IPS uses real examples to drive the exposition. Students learn the technique of least-squares regression and how
to interpret the regression slope. But they also learn the conceptual ties between regression and correlation and
the importance of looking for influential observations.

5. IPS is aware of current developments both in statistical science and in teaching statistics. Brief, optional Beyond
the Basics sections give quick overviews of topics such as density estimation, scatterplot smoothers, data mining,
nonlinear regression, and meta-analysis. Chapter 16 gives an elementary introduction to the bootstrap and other
computer-intensive statistical methods.

The title of the book expresses our intent to introduce readers to statistics as it is used in practice. Statistics in
practice is concerned with drawing conclusions from data. We focus on problem solving rather than on methods that
may be useful in specific settings.

GAISE The College Report of the Guidelines for Assessment and Instruction in Statistics Education (GAISE) Project
(www.amstat.org/education/gaise/) was funded by the American Statistical Association to make recommendations for
how introductory statistics courses should be taught. This report and its update contain many interesting teaching
suggestions, and we strongly recommend that you read it. The philosophy and approach of IPS closely reflect the
GAISE recommendations. Let’s examine each of the latest recommendations in the context of IPS.

1. Teach statistical thinking. Through our experiences as applied statisticians, we are very familiar with the
components that are needed for the appropriate use of statistical methods. We focus on formulating questions,
collecting and finding data, evaluating the quality of data, exploring the relationships among variables,
performing statistical analyses, and drawing conclusions. In examples and exercises throughout the text, we
emphasize putting the analysis in the proper context and translating numerical and graphical summaries into
conclusions.

2. Focus on conceptual understanding. With the software available today, it is very easy for almost anyone to apply
a wide variety of statistical procedures, both simple and complex, to a set of data. Without a firm grasp of the
concepts, such applications are frequently meaningless. By using the methods that we present on real sets of data,
we believe that students will gain an excellent understanding of these concepts. Our emphasis is on the input
(questions of interest, collecting or finding data, examining data) and the output (conclusions) for a statistical
analysis. Formulas are given only where they will provide some insight into concepts.

3. Integrate real data with a context and a purpose. Many of the examples and exercises in IPS include data that we
have obtained from collaborators or consulting clients. Other data sets have come from research related to these
activities. We have also used the Internet as a data source, particularly for data related to social media and other
topics of interest to undergraduates. Our emphasis on real data, rather than artificial data chosen to illustrate a

http://www.amstat.org/education/gaise/

calculation, serves to motivate students and help them see the usefulness of statistics in everyday life. We also
frequently encounter interesting statistical issues that we explore. These include outliers and nonlinear
relationships. All data sets are available from the text website.

4. Foster active learning in the classroom. As we mentioned earlier, we believe that statistics is exciting as
something to do rather than something to talk about. Throughout the text, we provide exercises in Use Your
Knowledge sections that ask the students to perform some relatively simple tasks that reinforce the material just
presented. Other exercises are particularly suited to being worked on and discussed within a classroom setting.

5. Use technology for developing concepts and analyzing data. Technology has altered statistical practice in a
fundamental way. In the past, some of the calculations that we performed were particularly difficult and tedious.
In other words, they were not fun. Today, freed from the burden of computation by software, we can concentrate
our efforts on the big picture: what questions are we trying to address with a study and what can we conclude
from our analysis?

6. Use assessments to improve and evaluate student learning. Our goal for students who complete a course based
on IPS is that they are able to design and carry out a statistical study for a project in their capstone course or
other setting. Our exercises are oriented toward this goal. Many ask about the design of a statistical study and the
collection of data. Others ask for a paragraph summarizing the results of an analysis. This recommendation
includes the use of projects, oral presentations, article critiques, and written reports. We believe that students
using this text will be well prepared to undertake these kinds of activities. Furthermore, we view these activities
not only as assessments but also as valuable tools for learning statistics.

Teaching Recommendations We have used IPS in courses taught to a variety of student audiences. For general
undergraduates from mixed disciplines, we recommend covering Chapters 1 through 8 and Chapters 9, 10, or 12. For a
quantitatively strong audience—sophomores planning to major in actuarial science or statistics—we recommend
moving more quickly. Add Chapters 10 and 11 to the core material in Chapters 1 through 8. In general, we recommend
deemphasizing the material on probability because these students will take a probability course later in their program.
For beginning graduate students in such fields as education, family studies, and retailing, we recommend that the
students read the entire text (Chapters 11 and 13 lightly), again with reduced emphasis on Chapter 4 and some parts of
Chapter 5. In all cases, beginning with data analysis and data production (Part I) helps students overcome their fear of
statistics and builds a sound base for studying inference. We believe that IPS can easily be adapted to a wide variety of
audiences.

The Ninth Edition: What’s New?
Chapter 1 now begins with a short section giving an overview of data.
“Toward Statistical Inference” (previously Section 3.3), which introduces the concepts of statistical inference and
sampling distributions, has been moved to Section 5.1 to better assist with the transition from a single data set to
sampling distributions.
Coverage of mosaic plots as a visual tool for relationships between two categorical variables has been added to
Chapters 2 and 9.
Chapter 3 now begins with a short section giving a basic overview of data sources.
Coverage of equivalence testing has been added to Chapter 7.
There is a greater emphasis on sample size determination using software in Chapters 7 and 8.
Resampling and bootstrapping are now introduced in Chapter 7 rather than Chapter 6.
“Inference for Categorical Data” is the new title for Chapter 9, which includes goodness of fit as well as
inference for two-way tables.
There are more JMP screenshots and updated screenshots of Minitab, Excel, and SPSS outputs.
Design A new design incorporates colorful, revised figures throughout to aid the students’ understanding of text
material. Photographs related to chapter examples and exercises make connections to real-life applications and
provide a visual context for topics. More figures with software output have been included.
Exercises and Examples More than 30% of the exercises are new or revised, and there are more than 1700
exercises total. Exercise sets have been added at the end of sections in Chapters 9 through 12. To maintain the
attractiveness of the examples to students, we have replaced or updated a large number of them. More than 30%
of the 430 examples are new or revised. A list of exercises and examples categorized by application area is
provided on the inside of the front cover.

In addition to the new ninth edition enhancements, IPS has retained the successful pedagogical features from previous
editions:

Look Back At key points in the text, Look Back margin notes direct the reader to the first explanation of a topic,
providing page numbers for easy reference.

Caution Warnings in the text, signaled by a caution icon, help students avoid common errors and
misconceptions.

Challenge Exercises More challenging exercises are signaled with an icon. Challenge exercises are varied: some
are mathematical, some require open-ended investigation, and others require deeper thought about the basic
concepts.

Applets Applet icons are used throughout the text to signal where related interactive statistical applets can be
found on the IPS website and in LaunchPad.
Use Your Knowledge Exercises We have found these exercises to be a very useful learning tool. They appear
throughout each section and are listed, with page numbers, before the section-ending exercises.
Technology output screenshots Most statistical analyses rely heavily on statistical software. In this book, we
discuss the use of Excel 2013, JMP 12, Minitab 17, SPSS 23, CrunchIt, R, and a TI-83/-84 calculator for
conducting statistical analysis. As specialized statistical packages, JMP, Minitab, and SPSS are the most popular
software choices both in industry and in colleges and schools of business. R is an extremely powerful statistical
environment that is free to anyone; it relies heavily on members of the academic and general statistical
communities for support. As an all-purpose spreadsheet program, Excel provides a limited set of statistical
analysis options in comparison. However, given its pervasiveness and wide acceptance in industry and the
computer world at large, we believe it is important to give Excel proper attention. It should be noted that for
users who want more statistical capabilities but want to work in an Excel environment, there are a number of
commercially available add-on packages (if you have JMP, for instance, it can be invoked from within Excel).
Finally, instructions are provided for the TI-83/-84 calculators.

Even though basic guidance is provided in the book, it should be emphasized that IPS is not bound to any of these
programs. Computer output from statistical packages is very similar, so you can feel quite comfortable using any one
these packages.

Acknowledgments
We are pleased that the first eight editions of Introduction to the Practice of Statistics have helped to move the teaching
of introductory statistics in a direction supported by most statisticians. We are grateful to the many colleagues and
students who have provided helpful comments, and we hope that they will find this new edition another step forward.
In particular, we would like to thank the following colleagues who offered specific comments on the new edition:

Ali Arab, Georgetown University
Tessema Astatkie, Dalhousie University
Fouzia Baki, McMaster University
Lynda Ballou, New Mexico Institute of Mining and Technology
Sanjib Basu, Northern Illinois University
David Bosworth, Hutchinson Community College

Max Buot, Xavier University
Nadjib Bouzar, University of Indianapolis
Matt Carlton, California Polytechnic State University–San Luis Obispo
Gustavo Cepparo, Austin Community College
Pinyuen Chen, Syracuse University
Dennis L. Clason, University of Cincinnati–Blue Ash College
Tadd Colver, Purdue University
Chris Edwards, University of Wisconsin–Oshkosh
Irina Gaynanova, Texas A&M University
Brian T. Gill, Seattle Pacific University
Mary Gray, American University
Gary E. Haefner, University of Cincinnati
Susan Herring, Sonoma State University
Lifang Hsu, Le Moyne College
Tiffany Kolba, Valparaiso University
Lia Liu, University of Illinois at Chicago
Xuewen Lu, University of Calgary
Antoinette Marquard, Cleveland State University
Frederick G. Schmitt, College of Marin
James D. Stamey, Baylor University
Engin Sungur, University of Minnesota–Morris
Anatoliy Swishchuk, University of Calgary
Richard Tardanico, Florida International University
Melanee Thomas, University of Calgary
Terri Torres, Oregon Institute of Technology
Mahbobeh Vezvaei, Kent State University
Yishi Wang, University of North Carolina–Wilmington
John Ward, Jefferson Community and Technical College
Debra Wiens, Rocky Mountain College
Victor Williams, Paine College
Christopher Wilson, Butler University
Anne Yust, Birmingham-Southern College
Biao Zhang, The University of Toledo
Michael L. Zwilling, University of Mount Union

The professionals at Macmillan, in particular, Terri Ward, Karen Carson, Jorge Amaral, Emily Tenenbaum, Ed
Dionne, Blake Logan, and Susan Wein, have contributed greatly to the success of IPS. In addition, we would like to
thank Tadd Colver at Purdue University for his valuable contributions to the ninth edition, including authoring the
back-of-book answers, solutions, and Instructor’s Guide. We’d also like to thank Monica Jackson at American
University for accuracy reviewing the back-of-book answers and solutions and for authoring the test bank. Thanks also
to Michael Zwilling at University of Mount Union for accuracy reviewing the test bank, Christopher Edwards at
University of Wisconsin Oshkosh for authoring the lecture slides, and James Stamey at Baylor University for authoring
the Clicker slides.

Most of all, we are grateful to the many friends and collaborators whose data and research questions have enabled
us to gain a deeper understanding of the science of data. Finally, we would like to acknowledge the contributions of
John W. Tukey, whose contributions to data analysis have had such a great influence on us as well as a whole
generation of applied statisticians.

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