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工程与科学统计学第三版:实用指南
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"《统计学应用于工程师与科学家》第三版是一本专门针对工程领域和科学研究人员设计的统计学基础教材。该书由William Navidi撰写,他在科罗拉多矿业学院任职,旨在帮助读者掌握在实际工作和研究中必不可少的统计分析技能。这本书是McGraw-Hill出版公司于2011年发行,此前的版本分别在2008年和2006年出版。 该书强调了版权保护,明确规定未经 McGraw-Hill Companies, Inc. 的书面许可,任何部分不得以任何形式复制、分发或存储,包括但不限于网络传输、电子储存或远程教育。这表明作者对知识的严谨性和尊重知识产权的态度。 《统计学应用于工程师与科学家》第三版的特点在于其实用性,内容涵盖了广泛的应用统计方法,如描述性统计、概率论、假设检验、回归分析、方差分析等,这些都是工程和科学工作中经常遇到的数据处理和解释工具。书中还可能包含一些辅助资源,如电子和印刷组件,但并非所有这些资源在全球范围内都可获取。 此外,本书的出版背景包括全球出版商Raghothaman Srinivasan的参与,以及Debra B. Hash作为赞助编辑的贡献,他们共同确保了内容的专业性和教材质量。开发总监的工作也确保了教材内容的持续更新和完善。 此书采用环保的酸碱纸张,印制清晰,便于学生和专业人士查阅。书后的国际标准书号(ISBN)和迈格劳希尔产品代码(MHID)便于识别和购买。对于那些希望在工程和技术研究中运用统计学来提升数据理解和决策能力的读者来说,这本书无疑是一个重要的学习资源。"
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Chapter 7 covers correlation and simple linear regression. I have worked hard to
emphasize that linear models are appropriate only when the relationship between the
variables is linear. This point is all the more important since it is often overlooked in
practice by engineers and scientists (not to mention statisticians). It is not hard to find
in the scientific literature straight-line fits and correlation coefficient summaries for
plots that show obvious curvature or for which the slope of the line is determined by
a few influential points. Therefore this chapter includes a lengthy section on checking
model assumptions and transforming variables.
Chapter 8 covers multiple regression. Model selection methods are given particular
emphasis, because choosing the variables to include in a model is an essential step in
many real-life analyses. The topic of confounding is given careful treatment as well.
Chapter 9 discusses some commonly used experimental designs and the methods
by which their data are analyzed. One-way and two-way analysis of variance meth-
ods, along with randomized complete block designs and 2
p
factorial designs, are cov-
ered fairly extensively.
Chapter 10 presents the topic of statistical quality control, discussing control charts,
CUSUM charts, and process capability; and concluding with a brief discussion of six-
sigma quality.
NEW FOR THIS EDITION
The third edition of this book is intended to extend the strengths of the second. Some
of the changes are:
• More than 250 new exercises have been included, many of which involve real data
from recently published sources.
• A new section on prediction intervals and tolerance intervals has been added to
Chapter 5.
• The material on pooled variance methods has been completely revised.
• The discussion of the effect of outliers on the correlation coefficient has been
amplified.
• Chapter 1 now contains a discussion of controlled experiments and observational
studies.
• Chapter 7 now contains a discussion of confounding in controlled experiments.
• The exposition has been improved in a number of places.
RECOMMENDED COVERAGE
The book contains enough material for a year-long course. For a one-semester course,
there are a number of options. In our three-hour course at the Colorado School of
Mines, we cover all of the first four chapters, except for joint distributions, the more
theoretical aspects of point estimation, and the exponential, gamma, and Weibull dis-
tributions. We then cover the material on confidence intervals and hypothesis testing
Preface xv
nav76337_fm.qxd 12/10/09 1:35 PM Page xv
in Chapters 5 and 6, going quickly over the two-sample methods and power calcula-
tions and omitting distribution-free methods and the chi-square and F tests. We finish
by covering as much of the material on correlation and simple linear regression in
Chapter 7 as time permits.
A course with a somewhat different emphasis can be fashioned by including more
material on probability, spending more time on two-sample methods and power, and
reducing coverage of propagation of error, simulation, or regression. Many other
options are available; for example, one may choose to include material on factorial
experiments in place of some of the preceding topics. Sample syllabi, emphasizing a
variety of approaches and course lengths, can be found on the book website
www.mhhe.com/navidi.
McGRAW-HILL CONNECT ENGINEERING
The online resources for this edition include McGraw-Hill Connect Engineering, a
web-based assignment and assessment platform that can help students to perform
better in their coursework and to master important concepts. With Connect Engineer-
ing, instructors can deliver assignments, quizzes, and tests easily online. Students can
practice important skills at their own pace and on their own schedule.
In addition, the website for Statistics for Engineers and Scientists, 3e, features data
sets for students, as well as solutions, PowerPoint lecture notes for each chapter, an
image library, and suggested syllabi for instructors. The website can be accessed at
www.mhhe.com/navidi.
ELECTRONIC TEXTBOOK OPTION
This text may be purchased in electronic form through an online resource know as
CourseSmart. Students can access the complete text online through their browsers at
approximately one-half the cost of a traditional text. In addition, purchasing the
eTextbook allows students to use CourseSmart’s web tools, which include full text
search, notes, and highlighting, and email tools for sharing notes among classmates.
More information can be found at www.CourseSmart.com.
ACKNOWLEDGMENTS
I am indebted to many people for contributions at every stage of development. I
received valuable suggestions from my colleagues Barbara Moskal, Gus Greivel,
Ashlyn Munson, and Melissa Laeser at the Colorado School of Mines. Mike Colagrosso
developed some excellent applets, and Jessica Kohlschmidt developed PowerPoint
slides to supplement the text. I am particularly grateful to Jackie Miller of The Ohio
State University, who has corrected many errors and made many valuable suggestions
for improvement.
The staff at McGraw-Hill has been extremely capable and supportive. In particular, I
would like to express my thanks to Developmental Editor Lora Neyens and Sponsoring
Editor Debra Hash for their patience and guidance in the preparation of this edition.
William Navidi
xvi Preface
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xvii
This text, through its three editions, reflects the generous contributions of well over one
hundred statistics instructors and their students, who, through numerous reviews, sur-
veys, and class tests, helped us understand how to meet their needs and how to make
improvements when we fell short. The ideas of these instructors and students are woven
throughout the book, from its content and organization to its supplements.
The author and the engineering team at McGraw-Hill are grateful to these colleagues
for their thoughtful comments and contributions during the development of the text and
its supplements and media resources. The following list represents those who have
reviewed the most recent editions.
ACKNOWLEDGMENTS
OF REVIEWERS AND
CONTRIBUTORS
Andre Adler
Illinois Institute of Technology
Derya Akleman
Texas A&M University
Sant Arora
University of Minnesota
Petro Babak
University of Alberta
Barb Barnet
University of Wisconsin, Platteville
John Beckwith
Michigan Technological University
Marla M. Bell
Kennesaw State University
André J. Butler
Mercer University
Mary Court
University of Oklahoma
John W. Daily
University of Colorado
Paul Fields
Brigham Young University
Dan Frangopol
University of Colorado
Joseph Harrington
Harvard University
Michael Hughes
Miami University
Aridaman K. Jain
New Jersey Institute of Technology
Amir Javaheri
Virginia State University
Xiaochun Jiang
North Carolina A&T State University
Steve Kachman
University of Nebraska, Lincoln
Kyungduk Ko
Boise State University
Seshavadhani Kumar
Rochester Institute of Technology
Ron Lasky
Dartmouth College
Michael Levine
Purdue University
Lia Lu
University of Illinois at Chicago
Saeed Manafzadeh
University of Illinois, Chicago
Mike McGill
Virginia Polytechnic Institute
Vince Melfi
Michigan State University
Emad Abouel Nasr
University of Houston
Mahour Parast
University of Nebraska, Lincoln
nav76337_fm.qxd 12/10/09 1:35 PM Page xvii
Richard Puerzer
Hofstra University
Balaji Rajagopalan
University of Colorado, Boulder
Adam Rickert
Drexel University
David M. Rose
University of Washington
Sheila E. Rowe
North Carolina A&T State University
Paul Savory
University of Nebraska, Lincoln
Harry Schey
Rochester Institute of Technology
Henrik Schmiediche
Texas A&M University
Mohammed A. Shayib
Prairie View A&M University
J. Y. Shen
North Carolina A&T State University
Julie A. Skipper
Wright State University
Danhong Song
Case Western Reserve University
Michael Speed
Texas A&M University
Bruce W. Turnbull
Cornell University
Vasant Waikar
Miami University
Xinhui Zhang
Wright State University
xviii Acknowledgments of Reviewers and Contributors
nav76337_fm.qxd 12/10/09 1:35 PM Page xviii
Key Features
Real-World Data Sets
With a fresh approach to the subject, the
author uses contemporary real-world data
sets to motivate students and show a direct
connection to industry and research.
Computer Output
The book contains exercises and examples
that involve interpreting, as well as
generating, computer output.
Content Overview
This book allows flexible coverage because
there are many ways to design a successful
introductory statistics course.
• Flexible coverage of probability
addresses the needs of different courses.
Allowing for a mathematically rigorous
approach, the major results are derived
from axioms, with proofs given for most
of them. On the other hand, each result
is illustrated with an example or two
to promote intuitive understanding.
Instructors who prefer a more informal
approach may therefore focus on the
examples rather than the proofs and skip
the optional sections.
• Extensive coverage of propagation of
error, sometimes called “error analysis”
or “the delta method,” is provided in a
separate chapter. The coverage is more
thorough than in most texts. The format
is flexible so that the amount of coverage
can be tailored to the needs of the
course.
• A solid introduction to simulation
methods and the bootstrap is
presented in the final sections of
Chapters 4, 5, and 6.
• Extensive coverage of linear model
diagnostic procedures in Chapter 7
includes a lengthy section on checking
model assumptions and transforming
variables. The chapter emphasizes that
linear models are appropriate only when
the relationship between the variables is
linear. This point is all the more important
since it is often overlooked in practice by
engineers and scientists (not to mention
statisticians).
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