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Applied Linear Statistical Models, 5th Michael H. Kutner
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Applied Linear Statistical Models, 5th Michael H. Kutner。全英版本,1500页,线性模型深入分析与解读,有一定的难度,国外研究生教程
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Applied Linear
Statistical Models
Fifth Edition
Michael H. Kutner
EmOlY University
Christopher
J.
Nachtsheim
University
of
Minnesota
John
Neter
University
of
Georgia
William
Li
Universlty
of
Minnesota
wa
McGraw-Hili
t:a
Irwin
Boston Burr RIdge, IL Dubuque, IA MadIson,
WI
New
York
San
FrancIsco St
LoUIs
Bangkok Bogota Caracas Kuala Lumpur LIsbon London Madnd MexIco CIty
MIlan Montreal
New Deihl SantIago Seoul Smgapore Sydney
TaIpeI
Toronto
~
McGraw-Hili
t:a
Irwin
APPUED
UNEAR
STATISTICAL
MODELS
The
McGraw·HiII Companies
Published
by
McGraw-Hill!Irwin, a business unit
of
The McGraw-Hill Companies, Inc., 1221 Avenue
of
the
Americas, New York,
NY,
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by
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1234567890DocmOC0987654
ISBN 0-07-238688-6
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Library
of
Congress Cataloging-in-Publication Data
Kutner, Michael H.
Applied linear statistical
models.-5th
ed.!
Michael H Kutner
...
let
al].
p.
cm. - (McGraw-HillfIrwin series Operations and decision sciences)
Rev.
ed. of: Applied linear regression models.
4th
ed. c2004.
Includes bibliographical references and index.
ISBN
0-07-238688-6 (acid-free paper)
1.
Regression analysis. 2. Mathematical statistics. I. Kutner, Michael H. Applied linear
regression models. II. Title. III. Series.
QA278.2.K87
2005
519.5'36-dc22
2004052447
www.mhhe.com
ies,
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.......
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........
To
Nancy, Michelle, Allison,
, Maureen, Abigael, Andrew, Henry G.,
Dorothy, Ron, David,
Dezhong, Chenghua,
Xu
Preface
vi
Linear statistical models for regression, analysis
of
variance, and experimental design are
widely used today in business administration, economics, engineering, and the social, health,
and biological sciences. Successful applications
of
these models require a sound understand-
ing
of
both the underlying theory and the practical problems that are encountered in using
the models in real-life situations. While Applied
linear
Statistical Models, Fifth Edition, is
basically an applied book,
it
seeks to blend theory and applications effectively, avoiding the
extremes
of
presenting theory in isolation and
of
giving elements
of
applications without
the needed understanding
of
the theoretical foundations.
The fifth edition differs from the fourth in a number
of
important respects.
In the area
of
regression analysis (Parts I-III):
1.
We
have reorganized the chapters for better clarity and flow
of
topics. Material from
the old Chapter
15
on normal correlation models has been integrated throughout the
text where appropriate. Much
of
the material is now found in an expanded Chapter
2, which focuses
on
inference in regression analysis. Material from the old Chapter 7
pertaining to polynomial and interaction regression models and from old Chapter
11
on quantitative predictors has been integrated into a new Chapter 8 called, "Models
for Quantitative and Qualitative Predictors." Material on model validation from old
Chapter
lOis
now fully integrated with updated material on model selection in a new
Chapter 9 entitled,
"Building the Regression Model I: Model Selection and Validation."
2.
We
have added material on important techniques for data mining, including regression
trees and neural network models in Chapters
11
and 13, respectively.
3. The chapter on logistic regression (Chapter 14) has been extensively revised and
expanded to include a more thorough treatment
of
logistic, probit, and complemen-
tary log-log models, logistic regression residuals, model selection, model assessment,
logistic regression diagnostics, and goodness
of
fit tests.
We
have also developed new
material on polytomous (multicategory) nominal logistic regression models and poly-
tomous ordinal logistic regression models.
4.
We
have expanded the discussion
of
model selection methods and criteria. The Akaike
information criterion and Schwarz Bayesian criterion have been added, and a greater
emphasis is placed on the use
of
cross-validation for model selection and validation.
In
the areas pertaining
to
the design and analysis
of
experimental and observational studies
(Parts
IV-VI):
5. In the previous edition, Chapters 16 through 25 emphasized the analysis
of
variance,
and the design
of
experiments was not encountered formally until Chapter 26. We
have completely reorganized Parts IV-VI, emphasizing the design
of
experimental and
observational studies from the start. In a new Chapter 15, we provide an overview
of
the basic concepts and planning approaches used in the design
of
experimental and
observational studies, drawing in part from material from old Chapters 16, 26, and
27. Fundamental concepts
of
experimental design, including the basic types
of
factors,
Preface
vii
treatments, experimental units, randomization, and blocking are described in detail.
This is followed by an overview
of
standard experimental designs, as well as the basic
types
of
observational studies, including cross-sectional, retrospective, and prospective
studies. Each
of
the design topics introduced in Chapter
15
is then covered in greater
detail in the chapters that follow.
We
emphasize the importance
of
good statistical
design
of
scientific studies, and make the point that proper design often leads to a
simple
analYSIS.
We
note that the statistical analysis techniques used for observational
and experimental studies are often the same, but the ability to "prove" cause-and-effect
requires a carefully designed experimental study.
6. Previously, the planning
of
sample sizes was covered
-in
Chapter 26.
We
now present
material on planning
of
sample sizes in the relevant chapter, rather than devoting a
single, general discussion to this issue.
7.
We
have expanded and updated
our
coverage (Section 24.2) on the interpretation
of
interaction plots for multi-factor studies.
8.
We
have reorganized and expanded the material on repeated measures designs in Chap-
ter 27.
In
particular, we introduce methods for handling the analysis
of
factor effects
when interactions between subjects and treatments are important, and when interactions
between factors are important.
9.
We
have added material on the design and analysis
of
balanced incomplete block
experiments in Section 28.1, including the planning
of
sample sizes. A new appendix
(B.15) has been added that provides standard balanced incomplete block designs.
10.
We
have added new material on robust product and process design experiments in
Chapter 29, and illustrate its use with a case study from the automotive industry. These
experiments are frequently used in industrial studies to identify product
or
process
designs that exhibit low levels
of
variation.
The remaining changes pertain to both regression analysis (Parts I-III) and the design and
analysis
of
experimental and observational studies (Parts IV-VI):
11. We have made extensive revisions to the problem material. Problem data sets are
generally larger and more challenging, and we have included a large number
of
new
case data sets in Appendix
C.
In addition, we have added a new category
of
chapter
exercises, called Case Studies. These are open-ended problems that require students,
given an overall objective, to carry out complete analyses
of
the various case data sets in
Appendix C. They are distinct from the material in the Problems and Projects sections,
which frequently ask students to simply carry out specific .analytical procedures.
12.
We
have substantially expanded the amount
of
graphic presentation, including much
greater use
of
scatter plot matrices, three-dimensional rotating plots, three-dimensional
response surface and contour plots, conditional effects plots, and main effects and
interaction plots.
13. Throughout the text, we have made extensive revisions in the exposition on the basis
of
classroom experience to improve the clarity
of
the presentation.
We
have included in this book not only the more conventional topics in regression and
design, but also topics that are frequently slighted, though important in practice.
We
devote
three chapters (Chapters
9-11)
to the model-building process for regression, including
computer-assisted selection procedures for identifying good subsets
of
predictor variables
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