"This is a first-class book dealing with one of the most important areas of current research in applied statistics…the methods described are widely applicable…the standard of exposition is extremely high."
--Short Book Reviews from the International Statistical Institute
"The new chapters (10-14) improve an already excellent resource for research and instruction. Their content expands the coverage of the book to include models for discrete level-1 outcomes, non-nested level-2 units, incomplete data, and measurement error---all vital topics in contemporary social statistics. In the tradition of the first edition, they are clearly written and make good use of interesting substantive examples to illustrate the methods. Advanced graduate students and social researchers will find the expanded edition immediately useful and pertinent to their research."
--TED GERBER, Sociology, University of Arizona
"Chapter 11 was also exciting reading and shows the versatility of the mixed model with the EM algorithm. There was a new revelation on practically every page. I found the exposition to be extremely clear. It was like being led from one treasure room to another, and all of the gems are inherently useful. These are problems that researchers face everyday, and this chapter gives us an excellent alternative to how we have traditionally handled these problems."
--PAUL SWANK, Houston School of Nursing, University of Texas, Houston
Popular in the
First Edition for its rich, illustrative examples and lucid explanations of the theory and use of hierarchical linear models (HLM), the book has been reorganized into four parts with four completely new chapters. The first two parts, Part I on "The Logic of Hierarchical Linear Modeling" and Part II on "Basic Applications" closely parallel the first nine chapters of the previous edition with significant expansions and technical clarifications, such as:
* An intuitive introductory summary of the basic procedures for estimation and inference used with HLM models that only requires a minimal level of mathematical sophistication in Chapter 3
* New section on multivariate growth models in Chapter 6
* A discussion of research synthesis or meta-analysis applications in Chapter 7
* Data analytic advice on centering of level-1 predictors and new material on plausible value intervals and robust standard estimators
While the first edition confined its attention to continuously distributed outcomes at level 1, this second edition now includes coverage of an array of outcomes types in Part III:
* New Chapter 10 considers applications of hierarchical models in the case of binary outcomes, counted data, ordered categories, and multinomial outcomes using detailed examples to illustrate each case
* New Chapter 11 on latent variable models, including estimating regressions from missing data, estimating regressions when predictors are measured with error, and embedding item response models within the framework of the HLM model
* New introduction to the logic of Bayesian inference with applications to hierarchical data (Chapter 13)
The authors conclude in Part IV with the statistical theory and computations used throughout the book, including univariate models with normal level-1 errors, multivariate linear models, and hierarchical generalized linear models.
To handle this, Hierarchical Linear Models were developed. The book gives a detailed treatment. A very comprehensive discussion. Including the handling of meta-analysis, where we wish to combine results across different studies. Which then involves using empirical Bayesian estimates. This method has also seen important usage in evaluating medical studies, conducted by different researchers on the same topic.
The book also illustrates the essential development of non-trivial computer programs to perform the gruntwork.
You will need a solid background in statistics to find this book useful. At a minimum, a year of statistics at the undergraduate level.
Average customer rating:
- Lacking in going from abstract to application
- Finite Mathematics for Business Economics, Life Sciences and Social Sciences (10th Edition)
- NOT USER FRIENDLY!
- Sound choice for a finite math textbook
- Many exercises in economics, life and the social sciences
|
Finite Mathematics for Business Economics, Life Sciences and Social Sciences (10th Edition)
Raymond A. Barnett ,
Michael R. Ziegler , and
Karl E. Byleen
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The Knowledge Management Toolkit: Orchestrating IT, Strategy, and Knowledge Platforms (2nd Edition)
ASIN: 0131139622 |
Book Description
Designed to be accessible, this book develops a thorough, functional understanding of mathematical concepts in preparation for their application in other areas. Coverage concentrates on developing concepts and ideas followed immediately by developing computational skills and problem solving.
This book features a collection of important topics from mathematics of finance, linear algebra, linear programming, probability, and statistics, with an emphasis on cross-discipline principles and practices.
For the professional who wants to acquire essential mathematical tools for application in business, economics, and the life and social sciences.
Customer Reviews:
Lacking in going from abstract to application.......2007-09-23
This review is for the 11th edition of the book. I am using this for a business math course and overall the book is lacking. Many of the examples presented in the chapters are simplistic and have no relation to the difficulty of the application examples. It's unfortunate that the solutions manual while containing the solutions to the examples does not go into more detail in how to solve these type of problems. While it appears the authors goal is to go from abstract to application, he misses the mark. I find myself consulting external resources for almost every chapter. Not recommended.
Finite Mathematics for Business Economics, Life Sciences and Social Sciences (10th Edition).......2005-09-17
They sent me the wrong book. I ordered the Finite Mathematics for Business Economics, Life Sciences and Social Sciences (10th Edition)by Raymond A. Barnett and received the solution manual. I feel very disapointed with this purchase.
NOT USER FRIENDLY!.......2005-09-08
I am using this textbook for my Math for Business and Economics class and it is terrible. This book is not written taking into consideration the audience "Business Majors". Concepts are better explained in the Chapter review than under the sections they are being presented. There is only 1 example for each new concept and definitions are written using one or two definitions of new concepts within it. If this book is intended to take the student from the abstract concept to the real world it sure doesn't do this. I understood the material better in my Intermediate Algebra class than I do now.
Sound choice for a finite math textbook.......2004-06-27
This is a very sound choice as a textbook for a course in finite mathematics. The coverage is appropriate, the level suitable for the non-math major, the explanations are excellent and the authors take the title seriously.
The topics are covered in the following order:
* Elementary functions and their graphs.
* The mathematics of finance.
* Matrices and systems of linear equations.
* Linear inequalities and linear programming.
* Logic, set theory and basic counting.
* Probability and probability distributions.
* Basic game and decision theory.
* Markov chains.
There are many exercises and at the end of each section there is a set of basic exercises followed by a collection of applied problems. The set of applied problems is split into three categories: business & economics, life sciences and social sciences. Since finite mathematics is often a preparation for students to work in these fields, this format is what impressed me the most. With all of these "real world" problems to work as part of their study, no student using this book could ever legitimately say that they see no purpose to their studies. Solutions to the odd-numbered problems are included.
I came into contact with this book after my choice of textbook was irrevocable. Had I seen it earlier, it would have been the one I used.
Many exercises in economics, life and the social sciences.......2004-06-26
This is a very sound choice as a textbook for a course in finite mathematics. The coverage is appropriate, the level suitable for the non-math major, the explanations are excellent and the authors take the title seriously.
The topics are covered in the following order:
* Elementary functions and their graphs.
* The mathematics of finance.
* Matrices and systems of linear equations.
* Linear inequalities and linear programming.
* Logic, set theory and basic counting.
* Probability and probability distributions.
* Basic game and decision theory.
* Markov chains.
There are many exercises and at the end of each section there is a set of basic exercises followed by a collection of applied problems. The set of applied problems is split into three categories: business & economics, life sciences and social sciences. Since finite mathematics is often a preparation for students to work in these fields, this format is what impressed me the most. With all of these "real world" problems to work as part of their study, no student using this book could ever legitimately say that they see no purpose to their studies. Solutions to the odd-numbered problems are included.
I came into contact with this book after my choice of textbook was irrevocable. Had I seen it earlier, it would have been the one I used.
Average customer rating:
- very nice conceptual overview
- Not for the practitioner
- Trash
- Excellent Introduction, Sparse on Details
- A Good Introductory Survey
|
Scientific Computing
Michael T. Heath
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ASIN: 0072399104 |
Book Description
Heath 2/e, presents a broad overview of numerical methods for solving all the major problems in scientific computing, including linear and nonlinear equations, least squares, eigenvalues, optimization, interpolation, integration, ordinary and partial differential equations, fast Fourier transforms, and random number generators. The treatment is comprehensive yet concise, software-oriented yet compatible with a variety of software packages and programming languages. The book features more than 160 examples, 500 review questions, 240 exercises, and 200 computer problems. Changes for the second edition include: expanded motivational discussions and examples; formal statements of all major algorithms; expanded discussions of existence, uniqueness, and conditioning for each type of problem so that students can recognize "good" and "bad" problem formulations and understand the corresponding quality of results produced; and expanded coverage of several topics, particularly eigenvalues and constrained optimization. The book contains a wealth of material and can be used in a variety of one- or two-term courses in computer science, mathematics, or engineering. Its comprehensiveness and modern perspective, as well as the software pointers provided, also make it a highly useful reference for practicing professionals who need to solve computational problems.
Customer Reviews:
very nice conceptual overview.......2006-07-22
Wow, people seem to be really split on this book. I had Mike Heath for numerical analysis/scientific computing and he was an excellent instructor, one of the best lecturers I've ever had. (As a consequence, I have a hard time separating the book and the class, so judge accordingly.) The book is based on his lecture notes, though he added some material and didn't cover every topic in the book. Just reading the book is useful to give you an overview of the point behind different methods. The goal of the class for which this book was written is actually quite conceptual. It was to give scientists (that's me: a stats researcher who makes heavy use of numerical computation) and CS people in areas other than scientific computing a leg up. It was only a first class for people in scientific computing, the rough equivalent of intro Physics or intro Probability/Stats for people in those respective majors. However, you *won't* be prepared to "roll your own" from this book. In fact, at the beginning of the semester Heath was very careful to note that if you have the opportunity to use a library function for most numerical programming, you are nuts to roll your own. Why? Numerical algorithms are usually extremely complicated and the authors of the code often spend years developing careful expertise on them. Frequently the formulas used to elucidate a given method are NOT the ones used to implement it. You need error traps, tricks to handle ill-scaling and other special cases, etc. These are things that someone who has a one-semester, superficial understanding of a topic simply won't have. So consider the book on the goals it set: it is an overview of a field. If you want to learn more about any one topic, you have to dig deeper and consult references and other works, but this is a good place to start. For this, the book serves admirably.
Not for the practitioner.......2005-11-17
If you are interested in Scientific computing from the viewpoint of the end user that is the guy who uses the method to solve practical engineering problems then this book is lacking.
Not enough methods in this book to constitute an introductory survey of the field. Every chapter gets heavy dose mathematical treatment, apparently Heath loves his math but for the rest of us it doesnt translate into know-how. Know how to solve equations using computational techniques. Very few derivations to back his mathematical swagger, very few examples (if any) and fewer numerical schemes to solve problems. Many of the chapters receive cursory treatment such as PDE's get about 70 pages of print. Far too little to do anyone any good.
He does talk about interesting issues such as conditioning and error analysis and computer precision and memory issues but it is done from such a superficial viewpoint that one cannot use anything to improve ones code. Not recommended if you want to learn numerical methods even if you have an excellent professor to learn from. His chapter on FFT's was even more abstruse and there was hardly any methods with which to solve PDE's.
I had this for a graduate course in Numerical Methods but ended up using Hoffman's excellent book on Numerical Methods.
Trash.......2005-10-14
If you want to have a solid understanding of numerical computation, this book is definitely the last choice. Many theorems are given without any proof or even intuitions behind them in this book. Even when a proof is provided, it's often far from rigorous. The organization of chapters is the worst I have ever seen, revelant materials are scattered over several different locations rather than put together. Take the SVD for example, it is mentioned in the end of chapter 3, but reappears in chapter 4, which is very confusing. If you are new to this area, please don't read this book. It gives you many many facts without explanations, which I think is not a good way to learn new things. David S. Watkins' Fundamentals of Matrix Computations is a lot better and easier to understand. It also emcompasses many detailed treatments of various theorems. If you have bought Heath's book, don't be sad, at least it can serve as a coaster.
Excellent Introduction, Sparse on Details.......2004-11-20
While sparse on the details of many of the algorithms and theorems mentioned, as an introduction it covers a broad range of material-enough for two semesters of study. The writing is lucid, and when a proof of a theorem is given, it is easy to follow and explained in english afterward. Rationale is given for everything, which is a great benefit to a student not familiar with the nuances of sophisticated linear algebra.
A Good Introductory Survey.......2002-11-05
This book excels at presenting a reader with little to no knowledge in computer science and a mild mathematical background (knowledge of differential equations as a prerequisite) with the fundamental concepts regarding scientific computing. The presentation of pseudo-code algorithms helps smooth the transition from analytical (pencil and paper) thinking to numerical thinking. The algorithms are presented in a manner such tha anyone with access to dozens of possible environments can apply them, though they are by no means complete, thus requiring some thought into the processes. The material covered is 110% of what an engineer will want to know, 90% of what an applied mathematician will want to know, and 45% of what a numerical analyist will want to know. In all, a great book to begin a foray into numerical computing.
Average customer rating:
- Great introduction to mathematical economics!
- A must read text book for any economics undergrad student
- A must read for graduate students in economics
- not so good
- The best math textbook for economist
|
Fundamental Methods of Mathematical Economics
Alpha C Chiang
Manufacturer: McGraw-Hill/Irwin
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ASIN: 0070108137 |
Book Description
The best-selling, best known text in Mathematical Economics course, Chiang teaches the basic mathematical methods indispensable for understanding current economic literature. the book's patient explanations are written in an informal, non-intimidating style. To underscore the relevance of mathematics to economics, the author allows the economist's analytical needs to motivate the study of related mathematical techniques; he then illustrates these techniques with appropriate economics models. Graphic illustrations often visually reinforce algebraic results. Many exercise problems serve as drills and help bolster student confidence. These major types of economic analysis are covered: statics, comparative statics, optimization problems, dynamics, and mathematical programming. These mathematical methods are introduced: matrix algebra, differential and integral calculus, differential equations, difference equations, and convex sets.
Customer Reviews:
Great introduction to mathematical economics!.......2007-07-18
I enjoy Chiang's writing style. I've been reading up on mathematical methods in preparation for a masters econ program, and feel very comfortable with the material thanks to this textbook. The international edition is a good bargain.
A must read text book for any economics undergrad student.......2006-04-02
I found it extremely easy to read and at the same time rigorous enough to settle the bases. The author knows very deeply the economics students needs of mathematical methods and achieves a precise and complete explanation of all notions I needed to know for my undergrad course. I strongly recommend it during the first or second year.
A must read for graduate students in economics.......2006-02-26
Alpha Chiang's text should serve as the foundation for all quantitive analysis done in economic theory. It is an invaluable teaching tool for graduate students in economics and will help them better understand the mathematical techniques that have become so necessary for economic modeling.
I am not a highly quantitative person myself, but I found Chiang's book comprehensible and a useful reference guide in my gradaute economics classes. Along with Hal Varian's "Microeconomic Theory" and Jan Kmenta's "Econometrics", I would say that Chiang's "Fundamentals of Mathematical Economics" should serve as sacred literature for any prospective graduate student in economics.
not so good.......2005-10-14
the text carries to excess the concept of "keeping the presentation as simple as possible". but in general you cannot understand or solve problems with a fifth grader's ability to abstract them.
especially the relunctance to use matrix notation makes some topics actually harder to understand once they become more complicated.
furthermore I find the structure quite confusing since the text amounts to a monotous blabla - clear definitions might be helpful and some rigor would keep the reader conscious instead of drifting off. after all the text is not so bad but I think we deserve something better. blume might be better.
The best math textbook for economist.......2005-09-30
That is why it used everywhere, in nearly all economic departments. I strongly recommend you buy this book. It really helped me in my undergrad, and it is helping in my graduate courses. If you want to buy another book to accompany this, get Simon and Blume book. One person (probably little masochistic) was saying that Chiang has so many examples, blah, blah, blah. Look, not everyone is a math genius, undergraduate student's need Chiang, it's even useful for graduates. Math is used quite too excessively in economics...showing off?
Average customer rating:
- Kind of old stuffs
- Optimization Mini-Library
- It's not the technique, it's the logic behind it
- improve your problem solving ability
- get this book!, read it!, understand it! :)
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How to Solve It: Modern Heuristics
Zbigniew Michalewicz
Manufacturer: Springer
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Mathematics and Plausible Reasoning (Volume I)
Accessories:
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Algorithms and Computation: 17th International Symposium, ISAAC 2006, Kolkata, India, December 18-20, 2006, Proceedings (Lecture Notes in Computer Science)
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Parallel and Distributed Processing and Applications: 4th International Symposium, ISPA 2006, Sorrento, Italy, December 4-6, 2006, Proceedings (Lecture Notes in Computer Science)
-
Approximation Algorithms
ASIN: 3540224947 |
Book Description
This book is the only source that provides comprehensive, current, and correct information on problem solving using modern heuristics. It covers classic methods of optimization, including dynamic programming, the simplex method, and gradient techniques, as well as recent innovations such as simulated annealing, tabu search, and evolutionary computation. Integrated into the discourse is a series of problems and puzzles to challenge the reader. The book is written in a lively, engaging style and is intended for students and practitioners alike. Anyone who reads and understands the material in the book will be armed with the most powerful problem solving tools currently known.
This second edition contains two new chapters, one on coevolutionary systems and one on multicriterial decision-making. Also some new puzzles are added and various subchapters are revised.
Customer Reviews:
Kind of old stuffs.......2007-09-23
The material is alright but it is just kind of old. I can not tell from the original description otherwise, I will
not have bought it.
Optimization Mini-Library.......2007-02-20
This is the best book I have in my optimization library. It is excellent for students and teachers as well. It introduces you to optimization using a simple language, practical examples explained in a very didactical manner. It surveys optimization techniques and categorizes it in a very well-arranged and simplified format. You wouldn't have to read tens of pages with unsightly symbols, messed with subscripts and superscripts to understand a single optimization technique.
It also brings an uplifting introduction to the concept of problem solving. I highly recommend this book to Optimization and Mathematics students and teachers.
Read the book, once you are done, look at the table of contents and give a five minutes lecture on each single title and subtitle, which is what you will be capable of doing at the end.
It's not the technique, it's the logic behind it.......2006-07-24
Most evolutionary computation or math books deal with the techniques of solving problems. This book teachs you how to think of a solution for the problem you face, and not what problems are appropriate for the technique in hand.
The logic is that when you do a craft work, you do pick the appropriate tool from your tools box, but you don't grasp a tool and then find a job to go with it, which is the case when you can only handle this tool.
improve your problem solving ability.......2006-02-20
The authors have updated their successful first edition, though the latter, printed in 99, was scarcely obsolete. A heuristic can be basically a rule of thumb, dressed up in fancier language. What the authors intend is for you to develop an intuition about when to use modern algorithms. Where is almost every case, these are actually implemented on a computer; a reflection of the cheap availability of computing power to most readers.
The book is a good complement to various standard algorithm texts, like those by Sedgewick, Aho and Knuth. You can consider this book as standing a level above those. [Though Knuth's books also do an excellent job of suggesting when to use or modify algorithms. ]
The level of discussion here is not of a strict, heavy mathematical approach. It can be read as informal guidelines, that discuss the gist of such ideas as simulated annealing and evolutionary methods. There is a wide range of example problems, to motivate you in understanding what might be used to solve them.
get this book!, read it!, understand it! :).......2006-01-01
i have not finished reading this book, but it's 'worth it' if only for the first two chapters! :) anyone interested in dynamical systems (control aspects), general problem solving, AI, and human thinking should read and understand this book! :) work the problems! :) think! enjoy! :)
Average customer rating:
- As promised, on time
- first great treatment of generalized linear models
- Very comprehensive, very helpful.
- One of the best books on modelling
|
Generalized Linear Models (MONOGRAPHS ON STATISTICS AND APPLIED PROBABILITY)
P. McCullagh
Manufacturer: Chapman & Hall/CRC
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ASIN: 0412317605 |
Book Description
The success of the first edition of Generalized Linear Models led to the updated Second Edition, which continues to provide a definitive unified, treatment of methods for the analysis of diverse types of data. Today, it remains popular for its clarity, richness of content and direct relevance to agricultural, biological, health, engineering, and other applications. The authors focus on examining the way a response variable depends on a combination of explanatory variables, treatment, and classification variables. They give particular emphasis to the important case where the dependence occurs through some unknown, linear combination of the explanatory variables. The Second Edition includes topics added to the core of the first edition, including conditional and marginal likelihood methods, estimating equations, and models for dispersion effects and components of dispersion. The discussion of other topics-log-linear and related models, log odds-ratio regression models, multinomial response models, inverse linear and related models, quasi-likelihood functions, and model checking-was expanded and incorporates significant revisions. Comprehension of the material requires simply a knowledge of matrix theory and the basic ideas of probability theory, but for the most part, the book is self-contained. Therefore, with its worked examples, plentiful exercises, and topics of direct use to researchers in many disciplines, Generalized Linear Models serves as ideal text, self-study guide, and reference.
Customer Reviews:
As promised, on time.......2006-03-21
I got this book in time and in perfect condition. Prompt delivery!!!
first great treatment of generalized linear models.......2000-08-09
Nelder and Wedderburn wrote the seminal paper on generalized linear models in the 1970s. Since then John Nelder has pioneered the research and software development of the methods. This is the first of several excellent texts on generalized linear models. It illustrates how through the use of a link function many classical statistical models can be unified into one general form of model. This unification is helpful both theoretically and computationally. Various applications are presented in a clear manner.
Very comprehensive, very helpful........2000-04-02
The first edition is already a well-known text and reference, this expanded version is even better. Very comprehensive and very helpful.
One of the best books on modelling.......2000-04-01
This is an important book. It is a mature, deep introduction to generalized linear models.
General linear models extend multiple linear models to include cases in which the distribution of the dependent variable is part of the exponential family and the expected value of the dependent variable is a function of the linear predictor. Besides the normal (Gaussian) distribution, the binomial distribution, the Poisson distribution and the Gamma distribution, are just some of the exponential family members most frequently encountered in the scientific literature. Using appropriate functions to join the dependent variable to the linear predictor many classic models of applied statistics are included in the broad frame of generalized linear models: "logistic regression", log-linear models, Cox's proportional hazards models are just some of them.
Further extensions to the "base" family of generalized linear models, such as those based on the use of quasi-likelihood functions, and models in which both the expected value and the dispersion are function of a linear predictor, are well presented in the book.
Examples, and exercises, introduce many non-banal, useful, designs.
There are some minor drawbacks. Some more advanced topics might have been introduced more smoothly (i.e. conditional likelihood). Some other topics are better understood when you are already familiar with the specific object of study (i.e. Cox's proportional hazards models as a generalized linear model). The book does not provide software examples, nor is it related with any specific statistical package. However, the maturity of the reader to whom the book is addressed should be so high that translating the majority of the examples presented in the book in the "language" of a familiar statistical package should not be a problem.
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Encyclopedia of Optimization
Christodoulos A., Ed. Floudas
Manufacturer: Kluwer Academic Publishers
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ASIN: 0792369327 |
Book Description
Optimization problems are widespread in the mathematical modeling of real world systems and their applications arise in all branches of science, applied science and engineering. The goal of the
Encyclopedia
of Optimization is to introduce the reader to a complete set of topics in order to show the spectrum of recent research activities and the richness of ideas in the development of theories, algorithms and the applications of optimization. It is directed to a diverse audience of students, scientists, engineers, decision makers and problem solvers in academia, business, industry, and government.
Average customer rating:
- Flawed but well-explained
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Extending Linear Model With R: Generalized Linear, Mixed Effects and Nonparametric Regression Models (Texts in Statistical Science Series (Chapman and Hall))
Julian J. Faraway
Manufacturer: Chapman and Hall
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Similar Items:
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Linear Models with R (Texts in Statistical Science Series (Chapman and Hall))
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R Graphics (Chapman & Hall/ CRC Computer Science & Data Analysis)
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An Intoduction to Generalized Additive Models with R (Texts in Statistical Science Series (Chapman and Hall))
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A Handbook of Statistical Analyses Using R
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Data Analysis and Graphics Using R: An Example-based Approach (Cambridge Series in Statistical and Probabilistic Mathematics)
ASIN: 158488424X |
Book Description
Linear models are central to the practice of statistics and form the foundation of a vast range of statistical methodologies. Julian J. Faraway's critically acclaimed Linear Models with R examined regression and analysis of variance, demonstrated the different methods available, and showed in which situations each one applies. Following in those footsteps, Extending the Linear Model with R surveys the techniques that grow from the regression model, presenting three extensions to that framework: generalized linear models (GLMs), mixed effect models, and nonparametric regression models. The author's treatment is thoroughly modern and covers topics that include GLM diagnostics, generalized linear mixed models, trees, and even the use of neural networks in statistics. To demonstrate the interplay of theory and practice, throughout the book the author weaves the use of the R software environment to analyze the data of real examples, providing all of the R commands necessary to reproduce the analyses. A supporting Web site at www.stat.lsa.umich.edu/~faraway/ELM holds all of the data described in the book. Statisticians need to be familiar with a broad range of ideas and techniques. This book provides a well-stocked toolbox of methodologies, and with its unique presentation of these very modern statistical techniques, holds the potential to break new ground in the way graduate-level courses in this area are taught.
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Flawed but well-explained.......2007-07-16
"Extending the Linear Model with R" is a "sequel" of sorts to the impressive "Linear Models with R" also written by Faraway. It assumes a basic knowledge of R (you don't have to be an expert) and a decent understanding of linear models. If you don't have that background, then I would start with the before-mentioned "Linear Models with R". If you read and understood that book, then you should be more than prepared for this one.
This book covers extensions of the linear model including Generalized Linear Models (GLM's), Mixed and Random Effects Models, Nonparametric Regression Models, Additive Models (including GAM's - Generalized Additive Models), and it contains a brief introduction to Regression Trees and Neural Networks. The biggest focus is on Generalized Linear Models. The book is fairly thorough, though not exactly comprehensive, in covering the topic of GLM's and specific commonly used GLM's. The material is very well-explained and easy to follow and they do a good job at integrating code, examples, and graphs in a way that facilitates understanding of both statistical concepts regarding GLM's and also the implementation of these concepts in R. The code is especially useful and it covers most things in R that you will need for this topic, at least those available from CRAN. The book is not very rigorous regarding theory, but that only makes the book easier to read and more practical. However, I do have one complaint regarding this section. The author spends several chapters discussing various commonly used GLM's and THEN finally gets around to defining what a GLM is and covering the basic theory. This seems backwards to me and for this reason I wouldn't read the chapters in order. Also, due to the late coverage of some of the basic theories, we don't get to see the implementation and analysis of certain sub-topics (e.g. leverage and influence) in the early examples.
Mixed and Random Effects models are second in terms of attention received. The organization is better and the explanations and code integration continue to be handled well. Nonparametric Regression and Additive Models only receive one chapter apiece, but both chapters are extremely informative and they are well-explained like the rest of the book. I was especially happy to see the coverage of GAM's (it's very short but useful) since it is a moderately recent topic (1990) and many similar books only make a brief mention of them (hey, GAM's exist) if they are mentioned at all. The chapter on Regression Trees is short, but again they make sure to cover many of the important sub-topics with clarity. The Neural Networks chapter is skimpy and you won't learn much, but it was an unexpected bonus so I can't take off points for that.
Do note that this book takes a regression approach throughout, so look elsewhere for an ANOVA perspective. The book is short with plenty of room left to talk about other topics. Thus, I would have liked to see a second part devoted to an ANOVA approach since I'm the kind of person who hates having to thumb through countless books, but they are open about the book's scope so I can't really complain.
Okay, one more complaint. I would have greatly liked to see an appendix of the R functions used throughout the book with short descriptions and references to where in the book you can find the function being discussed. R Help isn't bad, so it's not a tragic omission, but it still would have been nice.
In summary, this book is extremely useful if you plan on using extensions of linear models with R. Flaws aside, it receives my recommendation.
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