Book Description

This popular text, publishing Spring 1999 in its Second Edition, introduces the mathematics underlying the pricing of derivatives. The increase of interest in dynamic pricing models stems from their applicability to practical situations: with the freeing of exchange, interest rates, and capital controls, the market for derivative products has matured and pricing models have become more accurate. Professor Neftci's book answers the need for a resource targeting professionals, Ph.D. students, and advanced MBA students who are specifically interested in these financial products. The Second Edition is designed to make the book the main text in first year masters and Ph.D. programs for certain courses, and will continue to be an important manual for market professionals. ... Read more

Reviews (48)

The best intro book ever!
Students of derivative pricing techniques are often in a dilemma: Coming from their MBA or undergrad course, they have just build a "brealy-myers" type of intuition on options. Moving towards Hull then allows a deeper understanding. But any serious (eg PhD, Wall Street Analyst) student of derivatives needs to undertstand the math behind modern derivatives pricing. Essentially, this research divides into two streams: Solving Partial differential equations and developing equivalent Martingales. Without a rigorous pre-education (Maths, Physics), most students fail to understand (let alone learn to use) these methods. Nefci is the only book that does not assume lots of prior knowledge, as compared to Merton (1992) or Duffie (who is so bold to write "for mathematical preparation little beyong undergraduate analysis...is assumed" -ask PhD Students how easy this book reads! The answer is its tough!!). In Short, Neftci's book is a true blessing for all "normal" people. Can't wait to get the second edition!

Good explanations, with serious hand-waving
I used this book to teach a Financial Mathematics course, and found its explanations to be generally clear and good. However, part of the reason the text seems so clear is that it doesn't explain much of what's really going on. It covers the right material, but not really in such a way that the reader can then go on to apply the knowledge gained.This is evidenced by the complete (and almost unforgiveable) lack of exercises in the book. It is very easy to feel you understand this sort of material, only to be completely lost when you actually have to solve a problem. Neftci will not help in this regard. I understand that it is difficult to create good exercises, but their absence almost makes me wonder if Neftci realized he was not explaining things in enough detail to let the student actually work with the knowledge. Exercises are the only way to really learn this subject.A basic problem with all these texts is that, try as they might, they cannot impart true understanding unless the student can grasp real analysis at, say, an undergraduate level typically reached by students at a good engineering school. This text tries to avoid the problem by failing to mention any of the analysis...that's not likely to work.

Good Book
I've read Hull, Wilmott and Baxter books but definitely like this book better - particularly for entry (but not easy) level derivative math. Can't say much since English is not my first language. But if you want to learn about Derivative Math and don't have strong background in Math (I'm a Porfolio Manager and have pretty good background in Calculus, Differential Equation, Econometrics) this book is certainly worth considering. I give 4 stars due to the lack of practice problems.

Good book for the right audience
It is amazing that people are not willing to take it what it is, an 'introduction' to mathematics of financial derivatives. The 'reader from New York' of 'notation challenged' seemed to have wanted a rigourous treatment of SDE, yet is sorely disappointed not to find it in this book. IMO it gives an extremely clear exposition of the various tools of SDE and having read it has allowed me to progress to books in which mathematical rigor is stressed over intuition. So in a nutshell this book achieved its stated goal of offering an intuitive and heuristic explanation of mathematics of derivatives to the novices taking their first steps in the financial engineering land.

Notation Challenged
As Yogi might have said: "If you understand this book, you don't need this book.".

If you have a good grip on the industry conventions relative to notation, and have seen the material before, you might understand this book. If not, you won't.

Notation is: 1) frequently wrong; 2) used inconsistently; 3) used out of context (i.e., without foundation); 4) glued in as a concluding argument in a logically non-convex way.

The absence, misuse, abuse of time subscripts makes some of the arguments incomprehensible.

Some arguments pursue a change of reasoning in probability space, then make a jump to an S.D.E. with industry standard notation, but so far out of scope, that the connections are not clear. As one example, if you: 1) know the underlying S.D.E., and if you ; 2) understand the connection between risk-neutral probability and risk-free measure , and if you; 3) understand why a state variable is allowed to commute through an expectations operator because it is no longer stochastic (though why that might be so is not explained), then you will have a chance of understanding the author's argument connecting the transformation of synthetic probabilities to a standard S.D.E.

Some words are capitalized to emphasize, rather than being defined. Sort of like going to a foreign country and shouting more loudly as a communication strategy ... Read more

Book Description

Maintaining a contemporary perspective, this strongly algebraic-oriented text provides a concrete and readable text for the traditional course in elementary differential equations that science, engineering, and mathematics readers take following calculus.Matters of definition, classification, and logical structure deserve (and receive here) careful attention for the first time in the mathematical experience of many of the readers. While it is neither feasible nor desirable to include proofs of the fundamental existence and uniqueness theorems along the way in an elementary course, readers need to see precise and clear-cut statements of these theorems, and understand their role in the subject. Appropriate existence and uniqueness proofs in the Appendix are included, and referred to where appropriate in the main body of the text. Applications are highlighted throughout the text. These include: What explains the commonly observed lag time between indoor and outdoor daily temperature oscillations?; What makes the difference between doomsday and extinction in alligator populations?; How do a unicycle and a two-axle car react differently to road bumps?; Why are flagpoles hollow instead of solid?; Why might an earthquake demolish one building and leave standing the one next door?; How can you predict the time of next perihelion passage of a newly observed comet?; Why and when does non-linearity lead to chaos in biological and mechanical systems?; What explains the difference in the sounds of a guitar, a xylophone, and a drum? Includes almost 300 computer-generated graphics throughout the text.This text, with enough material for 2 terms, provides a concrete and readable text for the traditional course in elementary differential equations that science, engineering, and mathematics readers take following calculus. ... Read more

Reviews (3)

Very good
Fast and its just brand new, he never opened the books

As good as it gets
Differential equations are difficult to teach (though not conceptually difficult), and that fact becomes obvious when you look at most DE books. However, there is the occasional text that teaches the subject in a most comprehensible way. I have used other DE books, among them S. Goode's "Introduction to Differential Equations and Linear Algebra", and let me tell you -- it [is bad]. Plain and simple. It was the only math class I've ever taken which left me with what felt like an incomplete understanding of the subject. And then I used this book for a course at MIT and I realized why I hadn't learned anything previously. The old book [was bad]. And this was pretty good. The only reason I give it four stars instead of five is because it doesn't have any particular line of development. But that's not that important, and this book is a great way to learn differential equations.

Good book but a strong background on calculus required.
This book will tell you everything you need to learn on differential equations. It covers thoroughly the methods for solving first and second order differential equations. The book also extends into Fourier transforms. I used this book at MIT for the differential equations class and found it very useful. Within its contents, matlab exercises are present and some simple projects which lets the student apply its knowledge. The only problem with the book is that it can be hard to read at certain points. Also the author assumes a strong background in calculus. ... Read more

Book Description

An intuitive, yet precise introduction to probability theory, stochastic processes, and probabilistic models used in science, engineering, economics, and related fields.

The book covers the fundamentals of probability theory (probabilistic models, discrete and continuous random variables, multiple random variables, and limit theorems), which are typically part of a first course on the subject. It also contains, a number of more advanced topics, including transforms, sums of random variables, least squares estimation, the bivariate normal distribution, and a fairly detailed introduction to Bernoulli, Poisson, and Markov processes.

The book strikes a balance between simplicity in exposition and sophistication in analytical reasoning. Some of the more mathematically rigorous analysis is explained intuitively in the text, and is developed in detail (at the level of advanced calculus) in the numerous solved theoretical problems.

This text is being currently used in introductory probability classes at several universities, including M.I.T., Berkeley, and Stanford. ... Read more

Reviews (2)

great book!
Written by two prolific MIT professors, "Introduction to Probability" presents a clean and insightful introduction to probability and stochastic processes. The book is intended for advanced undergraduate and/or beginning graduate students. While many introductory probability texts are dominated by superficial case studies (which in my opinion convey a false sense of confidence about the subject), "Introduction to Probability" promotes deep understanding through clear mathematical writing and thought-provoking examples.

Testimonial: I recently adopted "Introduction to Probability" as the text for a first-year, masters of engineering course on stochastic systems, and it was a great experience. In working with the book, I found that the authors' thoughtful approach really helps to solidify the students' understanding of basic concepts. For example, the text's approach to conditional probability, particularly with its emphasis on sample-space, is so clear that several students (even the TA) came to me afterward saying that, prior to reading the book, they never had a clear understanding of what the formulas actually mean. From an instructor's perspective, "Introduction to Probability" is easy to use. It is accessible to students with diverse backgrounds, and it is also well-balanced, with lots of intuitive/motivating discussion in the main body of each chapter and advanced concepts in extended end-of-the chapter problems. The authors support the text by making available a large amount of supplementary material on the web, including supplementary exercises (suitable for homework or exams) and lecture notes from their introductory probability course at MIT. I highly recommend "Introduction to Probability" to anyone preparing to teach an introductory course on stochastic systems, probability, and stochastic processes.

The odds are you'll love this book
Probabilities are a powerful way of understanding the world and doing science. Trouble is, understanding probabilities is not easy: it takes math, insight, and a fresh way of thinking. Worse, the stuff is so useful in so many contexts that its expositions are often obscured by the intended applications.

I recently found myself looking at several probability books to give a recommendation to a friend. This book (by two well-known MIT professors of Electrical Engineering) is a wonderful treatment in terms of its style (simple informal explanations, motivating discussions, frequent notes of a historical/philosophical nature); its selection of topics (the basics, mainly, usually from the most useful perspective); its rigor and accuracy; its reasonable brevity; its rather conventional point of view (contrast it, for example, with the very interesting recent book by E. Jaynes); and its humor. ... Read more

Book Description

The last decade has brought dramatic changes in the way that researchers analyze economic and financial time series. This book synthesizes these recent advances and makes them accessible to first-year graduate students. James Hamilton provides the first adequate text-book treatments of important innovations such as vector autoregressions, generalized method of moments, the economic and statistical consequences of unit roots, time-varying variances, and nonlinear time series models. In addition, he presents basic tools for analyzing dynamic systems (including linear representations, autocovariance generating functions, spectral analysis, and the Kalman filter) in a way that integrates economic theory with the practical difficulties of analyzing and interpreting real-world data. Time Series Analysis fills an important need for a textbook that integrates economic theory, econometrics, and new results.

The book is intended to provide students and researchers with a self-contained survey of time series analysis. It starts from first principles and should be readily accessible to any beginning graduate student, while it is also intended to serve as a reference book for researchers. ... Read more

Reviews (18)

An excellent bridge to advanced econometrics
As an economist,before taking PhD lectures, I used to think that this book was too complicated. It is not for undergraduate students. Once you acquire some level in mathematics, this book becomes the best reference for time series econometricians. It covers a wide array of themes, the text is clear and understandable, even if, from time to time, you get lost in the mathematical explanations (but it's not the usual). I particularly liked the non-stationary chapters. The spectral analysis is a little bit confusing and there is no non-parametric section. I think this is one of the best books in the field. Mathematicians will find it extremely clear and graduate economists understandable. "Time series Analysis" it's an unavoidable book for those seeking to understand specialised papers.

excellent general textbook on time-series econometrics
This is an excellent introduction to time-series econometrics. It covers huge amount of material without going deep-deep into details. It assumes knowledge of matrix algebra, probability and statistical inference at the level that is expected from graduate students in economics program. The book discusses stationary and non-stationary time series, univariate and multivariate models, deterministic trends and many other topics. It has an excelent and intuitive introduction to spectral analysis. The book is easy to read and is oriented toward students, so I would say, that in general, it's a very friendly textbook and it's a great source for references. But for those who still find it difficult to read or for applied researches looking for quick recipes I would recommend to combine reading of Hamilton's book with Enders's "Applied Econometric Time Series"

this book rules
I bought this book when i studied econometrics in grad school. now i work at an investment bank, and i use the book practically every day. the derivations (which rely solely on calculus and linear algebra) are always clear, and most of the subjects are covered thoroughly but concisely. using this book, for example, i learned gmm in one day and implemented it on the next day. moreover, most of the chapters are self-contained (if you already know a bit about regression analysis), so you won't have to read a bunch of preliminary stuff before you get to what you need to learn.

btw, the author seems like a nice guy, too. one time, i had a question about his treatment of the kalman filter, and he actually responded to my email.

Quite some beach read
I got this book at the beginning of the summer and have been reading it everyday by the pool. This is not to say that you can read it mindlessly - you definitely can not - it is simply so interesting that every time I try to decide what to bring to the pool I would magically turn down Cosmo and Vogue and drag Hamilton along instead. As a rising junior in econonomics and mathematics at Duke, I find this book challenging yet doable. I have previously had an undergraduate course in econometrics and this book answers a lot of the questions I was casually wondering about when I took the class. One more thing I love about this book is that it is vey self-contained. I have a solid background in matrix algebra but not nearly enough in probability (only one undergrad course); I do not so far find it a problem at all. I recommend this book to everyone who liked his/her first econometrics course, even if you are an undergrad.

Not mathematically rigorous enough
What's funny is that everyone here is saying that it's too mathematical, and not friendly with people without a mathematical background. In my opinion, people without a mathematical background should better stay away from time series.

I actually think that Hamilton provides mathematical derivations where these are not too complicated and avoids them where it gets pretty tough. Case in point is the chapter on multivariate time series analysis, where estimation and forecasting of VMA and VARMA models is completely ignored. Actually, VARMA models are not even mentioned in the book. Hamilton only tackles VAR models, which are pretty easy to estimate due to their regression properties.

Otherwise, I think Hamilton tackles a wide range of topics. However, as an alternative I prefer Brockwell&Davis (the yellow book). It does not cover such a wide range of topics but in the end, it covers what it covers extremely rigorously, without leaving any questions unanswered and without shying away from tough mathematics. ... Read more

Book Description

This classic book provides a rigorous introduction to basic probability theory and statistical inference that is motivated by interesting, relevant applications. It assumes readers have a background in calculus, and offers a unique balance of theory and methodology.Chapter topics cover an introduction to statistics and data analysis, probability, random variables and probability distributions, mathematical expectation, some discrete probability distributions, some continuous probability distributions, functions of random variables, fundamental sampling distributions and data descriptions, one- and two-sample estimation problems, one- and two-sample tests of hypotheses, simple linear regression and correlation, multiple linear regression and certain nonlinear regression models, one factor experiments: general, factorial experiments (two or more factors), 2k factorial experiments and fractions, nonparametric statistics, and statistical quality control.For individuals trying to apply statistical concepts to real-life, and analyze and interpret data. ... Read more

Reviews (14)

Good book for those who know what they're doing
I really enjoy using this as a reference book when I need to look something up about inference. Everything in the book is highlighted well and gives clear and concise answer. If you're a straight A student in Math, there should be nothing confusing about this text.

Worked a lot better for me than the others
I thought this was a pretty good text for an introduction to statistics with a modicum of calculus (I used the 5th edition). I am a biologist and had taken statistics without calculus (VERY cookbook approach the first time through) so maybe knowing where the math was eventually taking me was the difference. I am very (brutally) applied in my interest in statistics (use it daily to model fish populations, estimate critter abundance, etc.) so I could see where I would not agree with the mathematician who said it killed the beauty of the subject (although I am not gifted enough in math to see the beauty of statistics; I honestly would like to be). Also I did cover the text in two classes (1st up through calculating a confidence interval, 2nd on the general linear model) so that may have made a difference as well - if the others were forced to march through all of the material in the book in 18 weeks. I notice that a lot of the reviewers are computer scientists (ones in my class hated the subject matter - I was not sure why it was a required course for them anyway) or mathematicians. Anyone else out there from the natural or physical sciences (e.g., biology, chemistry, geology) that had experience with this book? Finally - I don't recall the plethora of errata that the others refer to - although I had previously heard this complaint about earlier editions of this book.

Buy Hayter instead
Not worth the paper it's printed on or the ink used to print...poor trees! =(

This is not a great book...
I am a math major currently taking probability in my last year of college in NYC. I don't like this book!! The examples in the chapters make sense... but many of the exercises at the end of each section are not fair (hehe)... you need to make a lot of conclusions and jumps about what you read in the chapter that I am not able to make on my own. If the chapters were more in-depth or more detailed, then things would be different. But I feel like the chapters give examples that are much too simple compared to the exercises you are asked to do on your own. For instance, in the second chapter there is a section on permutations and combinations and all that good stuff... it was all good... but then when I got to the exercises at the end of the section, I found that there was no way I could have answered many of them without any previous knowledge on the subject (of which I had none.) There is no way that I could ever use this book to teach myself... you really need a good teacher or someone who understands the topic to help you (a lot!) if this is your first try at probability. I am a straight A student in math, so I feel that my beliefs on this book are pretty credible... and it seems that I am in good company!!!

This is a difficult book from which to teach yourself
I'm a second year computer science student taking a course on probability, and this is the book we are using. Why, I don't know, because it's not a very good book. I'm not a note-taker, and have a difficult time paying attention in math classes, so I usually teach myself from the book. With a 3.6 GPA, I'd say it usually works. Not so with this book.

This book lacks sufficient examples and the definitions and explanations of theorems are confusing. To its credit, it has odd answers in the back, but that's standard for math books. However, it lacks any answers to the review exercises at the end of each chapter, making the review exercises nearly worthless. ... Read more

Book Description

This book will appeal to both graduate students and practitioners. Students will value the Excel spreadsheets allowing them to develop their knowledge of modelling in finance, using a step-by-step approach accompanied by explanations using elementary mathematical statistics and probability. Practitioners will value the VBA functions as a source of up-to-date and efficient programs that can be easily used from Excel.

Standard material covered includes:

• portfolio theory and efficient frontiers
• the Capital Asset Pricing Model, beta and variance-covariance matrices
• performance measurement
• the Black-Scholes option pricing formula
• binomial trees for options on equities and bonds
• Monte Carlo simulation
• bond yield-to-maturity, duration and convexity
• term structure models from Vasicek and Cox, Ingersoll and Ross

Reviews (15)

Now I get it!
I've just finished studying this book. It's just absolutely delightful. Aftger having taken brain numbing statistics courses in graduate school, this book is like having an expert friend to talk to about the real basis for things. The authors are very thorough in developing baisc statisical theory through examples and practical problems, not to mention interesting and relevant historical background. It's basically a book on learning how to think statistacally, correctly! Common pitfalls togehter with discussions of famous and not so famous goofs and misapplicatinos of statistical methods are throughout the book used (not to poke fun, although it is fun) to develop a second nature in basic concepts. While the book is thick, the reading is easygoing and friendly. It won't take very long for most people to get through it. Concepts are developed progressively on firmly developed and well explained basic ideas. It's as much, if not more, a book on critical thinking as it is on the techniques of elementary statistics. -- Jack Penkethman

Excellent text
I taught an introductory statistics course with this book two years ago. I have to say that *I* learned a great deal preparing for class as I read it--there is a lot of insight and intuition here that you won't generally find anywhere else. Teaching out of it is tough, though, because you don't have the math to hide behind. For those of us used to math, formulas can be a comforting thing. For most students, they're usually just intimidating and the object of many blank stares. IMO, for students who will take only one class in statistics, learning out of this book would be very helpful in a way many other books would not be. For students who will take more than one, gaining a strong conceptual foundation will be helpful as well.

My guess is that the students complaining about this book don't know how good they've got it. You could be stuck with a book that focuses on how to do statistics with Excel or the like, in which case you'll basically learn nothing of subsequent value. :)

Statistics by David Freedman
Useful book. Lots of helpful examples. Don't really need any extra stats resources (like a workbook) if you buy this.

Great Intro Book for Non-Math/Stat Majors
I expected this to be dry and mechanical like lots of other math texts - too-technical proofs, homework questions irrelevant to the material, insufficient explanations for why things are the way they are. This book really surprised me because it wasn't "mathy" at all. It doesn't just throw proofs at you expecting you to wade thru page upon page of math notation until you understand - it gives you the intuitive side of important concepts, which means you only need common sense, not an intensive mathematical background to get everything. The examples they picked simplify rather than confuse the concepts. Each easily and naturally leads to the next. If there's anything not thoroughly elaborated, they were sure to cover it in the homework questions, by gently plodding the reader along towards the answer step by step instead of smacking them in the face with impossible problems. Homework questions supplement the material perfectly and basically leave you with a full and well-rounded impression of what the concepts mean as well as when and why to use them, not just how to plug numbers into some formula.
If anything, I'd say this book errs on the side of caution in that in some sections it could pick up the pace a little. But then again, you could always just skip the easier parts.

Nothing Better -Nothing even comes close
This is not merely the best introductory statistics text, it is in a sense the only one. So far as I know all the others (which were inferior before) have gone "computer." Students are taught how to solve problems on the computer which means they never learn statistics at all.

I have taught introductory statistics for many years and my contempt for the other texts increases year by year. Apparently publishers demand that texts be "computerized" and the authors have been too spineless to resist.

I would like to add that I suspect that most instructors who have used the other texts exclusively would have a tough time with some questions which students of this book would answer with ease. ... Read more

Book Description

A modern and highly innovative guide to industrial experimental design

The past two decades have seen major progress in the use of statistically designed experiments for product and process improvement. In this new work, Jeff Wu and Michael Hamada, two highly recognized researchers in the field, introduce some of the newest discoveries in the design and analysis of experiments as well as their applications to system optimization, robustness, and treatment comparisons in the diverse fields of engineering, technology, agriculture, biology, and medicine.

Drawing on examples from their impressive roster of industrial clients (including GM, Ford, AT&T, Lucent Technologies, and Chrysler), Wu and Hamada modernize accepted methodologies, while presenting many cutting-edge topics for the first time in a single, easily accessible source. These include robust parameter design, reliability improvement, analysis of nonnormal data, analysis of experiments with complex aliasing, multilevel designs, minimum aberration designs, and orthogonal arrays. Other features include:

• Coverage of parameter design for system improvement first introduced by Taguchi in the mid-1980s
• An innovative approach to the treatment of design tables
• A discussion of new computing techniques, including graphical methods, generalized linear models, and Bayesian computing via Gibbs samplers
• Each chapter motivated by a real experiment
• Extensive case studies, including goals, data, and experimental plans
• More than 80 data sets as well as hundreds of charts, tables, and figures
... Read more

Reviews (4)

A Superb Graduate Textbook
There are many ways one can judge a textbook. For graduate textbooks, the most important aspect one should look at is if they are worth keeping. Taking a graduate course in statistics generally means that one has chosen a career in statistics or a career with a significant statistical component in it. So the value of a textbook after graduate studies is an important consideration any instructor should give. It is pitiful that some of the textbooks from my own graduate studies are not worth a second reading, either because they lack modern topics or because they are mostly devoted to mechanical derivations.

Wu and Hamada (2000) is a superb textbook in this regard. The book is loaded with a number of most important modern topics in design of experiments, including robust parameter design, minimum aberration, designs with complex aliasing, and generalized linear models (p. xvii). These modern topics only receive some courteous treatment, if any at all, in most of design textbooks. The importance of these topics cannot be over-stated. It is impossible for an instructor to provide a detailed coverage of all the important topics in any design course. Practical problems often require the use of certain methods, which may or may not be touched in a design course. Therefore, we will often have to go back to our graduate textbooks to do some further reading. The comprehensive design tables in Wu and Hamada (2000)
also make this further learning process easier. For those who are doing research in the area after their graduate studies, Wu and Hamada (2000) is a necessity. Accessing design literature through journals is much more inconvenient and time-consuming. Wu and Hamada (2000) is also a suitable textbook for a design course for undergraduates majoring in statistics, or other areas of mathematical sciences.

If I can only own one design book, this is the one.

Not in touch with Grad Students...
I think this book has great potential. Unfortunately, it suffers from a few of the most fairly common Graduate Level text book problems.

Use of the 'et cetera' function, or a failure to work out examples. I'm not sure if I'm in a minority with this opinion, but I believe, after many years as a graduate student that examples should be worked on in their entirety. Unfortunately, this in not the case with this textbook. There are numerous places in this text where the authors reference, with great generality, pervious half-worked examples or formulas. Not only does this make the text sometimes difficult to follow, it also reduces the usefulness of the book as a self teaching tool.

The text also fails to include even some of the solutions to its exercises. I'm not sure why many authors fail to include even some of the solutions to their chapter exercises. In my opinion, I believe that this is a serious weakness in text. Most professors who teach graduate level courses create their own problem sets. By failing to include even partial solution sets, the authors minimizes or completely destroys any benefit of including exercises in the text (especially if you are not reading this text as part of a course). There is no benefit of working out exercises if you can not correct or even identify your mistakes.

If I had to have just one "Design of Experiments" book, I would not choose this one. Although there are many great things about this book, it is notoriously light on Split-Plot experiments. In fact, Split-plot experiments (which are very common) only receive a cursory mention. If you are looking for Books on Designs of experiments, I suggest you look at "Design and Analysis of Experiments" by Douglas Montgomery, or maybe even the older "Statistical Design and Analysis of Experiments" by Mason, Gunst, and Hess.

authoritative and thorough treatment
Jeff Wu got his Ph.D. in statistics from UC Berkeley. He started his career at the University of Wisconsin in Madison where he was influenced by George Box and was exposed to many important practical design problems. Jeff quickly established himself as a top notch theoretical statistician publishing some landmark papers in the Annals of Statistics. As his career developed at Wisconsin and later in Canada and at Michigan he made fundamental contributions to survey sampling and experimental design. This book is basically a sequel to the classic book by Box, Hunter and Hunter. It includes all aspects of experimental design and is very thorough in covering all the classical topics and the new area of robust design. It includes many recent advances by the authors (Wu and Hamada) in the 1990 and even the late 1990s (papers from 1997 and 1998 are referenced).

The book is intended for scientists and engineers as well as statisticians. The authors deliberately introduce the concepts gently, starting with a real problem and constructing and analyzing a design type considered in the chapter. This is done consistently from chapters 3-13.

They start with the simplest ideas and designs and build up. Chapter 1 deals with single factor experiments and Chapter 2 with experiments with more than one factor, starting with two. Section 1.1 provides an historical perspective which I find valuable. It leads to a classification of design problems that are distinct and they show how they arose in very different contexts. They do a good job of setting the stage for the remaining chapters. The categories are (1)Treatment Comparisons (the traditional agricultural experiment), (2) Variable Screening, (3) Response Surface Exploration, (4) System Optimization and (5) System Robustness. Although the theory of optimal designs is not covered in detail, the role of optimal designs is mentioned as is the early work of Kiefer (section 4.4.2)and reference to the recent book by Pukelsheim is given.

In Chapter 4 on fractional factorial experiments at two levels, concepts of resolution and aberration are clearly explained. I think it helps that the authors make these concepts concrete through the illustrative examples. I have often looked at standard design texts and found myself confused about the distinction between resolution III, IV and V designs.

There are several features that set this book apart from other books on design of experiments. Some attention is given to the one-factor-at-a-time approach. Most books ignore this commonly used approach and its many drawbacks. The authors explain its four main disadvantages and illustrate the problem with a design example. In my experience in industry, many engineers are not trained well in statistics and although it may seem clear to statisticians that one-at-a-time approaches overlook interactions or dependencies between variables, the engineers often do not. They see this approach as a way to simplify their search for the best operating conditions. I published an article in the mathematical modeling literature that also was intended to demonstrate the value of statistical design methods over the one-at-a-time approach. Latin square and Graeco-Latin Squares are covered as well as the more common factorial and fractional factorial designs. They also cover randomized blocks and balanced incomplete blocks. The concept of pairing (blocking) is well illustrated with a particular analysis of variance done both with and without pairing. Underlying assumptions are brought out and never hidden. The principles that are the basis for selection of fractional factorial designs are made explcit. Practical nonregular designs including the popular Plackett-Burman designs are well covered. Chapter 10 provides the basis and motivation for robust parameter designs. It also includes a discussion of the signal-to-noise ratio approach of Taguchi and describes some of its weaknesses. Chapter 11 looks at various performance measures for robust parameter design and compares several designs with respect to these parameters.

In the early chapters, the analysis of variance is presented clearly with all the required assumptions. Multiple comparison methods are discussed. Good references, both recent and old, are provided on each topic. My only disappointment was the omission of the recent resampling approaches to p-value adjustment due primarily to Westfall and Young.

Another interesting and unique aspect of the book is the presentation of Bayesian variable selection strategies. This introduces much of the interesting new work in Bayesian methods using the Markov Chain Monte Carlo methods.

Chapters 12 and 13 cover topics you will not find in other experimental design books. Chapter 12 deals with experiments to improve reliability and 13 with nonnormal data. Use of generalized linear models and transformation of variables is well covered in the book.

This book is a worthy sequel to Box, Hunter and Hunter. It is a great introductory book for experimental design courses and a great reference source for scientists, engineers and statisticians. It is already gaining in popularity.

A classic book on experimental design
This book is on the par with Box, Hunter, Hunter's famous book "Statistics for Experiments". If you already have the latter one, you should have both.

This book devotes more than half of its chapters to cover the rapid new developement in past two decades that was not ready to be coverd by BHH back in 1978.

First four chapters cover the same classic designs in BHH. Chapter 5 discusses in detail on three level factorial designs which is very useful but was not covered by BHH. Chapter 6 lists useful mixed level designs. Chapters 7 and 8 explain design and analysis of Platt-Burman and other irregular designs. Chapter 9 is on response surface design. Chapters 10 and 11 are devoted to Robust designs, better known as Taguchi method. Chapter 12 is specific on reliability study using experimental design. Chapter 13 wraps up the book with a nice discussion on how to deal with non-normal responses in an experiment.

The book is full of data from real experiments. There are on average 7-8 in each chapter. For practioners, there are hundreds of designs tabled after each chapter.

The authors explain the strategy of designing experiments and doing data analysis very clearly through examples. There are also pletty of exercise problems after each chapter. It could be used as a textbook for two semester experimental design courses.

The authors did not try to cover everything but rather stay focused. For example, optimal designs are left out from the book. Most of the data analysis method in the book requires to be done using statistics softwares but you couldn't find a single computer command in the book. Maybe in the future, we will have SAS books, S+ books, and Minitab books to go along with this book. At this moment, the software developers have to catch on. ... Read more

Book Description

In the numbers explosion all around us in our modern-day dealings, the buzzword is data, as in, “Do you have any data to support your claim?” “The data supported the original hypothesis that . . .” and “The data bear this out. . . .” But the field of statistics is not just about data. Statistics is the entire process involved in gathering evidence to answer questions about the world, in cases where that evidence happens to be numerical data.

Statistics For Dummies is for everyone who wants to sort through and evaluate the incredible amount of statistical information that comes to them on a daily basis. (You know the stuff: charts, graphs, tables, as well as headlines that talk about the results of the latest poll, survey, experiment, or other scientific study.) This book arms you with the ability to decipher and make important decisions about statistical results, being ever aware of the ways in which people can mislead you with statistics. Get the inside scoop on number-crunching nuances, plus insight into how you can

• Determine the odds
• Calculate a standard score
• Find the margin of error
• Recognize the impact of polls
• Establish criteria for a good survey
• Make informed decisions about experiments

This down-to-earth reference is chock-full of real examples from real sources that are relevant to your everyday life: from the latest medical breakthroughs, crime studies, and population trends to surveys on Internet dating, cell phone use, and the worst cars of the millennium. Statistics For Dummies departs from traditional statistics texts, references, supplement books, and study guides in the following ways:

• Practical and intuitive explanations of statistical concepts, ideas, techniques, formulas, and calculations.
• Clear and concise step-by-step procedures that intuitively explain how to work through statistics problems.
• Upfront and honest answers to your questions like, “What does this really mean?” and “When and how I will ever use this?”

Chances are, Statistics For Dummies will be your No. 1 resource for discovering how numerical data figures into your corner of the universe. ... Read more

Reviews (4)

It's just okay
A reasonable overview of the subject. Go down to your local community college, buy a $15 Texas Instrument ti-30x-II calculator, go on line and pull down a ti-30x-II pdf file for free and REALLY learn what you're looking at. Plus you get college credit for passing. "Stats for dummies" reminds me of a MBA in 12 hours course I once took. Oh yeah, you might have to invest some time using excel spreadsheets if the stats course is business related.

What Statistics for Dummies is About
Statistics for Dummies would be useful in a statistics class, but it is also easily accessible to the general public. The book contains a wide range of examples for any topic in the introductory statistics syllabus, as well as step-by-step explanations of all calculations needed. The book is also very useful for getting clear cut, intuitive explanations of statistical ideas. The index is a quick way to find whatever you are looking for.

Statistics for Dummies
This author is great, and the book has helped me tremendously! There are tons of problems in the book, and the author walks us step by step through the calculations. If you want to understand statistics, I would recommend this book.

More of a professional level
Deborah Rumsey has obviously written this book for the professional because there are no examples of any kind to be found in the book. If your looking for a book that shows examples of how to work the formulas for statistics then I highly recommend Shaum's Statistics third edition. Statistics for Dummies is a real disappointment to the dummies series of books. ... Read more

Book Description

* Emphasizes the latest trends in the field.
* Includes a new chapter on evolving methods.
* Provides updated or revised material in most of the chapters. ... Read more

Reviews (3)

Cautious and applicable
I'm working with data sets where up to 15% of measurements are unusable. If I'm going to get any results at all, I have to get them despite the lost values.

This book provides a huge library of techniques for working around the holes, as well as techniques for filling them in. This is not a cut-and-paste text for programmers - it gives the basic theory and algorithms for each technique. Still, the presentation is quite readable and fairly easy to put into practice.

The book's emphasis is on imputation - filling in values so that analysis can move forward. This is something to approach with real caution, though. The imputed (synthesized) values must not perturb the analysis, so the imputation must differ according to the analysis being performed. The authors present a variety of imputation techniques, as well as bootstrap, jacknife, and other techniques for measuring the quality of the results.

The authors also dedicate chapters to approaches that work only with available data, and to cases where missing data can not simply be ignored.

This is the most thorough and practical guide I know to handling missing data. In an ideal world, experiments would all produce usable results and surveys would all have every question answered. When you have to deal with reality, though, this is the book.

the bible on missing data
Don Rubin developed much of the current theory on missing data. He and Rod Little have written eloquently on an important and difficult topic. This is the best available reference on missing data. Multiple imputation has been a highly successful technique. It was developed by Rubin and it gets good coverage here. If you are particularly interested in multiple imputation Rubin has another text devoted solely to it. The only drawback to the text is that standard software to handle the new methods was not available in 1987. So there is no coverage of software packages. However, Rubin has worked with Statistical Solutions to get imputation and particularly multiple imputation techniques into a software package called Solas. With Rubin's books and the Solas manual you will be ready to do imputation and more importantly you will understand the modeling assumptions that the methods hinge on.

Classic Text on Missing Data
This is the standard reference for statistics of missing data. Anyone working in the field will find it indispensable. The new edition is updated to cover a number of recent developments in the field. ... Read more

Book Description

The fourth edition of Probability, Random Variables and Stochastic Processes has been updated significantly from the previous edition, and it now includes co-author S. Unnikrishna Pillai of Polytechnic University. The book is intended for a senior/graduate level course in probability and is aimed at students in electrical engineering, math, and physics departments. The authors' approach is to develop the subject of probability theory and stochastic processes as a deductive discipline and to illustrate the theory with basic applications of engineering interest. Approximately 1/3 of the text is new material--this material maintains the style and spirit of previous editions. In order to bridge the gap between concepts and applications, a number of additional examples have been added for further clarity, as well as several new topics. ... Read more

Reviews (19)

Papoulis is Useful
I first encountered the works of Papoulis when just out of graduate school in pure math, and worked for a major defense contractor as an analyst. I found out that almost all the engineers there had this book, and purchased a copy. I had studied stochastic processes at a much more theoretical level than is presented in this book, and that study was significantly more difficult than the material in the text under review, so complainers take note. Why do I think this book an excellent one? Because it is so eminently USEFUL to the working engineer. I believe that has been the intent of the author in all of his works. If you're a working engineer who needs to find answers to tough problems, you can scarcely do better than to consult Papoulis.
For example, the material on power spectra is of more than academic interest and is useful in applications; the bivariate Taylor expansion for moments of a function of two distributions has been used again and again in applications in industry; especially in the analysis of the ratio of noisy variables arising from radar measurements. The point is that the text provides the material in a readily accessible way for someone who needs it in the "real world" of engineering analysis.

Too elementary
This book lacks rigor, and contains horrendous typos (but there is an errata sheet available) although makes a nice effort to "engineerize" the topic for the dumb reader (i.e. engineers).
Not meant for the mathematician, it makes no use of measure theory, and so you have to believe the results at face value.
On the positive side, it contains tons of worked-out examples, the chapters on distribution functions are quite nice and contain nice applications of calculus.
Other than that, it is a bit too elementary and avoids any of the interesting topics dealt with in more rigorous courses such as the stochastic integrals.

Did I already mention this is an easy book? I don't see why the other reviewers complain it is hard, it must be due to their low IQ, so I wouldn't worry about their comments too much. These engineers want the answer ready to copy down on their homework sheets, this book almost gives you the answer if you're able to do changes variables etc., although this is sometime a difficult task for freshman engineers.

Not easy but worth the effort
This is a book which definitely requires diligence and effort to get through. The excercises are also not trivial to say the least. However, if you have the energy and patience to actually slug through this text, in the end you will discover that you have actually learned something. Something which is profound and difficult to understand. This book is definitely not recommended as a casual reference.

Not a good textbook
IMO, this is not a good textbook. On one hand, it never explains the purpose of the materials. I know it elaborates on the random variables and different distributions and a lot of materials in detail, but I don't know where can I use these things. On the other hand, it omits the mathematical details, too. So when I read this book, I found unclear points everywhere. Someone else recommended this book as a good engineer reference. I think that might be true if there were less errors. I find errors in the equations every two or three pages. Engineers may not need to know the details, and they know what they need to model their designs. But they need the "correct" thing to do that. Maybe that is not the author's fault but McGraw-Hill's, but to me, a reader of the textbook, it is the same. No recommendation of this book.

Plenty Of Examples. Great Detail
This book has a huge pool of examples that enables the reader to understand the concept better. The subject itself is "NOT TRIVIAL", however if it wasn't for this book, it could have been worse. I own this book and I liked it. The examples are fairly easy to understand and relevant to the end of chapter problems. There is also a web site for this book that has a lot of additional resources. So if you are thinking of buying this book, then go for it. (Please Note that I am writing this review for the Fourth Edition Hardcover by Papoulis and Pillai. Previous edition is not that good) ... Read more

Book Description

Reviews (1)

Useful, but need solid background in stats
This book describes important advances in statistical analysis of social science data, circa 1992. Much of this data has a natural hierarchical grouping. But traditional statistical methods proved inadequate at coping. The biggest drawback was the failure of the assumption of independence. If at the lowest level, Items I1,...,In are grouped into sets J1,...,Jm, where mTo 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. ... Read more

Book Description

The product of a unique collaboration among four leading scientists in academic research and industry, Numerical Recipes is a complete text and reference book on scientific computing.In a self-contained manner it proceeds from mathematical and theoretical considerations to actual practical computer routines. With over 100 new routines bringing the total to well over 300, plus upgraded versions of the original routines, the new edition remains the most practical, comprehensive handbook of scientific computing available today. ... Read more

Reviews (33)

good book, bad policy
This is a very useful book for scientists and engineers, it collects codes for many most-often-encountered numerical problems, and the discussion is lucid, frank and helpful. However, the author adopted a very bad policy: they do not permit users to distribute their code. So suppose you write an application program which uses lots of integrations, linear algebra and differential equation routines, you would naturally like to use the numerical recipe routines for these basic tasks, but if you want to make your code freely available to others, you find you can't, because the numerical recipes routines are copyrighted and the authors forbid you to distribute even part of them with your code(except for a few public domain routines). They suggest you use the Netlib code which is freely available, however, since there is no systematic documentation, it is more difficult to use the netlib code. In any case, what is the point of having this book and its code if you have to use netlib code? this is really a trouble for the readers and users of this book. On the other hand, the authors provided their book online free of charge, but this is of little use--most readers would buy the book anyway, and prefer to have the code free.

Check GNU Scientific Library first
I give the book 4 stars to maintain the current level. I own a Fortran copy of NR, but like the other authors, I like NR for the explanations of algorithms, but not for the code.

There is a VERY good alternative to Numerical Recipes in C, namely GNU Scientific Library. You can find the source code and manual from:

http://sources.redhat.com/gsl/

or

http://www.gnu.org/software/gsl

As typical GNU software, GSL is licensed under GNU General Public License, so it is ABSOLUTELY free ! You can download it, modify it, linked it with your own code, without feeling guilty of copyright violation (Not in the case of NR, NR comes with a copyright license to prohibit modification and linking).

GSL is written in C from scratch by its author. The design is modern, much better than NR in C, and also allowed linking with C++ or modern scripting language like Python. Some of the leading authors have background in theoretical physics and astrophysics, just like NR authors.

Check it out. You lose nothing to check GSL first, you may ended up saving some $$$.

Excellent reference, but poor writing style and license
I had to endure reading this book for 2 long semesters, and I've come to know some parts of it pretty well. I'll try to be short and say that the book is an excellent reference for the practicioner (and for the poor student:) - however, the ill-placed "jokes" have terribly annoyed me and my fellow class mates. Entire pagagraphs in almost every section dedicated to some second-tier humor were not so helpful in solving numerical problems.

The license for the code is just bad and I found it rather pointless, given the cost of the book (for me it's expensive; and I know it's downloadable). The authors should maybe reconsider this at a later stage...

PS: The GNU Scientific Library implements most, if not all, of the NR routines. It might be worth checking out, since it's also in plain C.

Proprietary source the Achilles' heel for non-students
I first bought the FORTRAN version of this text in 1994 while doing scientific programming for graduate school work. I've been able to do a lot of basic research quickly with NR codes, and I still occasionally use NR's routines. The authors have certainly done a good job assimilating a lot of material in the NR series. Since other reviewers have done well to highlight the importance and utility of this landmark series, there is no need to repeat those sentiments here. I also agree with earlier reviewers applauding this title more as a survey or reference work and less as a library of source code. However, to this title's detriment, the authors actually consider the NR series to be a proprietary library of source code more valuable than the explanatory text surrounding it (one can in fact download the text on-line from the publisher though it's hardly worth the hassle). This perception is ironic since the authors confess that "the lineage of many programs in common circulation is often unclear," and many details of presentation, ideas, and algorithms are clearly "borrowed" from other excellent (some now out-of-print) numerical methods books or journals.

Unfortunately, much of the source code in the 1993 C edition appears FORTRANish and is not very efficient as far as the C language goes (one would hope that improvements are coming in the new C edition, ISBN 0521574382). However, even the original FORTRAN NR routines occasionally adopted bizarre and/or obviously inefficient programming structures - over time I decided that this was probably done to make these algorithms appear as so not to obviously plagerize other published material.

Many programmers try to get around this by reworking the NR codes. Apparently the authors consider modification of their sometimes inefficient code "derivative works" (even bug fixes) which cannot be legally redistributed or even used on more than one machine at a time without purchasing a new license or book. As a student, NR's legal disclaimers regarding derivative works never bothered me and I was willing to overlook the sometimes unpolished source code insofar as it functioned properly. But as a professional, I now find the lack of fair-use provisions on uncompiled, derivative source way too restrictive to rely on them in good conscience. I have since expanded my numerical methods library to other references supporting true public-domain codes. With an expanded basis of comparison, I regret to say that I am becoming less and less impressed with NR's implementations and explanations. I am finding some of NR's algorithms to be inefficient or unnecessarily approximate, and - on rare occasion - buggy. There have been quite a few bugs uncovered over the years, although the NR web site has done a good job of keeping track of them.

In closing, this book is excellent for students wanting a good reference for quick and dirty types of analyses or scientific computing. Professional programmers, scientists, engineers, specialists or analysts performing research would be well advised to reference this title, but ultimately they will likely need to rely other resources if they require efficient and/or unrestricted (public-domain) source codes for their work.

Useful for fourier optics simulations
I have completed numerous fourier transform algorithms (as well a FFT ones too) and this little book has been very helpful with most of its functions. I use it all the time to train my interns. Very good to get started... but beware that for advanced computing you might need a more complicated book. ... Read more

Reviews (1)