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

Fundamental topics and new methods in time series analysis
Analysis of Financial Time Series provides a comprehensive and systematic introduction to financial econometric models and their application to modeling and prediction of financial time series data. It utilizes real-world examples and real financial data throughout the book to apply the models and methods described.
The author begins with basic characteristics of financial time series data before covering three main topics: analysis and application of univariate financial time series; the return series of multiple assets; and Bayesian inference in finance methods. Timely topics and recent results include:
* Value at Risk (VaR)
* High-frequency financial data analysis
* Markov Chain Monte Carlo (MCMC) methods
* Derivative pricing using jump diffusion with closed-form formulas
* VaR calculation using extreme value theory based on a non-homogeneous two-dimensional Poisson process
* Multivariate volatility models with time-varying correlations
Ideal as a fundamental introduction to time series for MBA students or as a reference for researchers and practitioners in business and finance, Analysis of Financial Time Series offers an in-depth and up-to-date account of these vital methods. ... Read more

Reviews (3)

Analysis of Financial Time Series
This book is awesome. It starts with bedrock concepts needed for analysis of financial data and it takes the student up to the most recent and important techniques used in the industry today. However, if one expects to fully utilize this text, one should have at least one semester of applied econometrics or some equivalent course in statistics and continuous probability, although it will be practical to study the two topics concurrently.

A very practical book
This is not a reference book, and it's not about "big" theory either. It's pretty practical, and good for self study. You should have access to some econometric/statistical software (i.e. EViews, S-Plus, etc.) to fully understand this book.

Petition: please provide table of contents!
I want to see the table of contents of this book. How can the author and the publisher expect people to buy this book without providing the table of the contents?... ... Read more

Book Description

This book gives the reader important accounts of basic statistical procedures used in clinical trials. It covers several areas of study, including biostatics, biomathematics, biometry and epidemiology. There is emphasis for trialists to learn good methodology while giving quality clinical treatment. Discusses and explores controversial issues such as ethics and offers pragmatic information regarding allegations of fraud or misconduct. ... Read more

Reviews (3)

presents clinical trials issues and methodology clearly
This book is very unique. Basic statistical concepts are clearly presented but only those concepts that are important in clinical trials. The author presents all the issues with clinical trials including ethical issues with some historical perspective. Principles of randomization and statistical design are clearly presented. It offers discussion of Bayesian techniques and meta-analyses, cross-over designs and group sequential methods (interim analyses). For statisticians doing clinical research like myself, this is a valuable reference source.

The best start in clinical trial
The amount of knowledge and the scope of this book are the exact need for the first contact with clinical trials. Yet, it is not a simple or superficial text. Instead, it not only will guide the reader through the basics of trials (and there is so much that is not basic in it) but the author points the reader to hundreds of papers and books that are landmarks. I regard this book itself as one of these landmarks!

Most up-to-date and thorough cover of Clinical Trials
Covers many aspects of trials (particularly facets of design and analysis)not yet covered by other books, eg randomisation with minimisation, and meta-analysis of trial results. Readable, applicable, practical, good references, well structured. ... Read more

Book Description

INTRODUCTION TO PROBABILITY AND STATISTICS is one of the first texts published by Duxbury and has been blending innovation with tradition for over thirty years. It was the first statistics text to include case studies in it, and now in the eleventh edition, this text is the first to include java applets in the body of the text. It has been used by hundreds of thousands of students since its first edition. This new edition retains the excellent examples, exercises and exposition that have made it a market leader, and builds upon this tradition of excellence with new technology integration. ... Read more

Reviews (4)

Very poorly written.
I had to buy this book for an introductory graduate stats class. This book assumes you have some knowledge of stats and so provides very brief explanations for most concepts. This is a big problem for students with little or no stats background.

Another problem is that there are too few examples. Considering their meager explanations, one would hope they would at least provide enough examples to help students understand the concepts. But they usually provide only one or two problems, which are woefully inadequate.

Also, the CD is totally useless. Most of the practice is multiple choice, which I think is pretty silly for a stats class. In addition, there is no explanation for how they got to the answer, so if you can't figure it out and choose the wrong answer, you're out of luck for any explanation.

I stopped using this book for my course and instead began using Elementary Statistics, A Step by Step Approach by Bluman. This book is excellent; it explains everything from step 1. There are also many, many examples and lots of pictures to help you further understand stats. Also, they explain very clearly when you should use the different formulas, which I found extremely helpful in bringing all the concepts together.

Disappointing
The text we used for our undergrad Stats class covered the exact same material as this text, which we used for a graduate level class and which cost about 60 bucks more, but the undergrad text was written more clearly, concisely and logically.We even found a mistake in one of the tables in the back of this book.

An "easy" introduction but lacks content
Author: Computer Science/Mathematics Undergraduate at Cameron University

This book was required for an introductory non-calculus based statistics course at my school and it turned out to be a mixed bag.The theories are explained well in most cases but the book doesn't have enough examples for all of the theorems.Many times only one case is explained in dynamic situations leaving this reader more than a little lost.Usually the theorems are explained well enough that this is not a big deal, but not so in every case.So there tends to be those points where the student may get a little stuck without assistance from their professor.

Another problem I found was that the book was a little bit dumbed down in many areas.This book uses no calculus, so the theories are presented often without the mathematical rigor required to properly formulate accurate results.The book still presents the theorems and formulas in a way where the student can get something out of this without calculus, but it becomes a problem later in the book in the sections on regression.For a non-calculus based class like the one I took, this is all unavoidable, so the book handled the situation well considering the constraints.

This book presents a very light introduction to statistics and is good prep for more advanced statistics courses, but as a stand-alone only the most basic material is presented since it is non-calculus based and the material is spread thin between combinatorics, probability, binomial distributions, normal distributions, t distributions, ANOVA tests, regression, non-parametric statistics, etc... To attain a significant amount of useful knowledge you really need to buy separate books tackling each of these subjects independantly, so this book is really unneccessary even for an introductory text but might serve well the student who needs to be eased into statistics lightly or just wants a brief overview of the subject.

Very good as a required text
This was the required text for a 2003 graduate Biostatistics course, so you should already know the basics.If you don't, buy the Study Guide and Solutions Manual to help you out. ... Read more

Amazon.com

With the stock market breaking records almost daily, leaving longtime market analysts shaking their heads and revising their forecasts, a study of the concept of risk seems quite timely. Peter Bernstein has written a comprehensive history of man's efforts to understand risk and probability, beginning with early gamblers in ancient Greece, continuing through the 17th-century French mathematicians Pascal and Fermat and up to modern chaos theory. Along the way he demonstrates that understanding risk underlies everything from game theory to bridge-building to winemaking. ... Read more

Reviews (111)

No Risk here, The Odds areYou'll love it.
i'm very much a novice in the study of risk and probabilities; however, I've been lucky enough to come across some excellent business oriented books - but in actuality and thankfully far more philosophical than financial - (Fooled by Randomness for instance) lately that have done a masterful job of presenting this seemingly dry subject in a very fascinating way. Bernstein has probably written the quintessential historical study on Risk and Probability with this volume. It is filled with interesting details of the human qualities and quirks of the mathematicians and philosophers that investigated the problems of probablity and explains the mathematics involved with lucidity and wit. The book follows a chronological approach; however, it's also thematic to show the evolution of the subject of risk management and the influence of other sciences on it - such as evolution. Those that refuse to accept the idea that markets remain unpredictable despite the development of modern risk control mechanisms like derivatives, computer modelling, and modern financial instruments will find this book frustrating. Those who want a wquick fix investment solutions guide will be even angrier and will nort find anything useful here. This book belongs in the philosophy of science and History of Ideas sections far more than the business dept. Nonetheless, the serious and thoughtful investor is advised to read this book carefully. as someone who's lost in the stock markets himself the Bernstein's book can reduce the sting of the loss and impart some well needed wisdom. I no longer invest but I loved this book all the same.

Long on history, but short on risk management strategy
The title of my review is aimed at warning those expecting to find a risk management manual in this book that they will be disappointed. So will those who expect to find the links between the evolution modern statistics and acturial science to the rise of insurance markets and risk management instruments which have proliferated in this century. Many other books quite ably cover these interesting topics.

Instead, the author provides a broad sweeping history of how modern statistics evolved and which answers some questions of why it took so long for modern risk management institutions to emerge. Ancient Greeks, among others, who appeared to be within easy reach of developing statisical theory, nonetheless relegated their fate to the whims of gods, rather than making them amenable to analysis with probabilities and actuarial tables. Tracing modern risk management from the time of Jacob Bernoulli's attempt to develop probabilities from sample data, the author also shows how a knowledge of probabilities can ultimately generate value. QUOTE Reality is a series of conneceted events, each dependent on another, radically diffeent form games of chance in which the outcome of any single throw has zero influence on the outcome of the next throw UNQUOTE The book closes with risk management innovations that followed the emergence of financial volatlity in the 1970s.

Ultimately, this book may be of less interest to statisticians and investment professionals, other than those who have a curious interest in how today's highly developed set of instruments, institutions, and policies around risk came about from the foundations provided in statistical theory.

History Buffs: Here you go!
Against the Gods draws you through a vast time span. Peter Bernstein begins with the conception of the Arabic numbeting system, up through present time super speed computers. Although, the history found in this book is interesting, the title leads you to believe it is all about investment risks, however it is more of a history text book than a manual. This book is a story of theories and how they developed. You will learn quite a bit about ancient times and how things evolved into the way that they are now, but do not expect any great help or advice on how to deal with risks in the investment world. Once you get into this book, Bernstein's writing sytle draws you in. The book is interesting enough, Bernstein's knowledge of hisotry is astounding. History Buffs: here ya go!

Good outline of the history of risk
"Against the Gods" is a book outlining the history of risk. The book provides an outline of all the key players and their contribution to risk theory and management. Chronologically, the book begins in ancient times and stretches all the way to the present, where Bernstein delves into the works of modern day risk luminaries. The book is well written and the style is engaging, with the author always managing to find a way to keep the reader entertained as well as informed.

The book does not pretend to be a "how to" guide for risk management, nor should readers treat it as such. Although the book does discuss modern risk management tools such as derivatives, it is devoid of complex technical analysis and its treatment of such devices is limited to outlining their place in the history of risk. Those looking for technical trading analysis should seek elsewhere.

One of the key questions a potential reader of this book should be asking is "Does this book have any practical applications with regards to modern day risk management?" Whilst as mentioned above the book is not a step by step guide, I firmly believe the book is useful insofar as it enables the reader to avoid the pitfalls of the past. For example, capital markets are continually surprising those who hold an unwavering belief in "regression to the mean". The books provides an explanation of what this theory states, how it has been applied and where overzealous disciples have misused this principle in the past. Overall I would recommend this book as an informative and enjoyable read.

Neutral Recommendation - Do Not Buy - Maybe Borrow
I am going to give you a short review and to the point.

I read this book because it was recommended on "Money Talk" the national radio show on every weekend for 6 hours on investing - Bob Brinker hosting. He is an excellent market timer and gives solid advice. Follow his (diversified investment) advice and you will make lots of money and unlike mutual funds do better than the S&P 500 with low expense ratios.

He had a recommended reading list and he named this book. The book is a disappointment. It is light weight stuff. Only part is on the markets. Frankly I cannot recommend the book.

The point of the book is that the market carries risk. Most people know that and never put more than 4% in one stock. Even Bill Gates knows that and has quietly converted some of his Microsoft stock into other areas. So skip the book and just invest in government backed instruments or follow the golden rule, no more than 4% in one stock.

Jack in Toronto ... Read more

Book Description

Amstat News asked three review editors to rate their top five favorite books in the September 2003 issue. Categorical Data Analysis was among those chosen.

A valuable new edition of a standard reference.
"A 'must-have' book for anyone expecting to do research and/or applications in categorical data analysis."
-Statistics in Medicine on Categorical Data Analysis, First Edition

The use of statistical methods for categorical data has increased dramatically, particularly for applications in the biomedical and social sciences. Responding to new developments in the field as well as to the needs of a new generation of professionals and students, this new edition of the classic Categorical Data Analysis offers a comprehensive introduction to the most important methods for categorical data analysis.

Designed for statisticians and biostatisticians as well as scientists and graduate students practicing statistics, Categorical Data Analysis, Second Edition summarizes the latest methods for univariate and correlated multivariate categorical responses. Readers will find a unified generalized linear models approach that connects logistic regression and Poisson and negative binomial regression for discrete data with normal regression for continuous data. Adding to the value in the new edition is coverage of:

• Three new chapters on methods for repeated measurement and other forms of clustered categorical data, including marginal models and associated generalized estimating equations (GEE) methods, and mixed models with random effects
• Stronger emphasis on logistic regression modeling of binary and multicategory data
• An appendix showing the use of SAS for conducting nearly all analyses in the book
• Prescriptions for how ordinal variables should be treated differently than nominal variables
• Discussion of exact small-sample procedures
• More than 100 analyses of real data sets to illustrate application of the methods, and more than 600 exercises

Reviews (3)

A classic, made even better
This is a very demanding, thorough, and clear description of just about everything anyone could want to know on the subject. The second edition is considerably more rigorous than the first. Agresti stresses that logistic models are one kind of generalized linear model. This offers solid connections to many other models, but places corresponding demands on the reader. In particular, Chapter 4 is difficult going, but might be skipped or skimmed on first reading.

Given the mathematical level and rigor, this is a remarkably clear book. Anyone who analyzes categorical data on a regular basis should read it and have it on his or her shelf.

Categorical Data Analysis
Book comes with great condition, although the shipping speed is somehow disappointing.

some day should be a Wiley classic
When this book came out in 1990 it was the first book to provide a truely modern treatment of categorical data analysis for both ordinal and nominal data. It provides an excellent treatment of the asymptotic theory for binary and multinomial data. It is extremely well written and is still a favorite of statisticians and practitioners. Because of its popularity and continued value, it should soon be added to the Wiley Classic series.

This is the first book to take the regression approach to categorical data analysis tieing the subject to the methods and theory of the generalized linear models. It also was one of the first to show the modern practicality of exact permutation methods.

The only drawback of this book is that it is 11 years old and there have been many interesting and relevant research developments in computer-intensive methods, analysis of missing data and mixed effects linear models to make a revision useful. Some of the latest developments can be found in Lloyd's new book "Statistical Analysis of Categorical Data" that was recently published by Wiley.

Agresti provides clear advice and also gives a nice historical perspective on the development of the subject. The book is authoritative and includes numerous relevant references. Each chapter contains many exercises and a wealth of practical examples for illustration of the techniques. This is a good text from both practical and theoretical perspectives. It is excellent for a graduate level course on categorical data analysis. ... Read more

Reviews (7)

Excellent book
Having taken two experimental design classes with Dr. Montgomery himself, hands down this book is excellent, as is the author himself. A lot of the material that we used in our advanced phD level experimental design class comes from the extra material that is available for free of off the books supplemental website, and there is a great wealth of more advanced material in there that is worth printing off, and there is a lot of supplemental material. So if you feel solid on what is in the textbook, tackle the supplemental material to further delve into DOE topics. The book uses numerous examples using SAS, Minitab and Design-Expert and lots of practice problems, so following along using any of the packages is pretty easy.

Comprehensive Introduction to the Design of Experiments
This text was used in a graduate course on the Design of Experiments. It has a great wealth of examples that span many different fields of industry, from pharmaceuticals to "tool and die" manufacturing. This text, when used along with a statistical software package, made research projects easy to design, implement, analyze and present to others. I found the chapters on reliability, determining sample size for mixed models (random and fixed variables), very helpful in my research.

great DOE book for engineers
Doug Montgomery is an engineer with great statistical training. This enabled him to be a great teacher for statistical practitioners and an author of several excellent applied books in the design and analysis of experimental data and statistical process control. This book is one of the best sources for Design of Experiments for engineers, currently a very important topic in industry. He provides a wealth of information on classical designs and the analysis of variance models. In addition he provides a balanced perspective on the value of Taguchi designs. Other topics covered include response surface methods, evolutionary operation and mixture experiments.

highly regarded book on design for engineers
Doug Montgomery got excellent statistical training at VPI. He also has a wealth of practical experience from his consulting work. His books on regression, statistical design and response surfaces are all well written and understandable to engineers. This 4th edition published in 1997 still contains all the useful information on factorial and fractional factorial designs. Chapter 14 is a wonderful up-to-date chapter that covers important process optimization topics not often covered in traditional design of experiments books. This includes response surface methods, mixture experiments, evolutionary operation and Taguchi methods. It does not require high level mathematics.

A good blend of theory and practical examples.
This book, along with the Box, Hunter, and Hunter book (also available by Wiley) are good references for the experimenter. ... Read more

Book Description

This introductory text provides students with a conceptual understanding of basic statistical procedures, as well as the computational skills needed to complete them. The clear presentation, accessible language, and step-by-step instruction make it easy for students from a variety of social science disciplines to grasp the material. The scenarios presented in chapter exercises span the curriculum, from political science to marketing, so that students make a connection between their own area of interest and the study of statistics.

Unique coverage focuses on concepts critical to understanding current statistical research such as power and sample size, multiple comparison tests, multiple regression, and analysis of covariance. Additional SPSS coverage throughout the text includes computer printouts and expanded discussion of their contents in interpreting the results of sample exercises.

Book Description

This book offers a straightforward, “nuts and bolts”, introduction to statistics. The explanations are clear and simple and minimize calculations where possible. A diverse range of applications and examples are presented to make the materials appealing to a wide range of learners.The Eighth Edition features new problems, more real data based examples and exercises, and fuller integration of technology.For individuals beginning a study of statistics. ... Read more

Reviews (1)

Probabilities
I have a little problem understanding the probabilities. I need somebody to help me ... Read more

Book Description

Statistics is a thought process.In this comprehensive introduction to statistical methods and data analysis, the process is presented utilizing a four-step approach:1) gathering data, 2) summarizing data, 3) analyzing data, and 4) communicating the results of data analyses. ... Read more

Reviews (1)

*Excellent* stats book for behavioral researchers
We used this as our primary textbook in a graduate psychology / consumer behavior research methods / data analyis class, and I found it to be an invaluable aid in statistical analysis. From simple measures such as z-scores, t-tests & f-tests, and chi-squares, to more in depth methods such as GLM, ANOVA, and regression, this book is clear and concise. While it can be slightly cursory at times as to the theoretical underpinnings underneath the data analysis methods being used, it is a very helpful book in understanding how to actually make the methods work, and more importantly, knowing which method to apply in which situation.

Untill Prof. Lattin releases his statistical analysis book for behavioral students, Ott's book is the #1 stat resource on my bookshelf. ... Read more

Book Description

STATISTICS FOR MANAGEMENT AND ECONOMICS is the worldwide best selling business statistics text. It is currently being used at hundreds of colleges and universities throughout the world. This text teaches students how to apply statistics to real business problems through the authors' unique three-step approach to problem solving. Students learn to IDENTIFY the right technique by focusing on the relationship between the problem and data type. They then learn to COMPUTE the answer either by hand, using Excel, or using MINITAB. Finally, they INTERPRET the results in the context of the problem. This approach enhances student comprehension as well as practical skills, and offers maximum flexibility to instructors wishing to teach concepts by hand or with the computer, or by using both hand and computer methods. ... Read more

Reviews (5)

My Bible
This book became my statistics bible. After never attending any statistics classes at the University, I was suddenly forced to work with multiple regressions at work. This book helped me understand all about statistics that I needed for my business purposes. The examples are very helpfull to understand what the tests actually do and how you can solve your own inference problems. I read the whole book in about 10 days (2 hours a day) at the beach and now work mainly with the overviews in the last chapters when I have a problem to solve at work.

A lot of ambiguity and errors
I had to use this edition as text book for my Statistics Course in an MBA Program and found multiple errors and ambiguities in describing basic concepts. Lack of rigurousity in presentation is pervasive. Although an engineer by formation, I found this book a terrible reading as an initiation in statistics!

Warning don't go near the 5th edition
The 5th edition of this book, available still in many places, is one awful book. The instructions for most of the Minitab examples are incomplete and in many cases worthless. Many explanations are superficial and the tie in to the overall statistical picture is weak. I have heard the 6th is better, the 5th should not have been printed.

Basic an clearly understandable
This book covers a wide area of fields necessary for business and economic students. It also explains how to go on if necessary conditions are violated. Time-series analysis, which is very important for economic students, are not covered to the extent as other topics, but it nevertheless gives a good introduction of other statistical concepts necessary for understanding following books on time series analysis. For my opion there are a little bit to much exercises than necessary, for an autodidactic study but enough for class room work. A very fine feature is that there never come up terminologies which has not been discussed before and one can use MS-Excel.

It's a very usefull book
It's realy usefull for to problem solving. It helps to learn easly to solve the problems with Excel. ... Read more

Reviews (1)

very nicely done!
I've just started working with this book, but I'm very impressed. Very clear layout, has lots of examples and exercises (with answers to even numbered ones). Very good about explaining why things are done a certain way, and especially strong in asking questions a typical reader might have. I'm recommending this for my graduate course on research methods for information technology. ... 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

Marcello Pagano and Kimberlee Gauvreau's PRINCIPLES OF BIOSTATISTICS, Second Edition is a concepts-based introduction to statistical procedures that prepares public health, medical, and life sciences students to conduct and evaluate research. With an engaging writing style and helpful graphics, the emphasis is on concepts over formulas or rote memorization. Throughout the book, the authors use practical, interesting examples with real data to bring the material to life. ... Read more

Reviews (2)

Better than nothing...
This book helped me (a non-biostat person) pass a advanced biostats class. It was a good suppliment to a fast-paced course, as it gives the same info, but somewhat easier format.

On the other hand, it occasionally skipped steps and made confusing leaps.

So it was better than nothing.

another good introductory biostatistics book
Like Rosner's book by the same publisher, this text is an introductory text by Harvard professors who teach medical students. It seems to be in competition with Rosner's book which makes me wonder why the same publisher is publishing them both. The style is different but the market and level of the two books seem to be the same. Both texts include diskettes with data for PCs. Both books are well written and cover mostly the same topics. The Pagano text seems to go into a little more detail on contingency tables and survival analysis. Both contain lists of valuable references. ... 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

* 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

This book is about the use of modern statistical methods for quality control and improvement. It provides comprehensive coverage of the subject from basic principles to state-of-art concepts and applications. The objective is to give the reader a sound understanding of the principles and the basis for applying them in a variety of both product and nonproduct situations. While statistical techniques are emphasized throughout, the book has a strong engineering and management orientation. ... Read more

Reviews (5)

good book worst service
the book is very good ..no doubt but the service is aweful.

1. on the website they show it is a hardcover book and so i ordered for it but got a soft cover book ...now i have to worry about return or refunds etc...

2. had to wait for 3 weeks to get the book.

3. print quality aweful ...the original book has good color print...the one i received for the same price has a black and white print with very thin see-through pages...

my advice would be to avoid abebooks.com through amazon if possible because the same book is listed for $ 33 on their own website and here they sell it for $ 41 ...so waste ur money if u want.

Everything You Ever Wanted to Know about SPC
As the developer of the QI Macros SPC Software for Excel, customers often call with exotic questions about statistical methods for quality control.

Montgomery's book, more often than not, is the one I turn to for answers that I can't seem to find anywhere else. Even if I do find the answer in another book, it's always easier to understand in Montgomery's. The book is written in a way that makes seemingly incomprehensible statistics understandable. And there are plenty of examples.

If you're looking for a book to help you implement quality control in a small business, Montgomery's book is a bit too much, so you might consider my book on the Small Business Guerrilla Guide to Six Sigma or Six Sigma Simplified.

A solid text on statistical quality control
The book gives an overview of the importance of quality management, the basics of statistics (variability, distributions, etc.), different methods of statistical process control, the use of control charts, capability analysis, design of experiments, process optimization, and sampling. I found all of the chapters informational and practical.

Montgomery does a great job of presenting the theory, giving examples, and helping the reader understand the big picture of various concepts. For example, Montgomery states that a "Pareto chart does not automatically identify the most important defects, but rather only those that occur most frequently," and then gives an example illustrating when this can be so. This is something that might have been overlooked if not pointed out to the reader.

While to book is rather current in introductory theory and practice, there are some relics from previous editions. For example, the inclusion of a random number table in the appendix is rather useless, since all modern calculators and spreadsheets have random number functions. This minor complaint aside, the book is solid and worth having.

An all-in-one text.
This is a rather comprehensive book on SQC. It benefits from a good introductory treatment of Design of Experiments, a subject sorely missing from most SQC texts. The arrangement of topics in the book is logical from both pedagogical and practical points of view, and the author's stress on improvement -- rather than control -- is the right one for the readers.

A college beginner's SQC textbook
This is a typical college text book which serves very well on the subject,SQC. It has provided a good coverage on SQC, in particular, as an introductory text. Each SQC subject is treated well from a newcomer perspective. If you're a beginner in the college education and looking for a book on this subject, this is IT! ... Read more

Book Description

Andy Field has written an up-to-date student-oriented textbook with the aim of making the learning of advanced statistics and using SPSS as painless as possible. While other books either concentrate on statistical theory or on the functions of the popular computer program SPSS, Andy Field integrates the two to provide the student with a thorough grounding in statistics through learning to use SPSS. He provides a detailed, but highly accessible, guide to using SPSS for more complex statistical tests. There are illustrations of dialogue boxes throughout, and each chapter concludes with a set of exercises and descriptions. The book includes a CD-ROM with SPSS datasets and the author provides a website for further help and updates. The book is suitable for use with SPSS versions 7.0, 7.5, 8.0 and above. ... Read more

Reviews (14)

"An essential book of statistics"
Discovering Statistics Using SPSS for Windows uncovers simultaneously the functional foundations of basic statistic and how to work with SPSS statistical software. In addition, Andy Field transformed complex concepts into palatable words.Therefore, if you do not have much time and need to learn statistics in a short period, this is the best book avalailable.

Discovering Statistics using SPSS for Windows
One of the things I admire about people is their ability to recognize others who share similar interests and talents and to give credit where credit is due. In the preface to his book Andy Field cites other authors who have written books about SPSS and stastical theory. I have bought those books and they are well worth the price.
This book is an excellent introduction
to both SPSS and statistics. The book is written with a light, humorous style. It has a lot of examples of statistical analysis. It is obvious that the author has an excellent grasp of SPSS and statistics. It contains a very useful bibliography on statistics and SPSS. There is also a CD ROM included. The book is very well organized and has high quality screen prints of SPSS windows and dialogue boxes. It is one of the best software manuals I have ever purchased. It is well worth the price. If you don't purchase any other book on SPSS, purchase this one

This book saved my life
I have taken statistics as an undergrad and as an MBA student. I am good at math, not the stereotypical math phobic female. But I always have had problems connecting the statistics with what to do, when to do it and what the results were telling me. Well, here I am again as a graduate student working on a Masters degree in Sociology. I am working on my thesis and this book pulled everything together. It matched up SPSS and statistics so that you know what to do, why to do it, how to make SPSS do what you want, and how to interpret the results. I cannot recommend this book enough. It is very clear, very cogent, in English as opposed to stero instructions, and charming as well. I highly recommend it!

Discovering Statistics using SPSS for Windows
The advent of statistical software for the personal computer in the 1980s can be argued to be both a great advancement as well as a critical liability for the field of research. Statistical software has certainly made difficult analytical tasks easier to accomplish, enabling more people to benefit from the use of quantitative techniques. The ever-increasing speed of the personal computer allows researchers to conduct complex analysis in minutes that would have taken days to complete manually. However, with this increased speed and usability comes pitfalls. The ease and speed of statistical software has encouraged some researchers to take a shotgun approach to