Paul Wallace offers an engaging and insightful examination of the prospects of an aging society. The title that wallace has chosen hints at the magnitude of the anticipated changes of a 'greying society'. Wallace makes extensive use of UN and OECD studies to wake up anyone who doubts that an aging society is not an issue that ought to concern them, their finances and business. For the more serious minded, there are plenty of specialist texts to consult. For a broad overview of the likely impact of an ageing society, wallace's contribution is timely and accessible since he spares us of a lot of technical jargon that may intrude (economists usually have plenty to say on this topic!).

Wallace constructs his analysis by building on the theme of his choosen title. Part one, explores the 'faultlines' that major studies identify, and awaits explanation in the next section, the 'tremors'. If you understand the logic of the opening chapters, then the 'shockwaves' should not be a big surprise. I suspect we'll have to live through it before anyone truly understands it. This is partly a get-out clause for political leaders who ought to urgently set out plans for, for example pensions reform, but since people and politicans don't tend to think and act for the long term, you can imagine wallace urging disapproval.

Each facet of life merits a mention; from ageism in the workplace, to the effect on property prices, spiralling healthcare costs and the impending 'pensions crunch'. While many of the current baby boomer generation are looking forward to early retirement, wallace leaves us to wonder if we, the younger generation, will have to work so much harder to share their (modest) ambition of enjoying their leisure after 40 years of labour?

A good read.

When I moved into France from my native Colombia, I was astounded at the number of old ladies with dogs and the paucity of mothers with babies and teenagers. Agequake confirms that this perception is just another aspect of the huge changes that new demographics will bring on within the next 20 years. The good news is that people are living longer. The bad news is that they are retiring at a younger age. And that, the longer they live, the more medical care they will require.

Wallace shows that the dependency ratios (the ratio of non-working "dependents" to working folk) will lead to a point where each worker will need to support not only himself, but a pensioner as well, and his own children, if any (and there will be very few). As the number of young working people, usually the more creative of all age-groups, continue to shrink, innovation will also came to a halt, and ultimately economic growth will vanish and then reverse the secular growing trend. While some environmentalists may feel overjoyed by this implosion of capitalism, most of us who rather liked material comforts and hoped that they would continue to grow endlessly will be less satisfied. The impact of the "agequake" will be felt in every sphere. Corporate hierarchies will make less sense when there are more middle-aged managers than young newcomers. The relationship between youthful and aggressive Third World Countries and rich older OECD countries (where elderly women will be the most influential constituency) will be fraught with dangers. Share prices will tend to collapse as the "Baby Boomers" start to retire and prefer to liquidate some of their assets. The housing market will be altered beyond recognition.

What can be done to avoid this future? Unsurprisingly, not much. Government policies cannot permanently improve fertility in rich countries, immigration in the scale required to make up for the shortfall of young workers will be politically indefensible, and the growing importance of older voters makes it virtually impossible for politicians to effect changes in fields such as retirement ages, pensioners' rights
or public health.

All in all, a sobering read. When these things happen, those of us who read it will have at least a headstart on everyone else. Not bad for a few bucks, eh?

A modern view of the rollercoaster of the stock market, this book really describes just what the market can be like. One day you're riding high, and the next you can be in the pits!

This tidy book is a highly readable, introductory survey to the topic of modern time series analysis. It excels in its ability to focus on the more intuitive aspects of analysis and model identification. The discussion of both time- and frequency-domain approaches is reasonably balanced, and Kalman filtering is also introduced. While it touches on many modern aspects of time series analysis, it sometimes (intentionally) lacks important technical depth necessary for implementation.

The author has done an admirable job at keeping the book manageably small. However, the reader is occasionally left wanting where interesting details are omitted because the author considered them "beyond the scope" of the book. For example, the preface mentions that several new topics are incorporated into the 5th edition (wavelets, for example), but the reader only finds a gratuitous single paragraph with references to complementary journal articles. In these few rare cases, the discussions are not intuitive enough for the reader to know whether it would be profitable to bother with further research at the professional journal level. Still, this title does well to reference the most important landmark works in the time series literature. Those performing remedial research may find it is easier - and more productive - to simply consult Chatfield's recommendations of important topical works before resorting to online or library literature searches.

This text has been in print since 1975 with new editions arriving every 5 years or so (perhaps even a 6th edition is close, since the last edition is copyrighted 1996). I am usually suspicious of textbooks having increasingly larger numbers of editions because the continual re-writing implies some level of recurring insufficiency. However, the frequency of update is probably justified due to continuing advances in this field of study. As a result, this title is surprisingly current given its introductory status (although the 4th and 5th editions do not differ too much).

For someone new to time series analysis, this may be one of the better places to start, especially for the price. Readers lacking in intuition or experience in time series analysis - especially non-statisticians - will certainly appreciate this introductory title. The more experienced analyst will also be well served by the author's expert perspectives - but to do practical work, this text will still likely need to be supplemented. The generous citation of additional literature will help the reader to know where to go next.

When I was a graduate student at Stanford my advisor taught an elementary time series course out of Chatfield's book. It was either the first or the second edition. I was his teaching assistant. The book has been very successful and is now in its fifth edition. It covers most of the important topics concisely and in an intuitive manner. This book gives the student a feel for time series analysis and an appreciation for its applicability. It is not meant for someone who wants a rigorous treatment and a strong understanding of the theory. For that the text of Brockwell and Davis or Anderson or Brillinger or Priestley are more appropriate.

Realmente explica de manera clara, conceptos básicos en series de tiempo, por lo cual lo recomiendo para cualquier persona, así conocera el fantástico Mundo del análisis estocástico en el tiempo...

Before going to the book, one advise is to have prerequisite to this topic, i.e. you need to come prepared with strong statistic and probability background since many of the derivation and proof assume the reader is well into the probability theory. I took the class from Stanford department of statistics, man, it doubled the time I spend comparing other statistics students since my training on probability is rather self-taught and not quite systemetic. Well, overall, it's a classic..

Very mathematical oriented, not at all intuitive. Of limited use to financial quants without extensive formal training in advanced mathematics.

This is one of those rare mathematical books that is both deeply
informative, and a sheer pleasure to read. The book is written in a
delightful old mathematical style, where the authors take you by hand
through the difficult passages and derivations. The intuition about
stochastic processes is so well conveyed, and the mathematics so well
explained, that the book can be read with little or no recourse to
pencil and paper, much as if it were an armchair book. The book
presents a comprehensive overview of the theory of stochastic
processes, and I wholeheartedly recommend it to anyone interested into
learning their foundations.

I wish somebody would write a concise tutorial of the matematics required for an "intermediate" book such as Dobson's. Undoubtedly for someone whose acquaintence with modern statitical methods is more current this book is a gem. For someone like myself who wants a refresher and whose math is a bit rusty it leaves something to be desired. Some of the theoretical derivations in chapters 3 and 4 (keys to the understanding of the rest of the book) would be improved by a bit more detail and a thoroughly worked example. A major shortcoming is the lack of answers to the excercises; I don't see how the book was published without them. If the book was 100 pages longer with the addition of the aforementioned material, I would have given it a five star rating.

My copy of the second edition just arrived yesterday and it is even better than the first edition (which was fantastic). The logical organization and clarity of writing make this book a 'must have' for any statistician's library. I'd give it 6 stars if I could. Readers should also check out McCulloch and Searle's 'Generalized, Linear and Mixed Models'.

...

Bill recommended Dobson's text because of her clear writing style and many useful examples. Dobson also places the theory in the context of the general exponential family of distributions. As I knew that the second edition was about to come out I waited for it.

The wait seems to have been very worthwhile. The second edition is a real bargin.... She has updated it with the many advances that have occurred over the past 12 years since the first edition was printed. This edition now includes some discussion of generalized additive models, broader coverage of applications as survival analysis, GEE, multi-level models and nominal and ordinal logistic regression have been added. It now offers the reader more applications in a wider variety of disciplines and includes modern approaches to diagnostic checking of the models.

As with the first edition, exploratory techniques are emphasized particularly graphical methods. The goal is to unify the apparently disparate statistical techniques that students are exposed to, into one general modeling framework.

It includes a nice up-to-date bibliography and recent advanced results on longitudinal models. The level is intermediate statistics with introductory statistics and linear models taken to be prerequisites. Students are also required to have some familiarity with calculus and linear algebra.

I thank Mr. Peng for saying the book "would serve as a decent linear models reference." J. Stapleton

Approach of linear models from a geometric point of view is often addressed in a single lecture or chapter and does not provide much assistance in the understanding of the material. This book, however, presents all material usually covered in a linear models book from this approach. For those who have learned models both ways, proofs and applications using these geometrical concepts are much less cumbersome than the standard matrix algebra manipulation. Additionally, having a solid understanding of this material is a greater help in the understanding of more advanced topics. As stated, the book is dense and previous exposure is useful if the reader is not assisted by a knowledgeable instructor.

I've used this text twice for a one-semester graduate course in linear models, emphasizing Chapters 1,2,3,5,6, and would use it again. It is outstanding.

The best feature of the book is its consistent theme: a least squares estimator is an orthogonal projection onto a subspace, which can be evaluated by orthogonal decomposition of the subspace. This gives the subject the elegance of pure mathematics, while at the same time making complex topics such as two-way and three-way analysis of variance readily accessible.

The second-best feature of the book is the extensive collection of problems. Most are just at the right level, not simply cookbook plug-in type exercises, but problems that require understanding, yet not too difficult for the average student, who is typically not a math major. A few of the problems require statistical software, but most do not.

The only negative aspect of the book is the large number of errata, although this does have the advantage of teaching the students to adopt a healthy degree of skepticism.

This is a pretty cool book.

I purchased "Lottery Master Guide" and " Lotto How to Wheel a Fortune". They are both fantastic books. I was " blown away" with the vast amount of tracking techniques that Gail Howard managed to uncover. I've been working for years programming my own system: I could have save a lot of time and effort if I had first found Gail's books and computer programs. a plethora of "wheels" to choose from -- great for any betting budget.

Seventy-four (74!) first prize jackpots worth $97 million were won with Gail Howard's systems. The best lotto wheeling systems are in the 397-page 1997 Third Edition of Lotto How to Wheel a Fortune.

The authors provide clear and sufficient explanation for the most common multivariate statistical techniques. Well written and easy to follow.

As someone who has tried to teach multivariate statistics to non-statistician graduate students for the past 5 years, I have found this to be a very valuable and clearly-written text. As advertised and as the previous reviewer noted, the text is largely free of complex statistical equations and instead has clear descriptions of each type of test as well as common applications of that test. It is a perfect introduction for students who are intimidated by numbers and equations yet need to know about multivariate statistics for their graduate studies.

The book has several weaknesses that I found require supplementing with other texts. For one, there is no tie-in with major computerized statistical applications like SPSS and SAS nor are there example exercises for students to run and interpret statistical tests for themselves. I have found such exercises to be invaluable in teaching the meaning and uses of multivariate tests. There also should have been a discussion of general issues that cut across the different multivariate tests such as data cleaning, data transformation, the role of correlation matrices and the like and so on. For coverage of these issues, I have found it helpful to use chapters from Tabachnik and Fidel's Using Multivariate Statistics text. Finally, a number of tests, such as survival analysis are not covered in this text, though a second volume by the same authors does cover survival analysis as well as other techniques and should be considered as a companion volume as well.

In sum, this is an excellent and unusually clearly written text that is ideal for non-statistician graduate students in the social sciences. More in-depth analysis of important issues related to multivariate statistics and classroom exercises using statistical computer applications requires augmenting this text with additional readings.

"Reading and Understanding Multivariate Statistics" achieves exactly what its title implies. Geared toward non-statisticians in behavioral and social science fields, this book provides clear and reasonably simple explanations of some of the most common multivariate analyses. Each chapter focuses on a different analysis and presents its conceptual underpinnings, underlying assumptions, and basic procedures with a minimum of equations and many concrete examples. It does not teach you how to perform the analyses but does provide references for those who wish to get more detailed information. As a research scientist who doesn't always remember everything I learned in graduate statistics class, I find this book an invaluable aid keeping up with the current literature in my field and in making the most of statistical consultations. This book is ideal for anyone whose job requires them to be a "consumer" of research; for researchers who wish to further their understanding of data analysis; and as a companion text for graduate statistics classes.

This is an extremely valuable compendium of what almost any pracitioner needs to know about 40 of the most commonly used statistical distributions. It is designed as a quick lookup reference for each of the distributions. Most chapters begin with a few brief lines describing some of the applications of the distribution, and then provide a list of relevant formulae, such as for the distribution function, probability density, moments etc. Relationships to other distributions are defined, means of estimating the parameters provided, and ways of generating random numbers from the distribution are indicated.
Graphs of the distributions are shown with varying parameter values in most cases.

The book should be seen purely as a handbook on statistical distributions, not as a theoretical reference. The book is ideal for those who make use of statistical distributions in other fields, and who are not necessarily statisticians themselves. I have no formal statistics training, but use distributions extensively in my own work, and found this book very easy to understand. I have been using Johnson and Kotz monographs fairly extensively as references for the distributions in which I am interested, but find this book a much simpler reference for the basic facts of the distributions. In addition, its consistent use of notation across the chapters makes it much easier for the reader to cross reference.

I refrain from giving 5 stars to the book because of a few weaknesses, primarily omissions. Firstly, as an earlier reviewer pointed out, the lack of an index is a little annoying sometimes. Secondly, the bibliography is very slim, and so the reader interested in finding further details, proofs etc., is given very little direction. Thirdly, there are a few obvious omissions, such as the cumulative distribution function for the chi-squared distribution. Fourthly, random number generation is described only when the generation is relatively simple (for example, a method for generating random variates from a gamma distribution is described only for special cases). Finally, I would like to have seen more guidance provided in the sections on parameter estimation, such as first and second derivatives of log-likelihood functions when the estimates have to be derived iteratively.

This is the most thorough reference on distributions that I have found. The information contained about each distribution is concisely stated in a few pages - you would probably have to look in several books to get the same material. Most useful to people writing digital simulations is instructions on how to generate the distribution using random number generators. This is especially useful if you don't have access to statistical software packages. Lack of an index detracts, but is minor. Listings are alphabetical, by distribution name, so you might have to page through the book to find one that is not in an obvious location (like continuous uniform is listed as "rectangular", but discrete uniform is listed as "discrete uniform"). You need to be familiar with basic statistics to understand the book; but you don't have to be a statistician.

This is a very good reference for univariate statistical distributions. It provides maximum likelihood and moment estimation formulas for many distributions i'ven't seen anywhere else. That's right that the price is quite high for the number of pages but this information is invaluable for practitioners who need to fit distributions to real data.

The book covers most of the Statistics. The problems are very well explained and the explanation covers the theory too. The book does not cover sufficiently neither Regressions nor Anovas. Times Series and BioStatistics are covered at a high school level. BootStrap - Monte Carlo, is not even mentioned and Stratified Samples are almost not touched

Nevertheless this is a valuable book that will deepen the statistical knowledge of the reader. I would recommend it to any one that has done some intermediate statistics and wants to consolidate his knowledge. Graduate students will not be helped much from it.

I wish REA publishes a second volume with the chapters that are not covered here.

This book was very helpful to me during the beggining of my graduate course of Statistics I. It does help with most of the basic stuff but it does not contains many good examples of ANOVAS, Latin Squares, Randomized Complete Block Design, Blocking and other advanced topics (It doesn't contains any exercises using Tukey, LSD, Dunnet etc). Anyway, it is still a good book and it's still a good buy.

One of the best investments anyone in a statistics course can ever make. I was lost in a graduate level statistics course and this book not only pulled me through but I finished with flying colors. These books start with the simplest problems and explain their solution each step of the way. You stop doing problems for a particular subject when the problem solver gets to a level they're not covering in class. I found the class text useless. You will not end up doing more work. Well worth the expense.

consistence,stability and convergence of multistep numerical methods for ordinary differential equations.

This is a mathematics book, not a cookbook. It's worth buying this classic just to read the hidden sentence formed by the first letter of each sentence in their preface.

This is the classic work that explains in detail why numerical methods perform well or poorly. It's the best book I've ever read on Numerical Analysis. Great problems too! From Numerical Linear Algebra to PDE's the basic theory is explained beautifully. If you've ever wondered why iterating the corrector in the predictor-corrector method for solving ODE's doesn't do any good this is the book for you. As an inexpensive Dover paperback this is a real bargain!