*Chance in Biology* is one of the best science books I have ever read (and I have read quite a few of them). This book applies probability theory (along with other topics in math and physics) to biological phenomena. A big PLUS for this book is that the authors intentionally wrote the book to be accessible to an educated but nonspecialized audience.

I really enjoyed the authors' discussion of random walks applied to 'genetic drift' (the likelihood that offsprings' genomes will be different than their parents') and a surprising application of probability theory to elastic materials found in nature.

I also enjoyed their chapter on the probability of extreme phenomena -- which is an obviously useful topic that gets short shrift in many probability and statistics books I have seen. They even use baseball statistics in that chapter!

Another interesting part of this book was the discussion and the practice problems dealing with Bayes' Theorem. The concepts discussed in this book is something that all health care officials and lawyers should familiarize themselves with.

Some caveats about the book:

(a) The reader should be familiar with the 1st year of college calculus. While it is is possible that someone with only an understanding of algebra can get a lot out of the book, the calculus would help. I should note that you do not need to know a lot of calculus and someone who is 'mathophobic' could still get a lot out of the book.

(b) This book does not deal too much with inferential statistics. This book focuses in on probability, which is the cornerstone of statistics. However, when it does touch upon inferential statistics, it does a superb job.

(c) I wish the authors spent a little bit of time going over Markov Chains (random walks is a type of Markov Chain and the book does deal with that but without talking about MC explicitly). But that is a minor complaint.

Rounding out my praise for this book is the fact that most of the chapters have practice problems and ALL of the problems have solutions to them at the back of the book. I can't even begin to tell you how great having all of the solutions for all of the problems is for self-study/comprehension. The problems provided are no 'toy problems' either ... they are actually extremely helpful in not only testing one's grasps of the materials but also in illuminating and extending the points made in the particular chapter.

Other miscellanous positive things about *Chance in Biology*:

- a sample MATLAB program to simulate random phenomenon (in the solution to one of the practice problems)

- a chapter that deals with 'noise' .... interesting for those interested in Chaos

- authors make an excellent distinction between non-deterministic random/stochastic phenomena vs. deterministic Chaos

- many more good things!!!

Bottom-line: If you are at all interested in probability, applied math, physics, chemistry, or biology, you should buy this book.

Biology progressed from natural history to its great depth and breadth in part from the inclusion of truly quantitative ideas from math, physics, and chemistry. The mantle of greatest expositor of these ideas in recent times may have passed from Stephen Vogel (his excellent books are well worth reading) to Mark Denny. Here, with Steven Gaines, he takes us into extreme events, the limits of our ability to hear and to see, the elasticity of spider silk, and more. The math is demanding, but it should be; Denny and Gaines make no bones about it, but reward the reader. Denny's book, Air and Water, is equally a pleasure to read.

This is a great, practical book for ecological and evolutionary researchers. Dytham walks you through the choosing and using of different common statistical applications. If you can't find it in here, you probably have an advanced question that requires a mathematical answer, in which case it's time to dust off that heavy textbook. For most questions, this will save you time and frustration.

After too many books with a useless wealth of equations to leave you in a worse confusion here is finally a book to give you hints and tools to tackle the problem. Good coverage of both parametric and less known non-parametric statistics, modern approach through coverage of popular packages, keys to choose the various tests and lots of examples and hints for the experimental design. Generally a complete book which manages to cover with a sensible balance from the basics to more complicated designs for ANOVA evaluations.

Often, students and researchers need to perform multivariate statistical analyses on their data. Unfortunately, a lack of mathematical training prevents many from taking advantage of advanced techniques, in part, because books focus on the theory and neglect explaining how to perform and interpret multivariate analyses on real-life data. Computer-Aided Multivariate Analysis is a welcome exception, helping students choose the appropriate analyses for their data, carry them out and interpret the results. Only a limited knowledge of statistics is assumed and geometrical and graphical explanations are used to explain what the analyses do. However the basic model is always given and assumptions are discussed. In this edition the computer emphasis is enhanced by the inclusion of three additional statistical packages written for the personal computer. The authors also discuss data entry, database management, data screening, data transformations, as well as multivariate data analysis. This third edition contains a new chapter on log-linear analysis of multi-way frequency tables.

While this book will not teach you all you need to know about any of the techniques to be an expert, it WILL teach you enough to read research, and to learn what the various techniques do. The mathematical level is moderate, and the emphasis is on practical applications. Highly recommended for grad students in the social sciences

I foudn this book inspiring. It develops on the concept of "predispositioning", taking it from the initial development in chess, to all aspects of life. Predisposition is a concept that can be used as a guide to life, as the author presents cases in which he used it to improve his position (an analogy to chess).

The book brilliantly extrapolates from the premise of chaos theory and indeterminism. A great work!!

I find this book very intersting SPSS data anaysis. I am using this book right now in my biostatistics class with SPSS 11.5 gad. package. The book is still valid with higher version than 8-10 of SPSS. Only one point, there is no mathimatical expression howerver, the auther explained in the introduction it is not a statsitical book and recommend a special book for that. I found chapter 2 is very valuable review of all the biostatistic principles. Maybe because I am familiar with excell programing, I find syantax chapter is very concise and just provide the meaning and how to use this option. I do recommend this book for all SPSS interested student.

If you haven't used statistics since college or graduate school and if you are unfamiliar with SPSS but want an excellent introduction to SPSS, this is a must have book. It is truly a beginners book for SPSS for windows. This is not a book that explains statistical concepts, it is a book about SPSS and how to use it in the real world. For those of us that don't like thick books this is a thin volume, only about 220 pages. But Foster crams a lot of useful, practical information into his thin volumn. Get it and you won't regret it.

Sivia shows in the first part of his compact book (189 pages) very nice examples (such as the lighthouse problem, signal amplitudes in presence of background noise, etc) how the Bayesian theory works out. The kangaroo problem and monkey argument come up to explain the maximum entropy theory. Further on in the book examples are given in the area of DSP (digital signal processing) and on experimental design, added with references to Sivia's Bayesian applications in molecular spectroscopy, neutron scattering - and powder diffrication analysis. As an applied statistician within the area of hydrological engineering (flood frequency analysis), it was very fruitful to read Sivia's book to fresh up the way of thinking... I highly recommend the book to other applied statisticians!

Mathematics looks like a pile of abstract facts, axioms and theoremsto most people. It is hard to imagine that in some branches of mathematics, there are unsettled controversies about the meanings of basic notions like probability. Statistics is one of these branches, where professional researchers and lecturers can be divided into some sort of "schools of thought".

This small book of 189 pages is a tutorial introduction into statistics. It addresses senior undergraduates and research students in science and engineering. If symbols like integrals, factorials or notions like Eigenvalues do frighten you, you should first complete some courses on calculus and algebra before reading this book. Contrary to "classic" text books on statistics, this book employs the so called Bayesian understanding of probability. While the classic understanding of probability sees each probability as a long-run relative frequency, the Bayesian school sees it as a degree-of-belief (or plausibility). This may sound like a minor disagreement, but it leads to very different ways of solving problems.

Throughout the book, the author explains seven examples of increasing complexity to the reader and solves the problems. Especially in the first two chapters, he simplifies his favourite applications of probability theory in order to explain basic concepts like probability, the error-bar, correlation, and marginal distributions. Each of the graphical panels is explained in detail to make it easier to understand the intuitive meaning of concepts like the probability density function. Often, the author also mentions common misconceptions and vividly explains the consequences of such misunderstandings.

Having read this book, you will be able to employ probability theory in scientific and engineering work. For example in estimation of a parameter like a scattering angle. While these results are often very useful in practice, you should be warned that the Bayesian approach might annoy some representatives of the orthodox statistical guild.

Nevertheless, the book is a good tutorial which is worth reading.

Very cool time saving SAS macros are included in this book. I used these macros daily to generate SAS graphs, analysis, html outputs etc in SAS Learning edition. The author updates the macros frequantly and I found out about the SAS learning edition recently by visiting the books web page ...

It is a unique approch to use SAS.

This book is an excellent reference for people who are working for theses, dissertations, papers, etc. It provides readers with concepts, implementation methods and interpretations of results for a number of categories in data analyses. The contents in the book are accurate. Therefore the book can be trusted.

This book also gives SAS macros to readers. It makes readers work much more efficient.

I had the good fortune to take a short course from Bernie Silverman on density estimation just after this book came out in 1986. It is one of the clearest treatments of the subject and I found it particularly good on the coverage of optimal kernels. It is also filled with good practical examples and advice. For instance, the Old Faithful data provides an excellent example of a bimodal distribution where kernel density estimation provides a way to detect the two modes.

The author was also very perceptive in recognizing the value of projection pursuit techniques and bootstrap methods and the way density estimation techniques relate to these methods.

The book has the virtue of being clear and concise.

Quite a few books have been written since 1986, but this book is still the best. Very intuitive and very readable. It is written with a mastery of the subject and an excellent style of pedagogy. I remember of the joy and refreshness of reading this book around 1987 and it has served me well on a very important introductory of mordern statistics without having to go through tedious "math" notations and a shining example that statistics can be full of intuitive ideas and beautiful. For people unfamiliar with this book, it deals with probability density estimation using the idea of "local averages", and so it does not deal with other techniques such as splines. Also it is purely a density estimation book, and does not deal with another important problem, namely regression estimation (on which there are many other books). In summary, this book introduces the ideas and sense of "smoothing", a large (perhaps a little overblown) area of modern statistics. If you want to learn statistical smoothing, besides from Steve Marron, this one is the way to go.

This is a great book. It is not difficult to read. The style is very informal and at times actually humourous. It does not follow the definition-lemma-proof way of doing things at the expense of leaving simple definitions out, but these can be easily found somewhere else. The book contains an enormous amount of information, and the authors are clearly men of great knowledge and depth. The book is very nicely produced (from a 1st edition) by Cambridge U Press. Very clearly printed, and at a low price for the volume. I highly recommend both volumes to anyone who works in stochastic processes, or mathematical finance (assuming one wants to learn things, rather than just talk about them).

Although not an easy read, this book contains a wealth of information on diffusion, martingales and Ito calculus. Reading difficulty is comparable to Karatzas/Shreve. Mastery of topics included enables the reader to get understanding of most of the current research papers in this field.

Nowadays every spreadsheet program such as Excel or 123 come with a complete Statistics module that can crunch the numbers in an instant and give you a complete analysis of any set of numbers, leaving those who grew up with MINITAB in awe.

However, what these powerful programs won't and can't help you, is in grasping the meaning it. When to decide when to reject or accept a null hypothesis. Let alone what all that means. This is where this CD-ROM/ book set come into play.

The program provides a very comprehensive introductory course in Statistics. It has some very interesting videos on real life applications to Statistics, more than 200 self score test questions and a very comprehensive and logically structured text.

Perhaps this product won't replace a tutor, but it definitely help Students, Home Schoolers, or anybody interested in Statistics to increase their understanding of this some times obscure art. I think it is definitely a winner.

I know when I earn a score of 100% on a section, that I really do understand the material, and have not simply memorized it. I am able to apply those concepts to real-world situations, analyze data, and form conclusions. If this is what a statistician does, I'm seriously considering pursuing a career in this field. I never thought math could be "fun"...until now! This is a great product!