Ok, I've tried to get through several statistics books: statistics without tears, statistics for the utterly confused, etc. etc. and this is the BEST beginner's book on statistics I have found (though it has the least attractive cover!). I did the exercises and didn't read ahead for answers, and really learned each lesson. Of all the books on statistics for beginners, this is the only one where I believe the author sincerely wants the reader to LEARN and not just get the $ for the book. Some of the books are simple at first and then jump to be more complicated too early. Not this one. Mr. Brightman really makes an effort to explain everything as simply as possible.

before reading this book I KNEW ABSOLUTELY NOTHING ABOUT STATISTICS .In a friendly way this book has met me with statistics .i never entered statistics courses at school.but i passed because this book is excellent for learning alone.I am writing this review because i thought Harvey Brightman is the best statistics teacher i have ever met.And I feel thankful to him. can alaybeyoglu ankara university Turkey

Dr. Urdan was a professor of mine, and he always made Stats very, very easy to understand. Good teacher, good book. Definitely worth buying.

This book is for all students who suffer any sort of statistics-anxiety and yet must take a statistics course in high school or college. Of course, it would also be of value for anyone simply interested in statistics. As college students well know, most statistics texts are typically impossible to make sense of. This book explains quite complex concepts and analyses in ways that professors are often unable to do. The examples are clean and crisp. Difficult material is brought down to earth with prose that is both user-friendly and often humorous. Well worth purchasing.

If you need to be spoon fed, this book is not for you. If you want the latest advanced theories with lots of proofs, this is not for you. If you, like me, are in between, and need a guide basic and intermediate statistics, the gives it too you straight, this book *IS* for you. If you want to know all the commonly used statistics, with consise explanation of why they work and how to use them, I doubt you could find a better source.

This is a superb 'How To' book for all basic statisticsl tests, methods. etc. Simple explanantions, lots of examples, step-by-step how-to do many statistical analyses. I used it during grad school and for 30+ years thereafter. Very useful and useable. If you are trying to buy a book to explain, use, practice statistics, then definitely buy this one!

To start off with, this is one of the least friendly looking textbooks I've ever had. Appearances are deceiving, though, as this has become one of the very few textbooks I've read cover to cover. Schork and Remington have a dry sense of humor, and as close to a light, flowing style as can be expected from a statistics textbook.

Excellent book for the biological or health related researcher with a touch of humor scattered throughout making it quite easy to read. It is comprehensive, touching on the most useful statistical techniques with excellent, concise examples and easy to understand explanations. The text is appropriate for those with little previous knowledge of statistics while refreshing and informative for those with a bit more experience. You can not go wrong with this book!

I like this book very much -- It provides an excellent introduction to the subject of stochastic processes from a physical, as opposed to a mahematical/axiomatic, poit of view. It also provides a good introduction to the very basic aspects of mathematical finance. It includes a rather detailed introduction to Levy processes, which is not easily available in introductory textbooks. I highly recommend this book for physics undergrads who wish to learn the subject. Mathematicians may not really find it to their liking.

I wish I had had this book 20 years ago when I first studied stochastic processes in school: I was very interested in them, but did not have a clue about what they were good for.

The book clarifies why we care about Levy distributions as opposed to plain old normal (Gaussian) distributions. I never understood this before. (If you're too lazy to get the book, I will just say it is because of "fat tails". In the world of finance, many people treat the Law of Large Numbers as if it were the Law of Small Numbers, and don't realize how big the samples have to get sometimes to get the effects you want.)

The book would be suitable for undergraduate math majors or graduate students in physics or finance who have enough mathematical background to follow it. It's over the head of people who can't do calculus and under the chin of graduate math students who probably would prefer something more pure and abstract.

This is a very well written text that is a particularly good reference on algorithms for the professional statistician. A nice feature is that it is concise and yet thorough. Many important problems in statistics are covered and presented through the deterministic and Monte Carlo techniques. Topics covered include the item response model, missing data and Bayesian methods. Most algorithms used to find maxima of posterior distributions can also be employed for maximizing likelihood. So those preferring classical inference can get a lot out of this book as well as the Bayesians.

The orientation is toward the Bayesian approach however, with good coverage of prior and posterior distributions, conjugate priors and Bayesian Hierarchical Models. The last chapter on Markov Chain Monte Carlo methods is mostly used for Bayesian inference.

This is a great reference source but can also be used in a graduate level course on mathematical statistics, probably as a supplemental text. There are many useful exercises in this edition. The book is fairly advanced and presupposes an introduction to mathematical statistics at the level of the text by Bickel and Doksum. It also assumes that the reader has had some introduction to Bayesian methods but only at the level of, say, Box and Tiao's text. It does not assume any knowledge of stochastic processes including Markov chains.

Convergence properties for the Markov Chain Monte Carlo algorithms (MCMC) are crucial to their success. Elements of discrete Markov chains are introduced in chapter 6 to make the algorithms understandable, but proof of convergence are avoided because they would involve a more detailed account of Markov chain theory.

Tanner provides a good list of the references that were available in 1996. The research in MCMC methods is continuing to be intense and so there are many good references that have appeared since the publication of this book. Robert and Casella (1999) provides a more detailed and more current treatment but even that book is a couple of years dated.

The EM and data augmentation algorthms are used for problems that are classified as missing data problems. The data may be missing as in a survey where particular questions are not answered by the respondents or it could be censored data as in a medical study or clinical trial. The censored data problem is illustrated by Tanner using the Stanford Heart Transplant data. Mixture models are also handled via these algorithms since the identification of the component that the observation belongs to can be viewed as missing data.

Tanner demonstrates a wide variety of techniques to handle many important problems and he illustrates them on real data. It is nice to have all of this compactly written in just 200 pages!

The book covers EM algorithm and Data Augmentation very good, and gives some info on MCMC. Very good reference book.

I am currently reading this text for college, and the only thing I can say is that the writing is done so well, that the material is fluid and comprehensible. The author has done a great job, especially for such a field as this. I truly think you won't be disappointed.

Prior to the first edition of this text I used a different text in my introductory statistics course each year because students did not like my text selections. I've used Pagano's text every year since the first edition was published in 1981 because every year the students tell me to retain this text. It is superbly written and exquisitely designed. It is the best text of its kind that I have seen in the past 25 years of teaching the subject.

The series of books are must for every statistician, period.

This book was providencial during my Master in Statistics research. It is THE reference when it comes to discrete distributions, a topic in Stats which has bloomed in the last two decades. This new edition covers some state-of-the-art topics in the field, such as mixture distributions and new families of discrete random variables. It is well written, but never verbose: it cuts to the chase and will be of great help to a practitioner in dire need of an arcane test. And finally, believe it or not, discrete random variables are FUN. The more you know about them, the more you perceive they can be applied in almost any practical situation. And you don't need much more than the contents of this book to be an expert.

The authors state that they wrote the books with two primary goals in mind. An opportunity to learn and use the statistical software package SPSS and to provide an introduction to a variety of statistical analysis procedures commonly used by graduate students, researchers and other professionals.

They have succeeded extremely well on both occasions.
The book has made a tremendous difference in the analyses of data collected for my master's thesis. The text is well written, well presented and in logical order. The examples, described in the book but also provided on a diskette, are very useful and clearly explain which procedures to use for which analysis and why. The illustrations in the book closely match the examples and the section "Interpreting SPSS --- results" is extremely useful in analyzing one's own results. One minor drawback: the text assumes total lack of computer literacy and goes to embarassing length in explaining how to open and save files. It can safely be assumed that most people using books of this kind would at least know that much.

All in all, a great book and a real time saver!

This book is very easy to read. It covers most topics you will meet in basic statistics. Very good explanations in procedures and outputs. Although you can not find any statistical formula in this book, use it with conventional statistics text books will be very benefical.

Unlike other reviewers, I did not find this book very helpful, especially considering the price I paid. The book is essentially a PowerPoint course presentation published with the notes pages as text. Unfortunately, the book is laid out so that the reader must make the connection between the text and the slide itself (they're stacked side-by-side with no separation). Often, the text discusses the material as though the instructor had a pointer in hand to make the connection -- without those visual clues the argument is hard to follow. On the other hand, if you know nothing about survival analysis and only want to run computer programs (specifically SPIDA) and read the output, I guess this book isn't bad. I'll keep looking for a good textbook.

I'm a graduate student in public health at Emory University and have had the opportunity to actually take the course in Epidemiologic Modeling with Dr. Kleinbaum. This book, as well as his self-learning text for logistic regression, are fabulous. Both books provide a good background to the methods needed to use each analytical technique. Survival Analysis: A Self-Learning Text, in particular, flows very well with good examples, diagrams, and explanations for the student who wishes to learn this technique. It also serves a great reference for those who use this analytical method.

Kleinbaum's Survival Analysis: A Self-Learning Text is an excellent nontechnical introduction to survival analysis. Survival analysis are statistical techniques that addresses the problem of how much time it takes for an event to occur. The techniques is widely used in medical research, and my interest in it comes from wanting to explore how long it will take for a person to refinance a loan. Kleinbaum explores the topic in a straightforward, and easy-to-follow manner. The topics are illustrated through numerous figures, diagrams, and analysis of real data sets. Kleinbaum uses a minimial amount of mathematics and carefully leads the reader through any math that is used. The book concentrates on the Cox Proportional Hazard model which is the most widely used technique in survival analysis. Given the introductory nature of the book one will not find materials covering other models. Someone with some mathematical knowledge, one semester of calculus, and a semester of statistics and a semester of undergraduate econometrics would get the most out of this book. If you are looking for an introduction to survival analysis this is a great place to start. I feel I have a strong foundation to start using survival analysis at my job and continue with a more technical exploration.