This book is great for people who want to use Excel to analyze data using the statistical package and functions that Excel has to offer. The book reviews how to manuever in Excel and starts from the basics of statistics and proceeds to more advanced statistics such as hypothesis testing and regression. The book has great examples, and plenty of pictures and visual aides so that the reader does not have to guess as to what to do. The book does come with software so one can practice and have hands on experience.

The authors explain statistics in a plain and simple way and do not try to lose the reader with long and complex words. They are straight and to the point keeping it simple so that the reader and student learns and can go through the book and the course with more ease. If you want to use do statistics with Excel, this book is a great start.

Jaynes's approach to statistical mechanics is so simple and convincing you won't be needing ergodic theory anymore to justify ensamble avrages after you'll finish this book. after making his contribution to statistical mechanics Jaynes turns to probability theory and attacks the (usual) random variable approach, claiming a new basis for probability theory is both avaliable and needed . I recomend to start reading this book with the article from the brandis lecture and than to proceed to the article "where do we stand on maximum entropy" where jaynes wittily answers the criticism raised upon his analysis of the "dice problem" (presented in the brandis lecture). A true classic for anyone interested in statistical physics and probability theory.

Wow! Who knew Biostatistics could actually be fun? It had been a several years since I'd had any courses in this subject and my memory of it was that it was difficult and rather dry. But as soon as I popped in this CD, it was like learning it all over again. And this time it was a lot of fun!

To have the material presented the way it is in this software (ie-using really cool multimedia), makes it not only very understandable, but very interesting as well. I'd certainly say this is a must for a student or anyone else interested in Biostatistics.

I would like to say that Cogito's Electronic Companion to Business Statistics is an outstanding learning tool and I highly recommend it.

On the recommendation of a friend, I began using the software to refresh my skills in the subject before beginning my MBA. Statistics had always been a difficult subject for me and I was concerned I would really struggle when I needed to use it at the graduate level. After running through this CD just one time, I understand stats like I never have before. And the fact that this Electronic Companion focuses on Business Statistics has given me an excellent foundation of understanding for "real world" statistical examples.

The thing that sets this product apart is that it brings the subject to life in a way no textbook can. The video, graphics, animations and audio are a lot more effective than simply the one-dimensional picture and text in a book.

After my experience, I recommended it to three of my classmates and all three have told me that they are thrilled with it as well.

A definite thumbs up!

At first I thought this was a revision of his excellent book with Kennedy on statistical computing. But after browsing it I discovered it was a book on a subject that is near and dear to my "computationally intensive statistical methods". I then discovered a whole chapter on bootstrap methods, a topic of have studied, taught and written about!

I concur with the editorial reviewer on the content of the book. So I will not go into a detailed description that would just be repetitious.

The distinction that Gentle chooses to make between statistical computing and computational statistics is interesting. He sees statistical computing as methods of calculation. So statistical computing encompasses numerical analysis methods, Monte Carlo integration etc. On the other hand computational statistics involves computer-intensive methods like bootstrap, jackknife, cross-validation, permutation or randomization tests, projection pursuit, function estimation, data mining, clustering and kernel methods. But Gentle includes some other tools that are not necessarily intensive such as transformations, parametric estimation and some graphical methods.

Where would you put the EM algorithm and Markov Chain Monte Carlo? These are computational algorithms and hence I think belong under statistical computing, but they also can be computationally intensive methods especially MCMC. What does Gentle say. Well Chapter 1 is on preliminaries and he includes a section on the role of optimization in statistical inference. Here the EM algorithm is well placed as well as many other computing techniques like iteratively reweighted least squares, Lagrange multipliers and quasi-Newton methods.

The bootstrap chapter provides a self-contained introduction to the topic supported by a good choice of references. Variance estimation and the various types of bootstrap confidence intervals for parameters are discussed. Independent samples are the main topic though section 4.4 briefly describes dependency cases such as in regression analysis and time series.

The book is up-to-date and authoritative and is a very good choice for anyone interested in computer-intensive methods and its connections to statistical computing. This is the way modern statistics is moving and so is worth looking at.

Erich Lehmann is well known for his advanced statistical texts on hypothesis testing and estimation. he has also written a nice intermediate level text on nonparametric methods based on ranks. This book is another advanced text providing a thorough treatment of asymptotic (large sample theory) methods. It is very modern and includes such popular current topics as bootstrap and density estimation.

It's not easy to find good books about simulation techniques for experimental phenomena for general purpose: one must rely on area-specific texts (computational physics, computational biology, optimal control, etc.), that often lack important topics, so in introductory level as in specific procedures and possible generalizations. This book has it all: practically self-contained for those with basic notions about calculus (its chapter about probability and statistics, although brief, is enough to follow the rest of the book), it is centered on learning how to do simulation in real-life situations. His depth and detail in treating random number generation, for arbitrary probability distributions in discrete or continuous case, is matchless (it occupies a third of the book). Another differential is the thorough treatment of Monte Carlo methods, Markov models, as well as topics concerning model construction and analysis, and error estimations. All this with profuse examples from biology and statistics, and lots of exercises at the end of each chapter (many of them with commented resolutions at the end of the book), as well as a huge bibliography and a guide to FORTRAN IMSL and NAG routines for use in several simulation techniques (random number generators, Markov chains, etc.). I only regret that the few code listings are only in BASIC and MINITAB. As a physicist, I'd like to see some of these codes in Fortran or C (even because it would be coherent with the routines guide); but I can't ask too much, because people at business, engineering and biology may not agree with me in the programming language choice. Anyway, it's a remarkable and very useful book. I can't get rid of it in my work in Monte Carlo simulation. If you are interested on or work with statistical simulation in any area of science or technology, you'll certainly do well in acquiring a copy: it won't disappoint you!

The book is the first to present a compilation of results on elliptically countoured random matrices. It extends and generalizes normal theory methods to symmetric, non-normal, and possibly dependent, data. In addition to basic distribution theory, it presents results on characterizations, estimation, linear models and hypothesis testing.

This is a great book. Hacking describes the development of probability and statistics from the Renaissance to David Hume. His central questions are: What were Pascal, Huygens, Leibniz, Jacques Bernoulli, and all the others really doing? What problems were they trying to solve? What limitations were they working under? How did all this fit into other intellectual and mathematical problems of the day? How did all this affect the subsequent development of probability and statistics? Some of this clears up minor details that I had never grasped before, such as what was the problem with two dice that Pascal solved for the Chevalier de Mere. More important is the description of the intellectual implications of the development of modern probability and statistics. I had not known that the very name "probability" grew out of a profound religious and intellectual argument between the Jansenist Pascal and the Jesuits.

The book is full of historical gems. For example, the Dutch and English governments in the seventeenth century became infatuated with annuities as a way to finance theor expenses, especially wars. Most of the schemes were actuarially unsound. The early statisticians devoted a lot of energy to this problem and this led to major advances. Unfortunately the governments were not always pleased to be told they had no clothes. It all sounds terribly up to date.

In summary, this book covers material that is important not only in a histroical context but also for its relvance to many contemporary issues. It is well written and concise. If you want to know what the early probabilists were thinking about and how that affected the way we all think about uncertainty today, this is the book for you.

Likelihood inference goes back to the seminal work of R. A. Fisher beginning in the 1920s. Empirical Bayes methods originated with Robbins the the 1950s and 60s. Efron and Morris showed practical examples of its use in the mid 1970s and showed that Stein shrinkage estimators had the form of empirical Bayes estimates.

This recent workshop brought together some of the top researchers in these forms of inference. Likelihood inference, Bayesian inference and empirical Bayes inference are three different but similar forms of statistical inference. The basic difference between likelihood inference and Bayesian inference is the inclusion of a prior distribution that is multiplied by the likeihood function to get a posterior distribution for Bayesian inference. The purpose of the workshop was to explore the state of the art in each area and to try to find common ground and directions for future research. The volume includes thirteen articles by 18 authors.

This important area of research is also covered in the fine text that Tom Louis coauthored. That text is now in its second edition.