Libro realmente interesante, que aporta un nuevo enfoque

So many statistics books that claim to be introductions are not. This one really is. It is clear, with just the right amount of detail. Read this book first before reading other books on multiple regression.

This book takes you along the process of learning regression analysis easily. The author takes the time to show you how to solve real problems, the disc that comes with the book allows to even go deeper on the subject of study. The one who buys this book will never sell it, you'll need it all your professional life.

Nearly 20 years had passed between my graduate statistics courses and needing to do some regression analyses for my job. This book quickly refreshed my memory, taught me new insights for the first time, got me comfortable with what I was doing after a long absence from performing statistical analyses, and actually got me digging for deeper understanding beyond what I simply needed to know to get the job done! Practical, with applied examples that workers in human resources, quality, and team-based problem solving can use. I skimmed through a lot of texts at bookstores looking for something I could use - that met my needs - and found nothing. I took a chance on Applied Statistical Methods and won! I highly recommend this book for those such as myself that need a quick refresher.

This book is very good for engineering and business. The book is easy to read and to follow. I apply statistics concepts from this book in business and technical problem solving at work. This book has 11 chapters. These chapters have statistical techniques that the industry is using. The student solution manual is also a great tool to learn statistical techniques from this book. It is great!

Finally a good and thorough book on statistical methods. Most of the books I've investigated are either of the traditional mathematical style (which are strong on probability theory, weak on methods), or guides to quantitative research in social sciences (which are usually quite "lightweight" and a bit off-track for computer scientists). This book gives very good explanations on when to choose which distribution and experimental setup based on the nature of your problem.

This book is suitable for:
1. those who are using quantitative methods in their research
2. those who felt they had a good grasp on calculating probability and statistics, but never got a grasp on when to use which statistical methods and setting up statistical experiments.

Reiss systematically develops the distribution theory for order statistics. For intermediate order statistics a central limit theorem applies and for the very extreme order statistics extreme value limit distributions apply. This theory and its application to nonparametric statistics is also covered in the book "Order Statistics" by David and "The Asymptotic Theory of Extreme Order Statistics" by Galambos. Reiss departs from these books by providing a complete and rigorous treatment of the most recent research including the bootstrap.

Chapter 0 is a good introduction that puts everything in the proper context. The book was published in 1989 and the theory continues to develop. Reiss and others have published texts in the 1990s that further develop the theory and applications of extreme values.

This book has lots of information about estimating integrals all in one place. The text is very readable and has clearly explained examples throughout. I would recommend this book to anyone who does work using computers to calculate integrals.

As a graduate student in statistics, I have had plenty of opportunities to browse through the pages of this book in search of that theorem, technique, or tool that would be applicable to my own work. In many occasions I was succesful in such search. This title compiles and synthesizes a wealth of definitions, theorems, and techniques classically used in asymptotic theory of estimators. It is as much as possible a self-contained work with enough introductory material to be used independently of other references. A bit outdated now, and with minimal material on dependent random variables, this title is still a classic compendium on statistical approximation theory.

This book covers asymptotics at a level suitable for graduate students in statistics and other disciplines such as economics. It does not really treat time-series issues, but apart from that its coverage is very impressive. It covers M-estimators, efficiency, quantile estimators, semiparametrics, and much more. The writing is remarkably lucid; proofs are succinct but very readable. One feature I particularly liked was a very readable introduction to empirical process theory, which would be a good place to start before tackling the van der Vaart and Wellner's more advanced book.