This book is absolutely remarkable.

It presents the material traditionally taught in the second-year statistics (but actually goes well beyond, e.g. stochastic processes) and will be of interest to all people interested to (re-)learn statistics well, either undergraduates, or advanced students of any level. Professors should also read it maybe use it in class. Students will thank them.

The author took more than 14 years to polish it, and I would bet that scholars of pedagogy will put this book as an example of the highest possible level of their discipline. I would also bet that this book will have a long and brilliant career in statistical education. On gets the feeling that the author gave the same care and energy to the elaboration of this book as people commonly give to research.

The author is also a man with a mission. In his preface, one can read with pleasure and disbelief a passionate attack on the dumbing-down of undergraduate education in Europe and America. Having taught undergrads in commerce with the "predigested pap" he is talking about, I can really relate to the frustration of the author. There is no dilution of material or dumbing down here: all the ugly details are given, which makes that book not only a pedagogical tool but also a great reference.

There is no book on the market that is so polished in both presentation and discussion, that exposes intermediate stats at such an intelligent, comprehensive level, and finally that uses the historical development to project such clarity on the actual state of the science. I would say the closest competitor to this book is the great volume "Intermediate Statistics" by Dale Poirier, which has more econometrics and which might be a bit more comprehensive on the bayesian side, but the main focus of this one is on UNDERSTANDING and SYNTHESIZING. By the way, the focus of this book is statistics, not econometrics, despite the fact that the author has written extensively in econometrics.

I wish I would be an undergrad again and re-learn statistics with this book. Nevertheless, readers of all levels will learn something from it.

Ah, and the price is right. Value for your money!

Like many, I used the 1986 big green Spanos book to learn econometrics. Simply put, this latest follows the course charted by the first book -- it first covers probability theory, and then uses this to motivate the modelling portion of the book. Unlike the 1986 book, which was terse in places, this monograph is much more comprehensive. It isn't so much that new subjects are covered (though some are), but the current treatment far surpasses the 1986 book. More graphs, to aid geometric intution, are included. More text discusses the implications of the mathematics. More useful, in short, for a graduate student trying to learn econometrics. The only book I think is in this class is DeGroot, and that book suffers from little attention paid to econometrics (though it does do the probability material quite well).

Highly, highly recommended. And one other bonus: Spanos discusses in the introduction why he refuses to water things down, and even has a quote from the mathemetician Binmore.

I own a number of econometric and statistics texts, but this is easily the most comprehensive and probably the most important stats book that I own. Not only does it cover just about every stat concept one would need for doing applied research, but it teaches the reader how to think logically and clearly about econometrics problems. It teaches the reader how to properly integrate economic theory and statistical information for conducting good research. If you want to learn applied econometrics the proper way, read this book.

A friend of mine had Carol Ash as his professor for probability theory back in the late '80s and he remembers her quite fondly. Upon looking through the book he told me this a write-up of her lecture notes. Taking probability (a notoriously difficult subject) around the same time I didn't get so lucky. The professor was decent but the book was not and I struggled with probability topics (though curiously enough, I still have it and use it as a reference--time cures all wounds :).

Much later I taught probability and statistics and drew a lot of examples, problems, and explanations for class from this book. It's quite good and very well written, worth your time if you need some extra help understanding your textbook (many of these are shockingly bad) or for self-study. One big selling point is that the problems are quite thoroughly worked out with intermediate steps explained.

Like a good tutor, the "Probability Tutoring Book" explained and clarified points that I'd learned from my instructor and textbook in introductory Probability. Ash's explanations are often very helpful, and her book is loaded with good practice problems. The section on Combinatorics is probably a little too difficult, but I finally managed to grasp the concepts by consulting and doing problems from an old, very helpful book from the library: "Probability: A First Course" by Mosteller, Rourke, and Thomas. Overall, I give Ash's "Probability Tutoring Book" a very high rating as an aid to mastering the fundamentals of probability theory.

This is a truly terrific book covering the basics of probability theory, but it goes far beyond a mere topical treatment of the subject. I was preparing to take a graduate course required for getting a PhD in statistics, and this book was an excellent way to get a leg up in the course. It is clear to me that a great deal of effort and thought was put into making this an excellent text. Thank you Dr. Ash!

This is the most easy to follow yet comprehensive book on queuing theory I have seen. I read it cover to cover - most unusual for a mathematical text book. It fully explains the concepts it develops step-by-step and gave me a full enough understanding to develop the analyses I needed for my own research on computer networks - moreso than any other reference I have used.

I am taking a course in Queuing theory this semester and I find it an invaluable companion. This book has increased my understanding of Queueing Theory and I have learnt to enhjoy the subject through this book.

Leonard Kleinrock's "Queuing Systems", is *THE* book for any person interested in Queuing Theory. However, the reader should have a working knowledge of probability theory to be able to exploit this book fully. The theory is presented in an easily comprehensible form, and derivations of results are present in good mathematical detail. This book is widely referred to, in many technical publications.

This is THE seminal text on reliability analysis. The author writes with the same crystal clarity he uses to present material at technical conferences. The exercises are carefully graded to lead the diligent reader toward steadily deepening understanding of the material. All diagrams are clear, cogent, carefully annotated, and well keyed to accompanying text. The definition and explanations of cut sets are especially good, allowing the engineer or analyst to economically reduce a complex problem to a set of smaller, more mathematically tractable problems. Also, this work does an excellent job of ramping the reader's knowledge upward from the justifiably assumes prerequisite of basic statistics learned in one introductory class presumably having a calculus prerequisite.

I have to give it to this author, he has a very high assumption of math. If you are not a math wiz but can comprehend Calculus, this book is beyond outstanding. I will give the reader the necessary math methods for achieving you reliability analysis correctly. I spent alot of time going through these calcs and doing the proofs they are with out a dought right on the money. The boo is packed full of wonderful examples and methods. Kudos to the author.

This is definitely the best text on Reliability Engineering that I've seen. Leemis really brings the material to life in a way that I have not seen replicated in the other texts that I have perused. For a first introduction to Reliability, I cannot think of a better text. The reader should have a solid foundation in mathematical statistics, however, before starting on this volume. An adequate resource for building this foundation is Larson and Marx's "Introduction to Mathematical Statistics". Especially make sure you understand the basics of maximum-likelihood, as Leemis emphasizes it in his derivations -- the more advanced stuff you'll learn about in his book, however.

As an aside, I have actually taken Leemis' class, and I can honestly say that I learned more about probability from his lectures and the text than I ever previously thought possible. Again, I highly recommend the text.

I've taught an undergraduate statistics class in the College of Arts and Sciences at the University of South Florida for a few years and found that this book works the best. Even students who tell me they have high math anxiety have enjoyed the text because it explains complex things in a simple and easy to understand way.

I have been taking and teaching statistical concepts for almost 10 years now and have never found a better, more concise intro to modern statistics. I sugggest that anyone trying to learn statistics have a copy of this valuable text.

Although this is an intro text, it is a programming text that trains the reader to comprehend basic statistical concepts. This is especially important if the users "think" they have a "math" problem. It clearly explains in a step by step fashion "how to do it".

The techniques described in this book have applications well beyond process or parts industries. I'm an engineer in mobile communications (an industry that desperately needs quality improvement) and I'm using SPC methods to eliminate choppy voice and dropped calls using feedback mechanisms between cell phones and base-stations. This book has provided me with a comprehensive framework for real-time network quality management. Don't be put off by its somewhat quaint language: this book gives real solutions to modern real-time signal processing problems.

Professor George Box is a world renowned statistician, one of the greatest applied statisticians of the 20th Century. He has made fundamental contributions to the theory and practice of statistical methods. He is a wonderful lecturer and writer and can write on many levels. He has co-authored many classic books in statistics including texts on times series analysis and control, Bayesian methods, experimental designs, response surfaces and evolutionary operation. His work in design, response surface methods, time series and evolutionary operation were first motivated by his work in the chemical industry.

The text with Jenkins on time series analysis and control is a classic and had a major impact on the application of time series methods in business and industry. The systematic approach that they advocated was quickly referred to as the Box-Jenkins methodology. Although time series forecasting applications were well accepted by industry, the portion of the text demonstrating application to feedback control systems was largely ignored.

The 1980s and 1990s saw a resurgence of application of statistical process control in manufacturing. Basic Statistical Process Control (SPC) deals with control charts that assume independent identically distributed observations when "the system is under control". This is the basic theme for the Shewhart control charts and the cumulative sum charts that are standard in basic SPC. Hahn and Tucker at General Electric (among others) recognized that the approach could be generalized to dependent stationary processes using the Box-Jenkins methodology. In this text Box and Luceno describe this methodology and the recent results in the context of monitoring for process control and the incorporation of feedback adjustments to the process when the process goes out of control. Basically, they have taken the ideas on feedback control systems from the Box and Jenkins book and found applications to the control of manufacturing processes.

This is very useful stuff and is certainly worth knowing about. However if the reader is looking for a good introduction to standard SPC methods this is not the book he/she wants.

George Box and Alberto Luceno give you the best advice you need for Engineering Process Control (EPC) and SPC theory in this 300 page book. New methods, such as Cusum, and EWMA are also explained. Enough knowledge for a practician and more if you want in detail. They don't bore you by deriving equations you care less. I am an engineer in manufacturing and know when I need to study an equation or accept it as correct. In year 2000's, SPC and EPC will be emphasized in manufacturing even more than ever. Engineering process control has benefited from SPC in an exponentially growing rate. My motto is "SPC is the blood test of the manufacturing process control - S. Can". I recommend this book to all practicians of EPC.

This book was fantastic! It outlined the basic concepts of statistics in a way that didn't completely confuse the student. I was able to pass all my tests and actually UNDERSTAND my coursework. Kudos to the author--I HIGHLY recommend this book to the beginning statistics student.

This has to be the most "gentle" book about statistics on the market. I can actually say that I now understand the basic statistical methods! Too often, statistics books give you too much information all at once, which can be confusing to beginners. This book gradually lays it out step by step and gives clear examples of almost every possible variation. Authors of statistics texts often confuse the reader in their attempts to impress them with technical language. This book is written in language understandable to beginners and proceeds at a digestable pace. Plus, it includes brief glimpses of historical information to give the reader a broader understanding of the subject. I highly recommend this as a text for introductory statistics classes or as a refresher for anyone!

This book introduced all areas of statistics with a story or an explanation that made learning statistics enjoyable. In addition, this book contained many detailed examples that I used over and over again throughtout the course as a way of ensuring myself that I truly understood the concept that I was currently being taught. Statistics: A Gentle Introduction is a book that will remian in my personal library for many years.

Listen Software Solutions: I found the book to be easy to understand and packed with alot of step by step illustrations. Using the samples and easy to follow instructions such as concepts like Least Square Approximation and Multiple Regression Model techniques, I felt comfortable in solving my trend analysis problems. The authors do not overwhelm the reader with advanced mathematical notation but instead provide an learn by example method to their teaching. After each chapter, a number of homework problems are available, for the the student to try. This is a great college textbook to learn business statistics from. I used the textbox to learn how to program MFC VC++ curve fitting alogrithms for a stock trends analysis algorithm. I must say the internet has a ton of material, however, this book provided me the clearest explanations and helped me gain the greatest understanding of my curve fitting solutions.

The text is very easy to understand. The case study is great as well as the exercises.

Great book and also "great" price

I was told that it is impossible to learn statistics and SPSS at the same time but this book proves that it can be done. It is very logical and the increasing complexity is easy to follow and the examples used in the book are easy to understand. Each chapter ends with an exercise section which helps to test your comprehension of the chapter. I have already used 4 other books and this is the best. Don't bother wasting your money and time with other books. I highly recommend it.

This is the rarest of statistics texts that combines complete accessibility (especially to "numerically challenged" students) with rigorous treatments of introductory and advanced statistical concepts. The integration of SPSS menus directly into the text is a unique and outstanding feature that allows for the seamless integration of this software package into lectures, problem sets, and exams. Practitioners will also love this book. This is THE text you want to own!!!

Jason Hecht

Assistant Professor of Finance
Ramapo College of New Jersey

`This is a text that can be strongly recommended....I would imagine this book would be useful as a set text for undergraduate modules in social statistics, or as a resource on postgraduate degrees in social science that take a quantitative focus' -
Medical Sociology News

`This book is a detailed and very practical manual introducing statistical methods for research.... In its treatment of statistics the book is accurate and has obviously been edited with great care.... For the reviewer, the main attraction of the book was the detailed explanation of principles that underlie test formulae. As a result, the mystery of statistical output is removed and the simple practicalities are laid bare. This approach, so necessary for deep learning, makes the work far more than a statistical recipe book with a DIY computing section. The author has gone to considerable lengths to assist students by including example questions with model answers, a glossary of terms, a list of key equations and a selection of statistical tables. A CD-ROM containing data files for student exercises is provided with the book and a public access Internet address is given for readers to obtain updated files.... For undergraduate and graduate students seeking to acquire an understanding of statistical methods for research, and the associated SPSS procedures, this book provides an excellent introduction' -
Journal of Advanced Nursing

Stephen Stigler is a well-known statistician and author. He is also one of the few statisticians to do intense research on historical facts related to the development of the field. He has written other fine books on the history of statistics. This book concentrates on stories in the history of statistics where statistical analyses were done that had an impact and the statisticians laid their cards on the table. Too often, even today claims are made that require statistical evidence but the evidence is lacking, or some of the assumptions are hidden. Starting with the controversy between Karl Pearson and the Cambridge economists, Stigler shows how important it is to bring out the assumptions and methods used to make the case convincing and how not to fall into subtle traps. He also points out that attribution of a method to a person does not usually go to the discoverer. He calls it Stigler's Law of Eponymy. Examples include Chebychev's inequality discovered earlier by Bienayme and the Gaussian distribution associated with Gauss but known earlier by De Moivre and Laplace. He also includes a chapter questioning whether Thomas Bayes was the discoverer of Bayes's Theorem.

Well written and thoroughly researched, this is a great reference book on aspects of the history of statistics. This book is typical of what we have learned to expect from Stigler.

Professor Stephen M. Stigler has written a very worthwhile textbook on the history of statistics, or, more accurately, the development of statistics in modern Western civilization. This book is not a strict chronology of the development of statistical science: it is more of a collection of profiles of profound, significant events that shaped the scientific community and the World at large.

Readers will be amazed by the author's knowledge and insights in this special corner of historical research, and can also look forward to a presentation of compelling stories and gripping dramas, complemented by the author's trademark wit and humour.

Given its position as one of the leading college text books in the history of statistics, this book is perhaps less accessible to a general audience compared with the recent crop of "popular science" books such as "Fermat's Enigma"; but any learned readers should nevertheless find this a highly informative and worthwhile book.

These essays examine some of the basic concepts involved in bringing statistical argument to the table, and considers the history of statistics and their methods and use. Essential to any college-level math course on the topic, Statistics on the Table probes how statisticians link statistics to social issues and daily life, providing essays which examine statistical ideas and their impact.