Sveshnikov gives a very brief introduction to each topic, with definitions and formulae presented without proofs, followed by a couple of worked examples, and a couple pages of problems. Answers to all problems are in the back, making it ideal for self-study.
If you have not studied any statistics before, you might want to start with a book that gives more explanation, such as Bulmer, "Principles of Statistics."
This book makes use of calculus in many sections. For interesting probability problems with less advanced math, you might try Mosteller, "Fifty Challenging Problems in Probability."
If you take the time to work through a number of problems from each chapter, you can learn how to apply probability and statistics to a wide variety of problems. The equations I derived for the thesis I'm working on are the topic of chapter 6!

Instructors and students alike will find here a real treasure of exercises in probability and statistics. A broad range of topics is covered. Solutions for typical examples are provided at the start of each section. Apart from the more than 1000 problems (the answers and solutions to all of which are provided at the back), the book contains problems both simple and challenging. I personally found some of the latter most fascinating.

This is one of the best books around for studying Random Processes ! The author has also provided a very good introduction to Detection, Estimation and Modelling of Stochastic Processes. I found this book very useful and I'd strongly suggest this book for an introductionary level graduate course. If you want to build a strong foundation in Random Signal Theory, this book is the way to go.. Other advanced texts like Simon Haykin's "Adaptive Filter Theory" will be a lot easier to understand once you study this book thoroughly and work out the exercise problems.

The authors have unleashed the subject of Stochastic Processes using a carefully paced and proven approach of introducing the material using a number of elegant examples where emphasis is to generalize specific results. This quality text will certainly not dissapoint readers who have come to expect high quality from K. Sam Shanmugam and Authur .M.Breipohl .It can be recommended for first course and also for gaduate level courses on the fascinating and challenging subject of Random Processes

The book brings together two of my passions : random
walks and electric networks. It turns out that there are
interesting relationships between these two areas, so insights
in one can be used to prove things in the other.

There is this beautiful theorem by Polya which states that a
random walker on an infinite street network in d-dimensional
space is bound to return to the starting point when d = 2,
but has a positive probability of escaping to infinity without
returning to the starting point when d >= 3. The book
reinterprets this theorem as a statement about electric networks,
and then proves the theorem using techniques from classical
network theory. The proof relies on showing that the resistance
of the corresponding electric network in 1 and 2 dimensions
is infinite, whereas it is finite in the 3 dimensional case.
Thus some current [like our random walker] can flow to infinity.

Strongly recommended!.

This book provides fascinating insights and analogies between random walks and electric networks- and how you can exploit these analogies to solve difficult problems in one using the other... there's also a nice proof of the "Polya's theorem" using these analogies- basically Polya's theorem says that a random walk in dimensions >2 is transient, while a random walk on a plane or a line always returns to its starting point...

I ran into this book by accident (or "luck" as the author would say). I perused the section on entropy, which is one of the most misunderstood but widely used terms. The treatment was crystal clear and deep. I then wandered off to sample the rest of the book, when I realized it was for non-specialists. It has been my pet peeve that most attempts at popularization consist of simply material written for professionals with the equations deleted, making them uniformly useless to all parties concerned. This book is a refreshing exception aimed at a reader who is willing to do the requisite work and enjoy the satisfaction of really understanding the basics of probability and its applications to statistical physics. While not everything in this subject can be explained to nonexperts, the author has carefully chosen a path that I know will make sense to a diligent reader and give a true flavor for the subject and genuine sense of accomplishment at the end. All that is required is a minimum knowledge of algebra and a true desire to really understanding something, rather than a craving for the "warm and fuzzy" feeling
of having understood something profound without the requisite effort.

Of late more and more colleges have tried to expose nonscientists to quantitative reasoning. Now, such a course has to be about something. Why not Statistical Physics, which on the one hand is incredibly far reaching in its consequences and on the other, accessible with minimum of preamble. There is no longer the excuse that a suitable text does not exist. Here it is, so go ahead and use it for self-study or your class! The section on entropy alone will make it worthwhile. Finally, even if you know all the physics, this will teach you how write the most elegant prose.

Vinay Ambegaokar's "Reasoning About Luck" is a beautifully
written, highly original treatment of probability in the
physical sciences -- notable for its clarity, its wit, and its
insights. It succeeds admirably in teaching the subject to any
attentive reader, even one with no mathematics beyond high-
school algebra, and yet can be read with pleasure and profit
by professional physicists as well

I bought this book to refresh my memory of the statistics class I took in college and also to master how to use the ubiquitous Excel program for statistical analysis. I was not willing to try any other statistical analysis softwares.

This book exceeded my expectations in both regards. It explains key concepts very well, backed by real-life examples to illustrate their practical aplications using Excel. I have had an easier and more enjoyable time both reviewing my statistics and learning Excel.

This book accomplishes a great deal - it moves the reader from basic, descriptive statistics to inferential statistics quickly and logically. Each chapter is organized so you can repreat the steps using Excel functions, the Excel Data Analysis Add-in, or using traditional notation. The data are included on a disk, so you don't have to re-key. Also included are Dr. Gerbing's own templates, which include important tools that the Redmond Raiders left out.

I was fortunate enough to have had a course with Dr. Gerbing; I know that he has spent the last five or so years refining his teaching methods, so MBA students can learn AND apply statistics to their business lives. This book is truly a great accomlishment and a contribution to business people who want to use regression analysis - or just have a better understanding of the components that make for solid market research. In an age where we all spend thousands on research studies, it is more than a good idea to know what questions to ask of the syndicated research we're offered, or how to set up the framework for a proprietary study. As a marketing professional, I have found this text very useful. Now - if you can get Gerbing to market the powerpoint slides ...

This book does an excellent job of laying out the basic of risk assessment, including all of the non-mathematical aspects, such as office politics and educating others in the use of risk models. It's easy to understand, even if you don't have a strong math background. I'm an English major, so I should know!

This is a great book for someone who is starting to practice Risk Assessment, either as a consultant, or internally. It covers the practical aspects of risk assessment studies which are usually overlooked, such as working and communicating with stakeholders. The 'softer' side of risk assessment theory is covered i.e. what does all this mean in plain english - how do you communicate the results etc.

The author has a good style, and the text does not contain the usual fluff and buzzwords often found in business-oriented books. Check it out!

If you need clear explanations about robust statistics, if you need ideas to perform robust regression, or if you need some ground to develop robust algorithms, all you need is this text, and only this text. It covers theoretical as well as practical aspects of robust statistics. If you need more modern theoretical materials on robust statistics, Rieder's Asymptotic Robust Statistics is the companion text.

In the 1970s Peter Huber was one of the innovative geniuses that developed the area of robust statistical methods. After the famous Princeton robustness study that Huber participated in there was a scattered set of techniques that were shown to be robust estimators of location based on simulations over wide classes of probability distributions. Huber and Hampel were the leaders at putting together some mathematical theory for robustness. This book was the first attempt to unify the mathematical ideas into a general theory. It is intended for research statisticians and is a masterpiece for the subject. There are now other good books of a more practical nature. Huber also wrote a nice monograph in the SIAM series around the same time. It is now 20 years since the publication of the book and it perhaps deserves to be recognized by republication as a Wiley Classic.

My background is information systems service delivery. I stumbled across this gem when I was searching for resources on how to structure outage investigations for applications that did not meet service level objectives.

Prior to reading this book I viewed root cause analysis as a reactive tool to be used to investigate the causes of failures. The approach taken by the author is to also use this technique as a proactive tool in a manner similar to failure mode effects and criticality analysis (FMECA).

Here is a summary of what how this book approaches root cause analysis: it begins with a section on defining problems and collecting data. This approach allows you to take a proactive approach, especially if you are in an environment that uses statistical process controls to measure process performance. This is augmented by a chapters on task analysis and event and casual factor charting. This is where the author's approach begins to resemble FMECA techniques, which are proactive versus reactive methods of addressing problems and risks. This is followed by chapters on interviewing and reporting.

I particularily liked the chapter on interviewing techniques, which added a practical dimension root cause analysis. I also liked the way the author used hints throughout the book to reinforce methods.

The rest of the brief 135-page book is devoted to forms, worksheets and checklists that significantly add to the value or the book. I would have liked an accompanying diskette with this material in electronic format, but the lack of it does not detract from the book in any way.

If you want to use root cause analysis as a proactive tool in conntection with a continuous improvement initiative I recommend that you also consider Understanding Variation by Donald Wheeler. If you are seeking a more proactive approach to preventing problems in the first place you might consider augmenting Root Cause Analysis Handbook with Failure Mode and Effect Analysis by D. H. Stamatis.

Provides a clear, step by step methodology, with techniques, for problem or error analysis and identifying a solution. This is an exceptionally practical work offering a methodology for problem solving that is attractive; one which I subscribe to and use. Recommended.

Karlin and Taylor wrote a classic text on stochastic processes in their "A First Course in Stochastic Processes". The second edition of that text was published in 1975. This sequel came out in 1981. It is not only a second course but it is also intended as a second volume on a larger course in stochastic processes. The authors show that they are continuing from teh first course by picking up with Chapter 10 after the first book ended with Chapter 9. Many of the topics in the first book are continued in this text including Markov chains and Diffusions. Heavy emphasis is placed on point processes and their applications including Poisson and compound Poisson processes, population growth models and queueing processes.

In financial derivatives, people are generally dealing with all kinds of stochastic processes. This second course focuses on diffusion processes and prepares one with adequate knowledge to go ahead and understand how options are priced. This book itself does not touch any financial theory and will be of great use to people in genetics, mathematics and physics alike (finance also, of course). The authors give a chart of logical dependence of all the chaptors so you do not need to read every single corner if you are only interested in a specific topic. Readers are assumed to know Calculus and some basic probability theory. Knowledge of Brownian motion is not required and the authors succeded in keeping the math accessible. Although a mature senior might undertake this book, math in this book is not sloppy at all. Another thing I liked this book very much is there are so many excersices at the end of each chapter and one can check if he understands the materials or not. It's quite fair to give this book five stars.

It's good to see a subject which so benefits from advances in technology getting such excellent treatment. The interaction is simple to use and works well in front of a class on a smart board or on individual machines.

This approach is exactly right, for it gives the learner the ability to play with the concepts. Statistics has traditionally forced people to grapple with the unusual vocabulary (greek letters etc.) and connect them with the ideas, which one had to "animate" in one's mind. Active engagement depended on constructing graphs in private so you could finally get what was being talked about. In contrast, the interactive graphic illustrations in this electronic book let you see the concepts and how they work by changing them with your own hands. This increases your feeling of engagement, and speeds up your understanding of the concepts.