Customer Reviews

Probability and Statistics for Engineering and the Sciences

5 star:		(3)
4 star:		(2)
3 star:		(1)
2 star:		(4)
1 star:		(4)

 

 

Given any particular university level textbook, there's always a compromise between the author's need to write a professional text and concurrently make it easy to understand. This textbook is no exception, and it leans toward the professional aspect. It is therefore good reference or supplementary material for those who already have some background in statistics, but it is very difficult to learn from the text alone.

As for the book itself, it has strengths and weaknesses. On the good side, it has excellent examples that use real world data, albeit largely from esoteric sources. You can see right off that knowing the material will be very useful in real-world applications, which isn't something you get from many other textbooks.

If you happen to be interested in statistical theory, this book has everything you'd want to know and more. Some of the details get pretty gory, but if you like that sort of thing, it's all here. At the same time, the text is organized so you can easily skip those parts if it's not your ballgame.

The layout and organization, in general, are well thought out and implemented. Important formulas are boxed for easy identification, and key terms are well referenced. The book size and weight is also very reasonable for a textbook. This is attained by very concise, mathematical language. Also, a useful CD is included with the text, containing all the data used in the exercises (various program formats) so you don't have to type it all in manually. All-important tables are located in the back of the book, where you can always find them. Additionally, the appendix section has answers to the odd-numbered exercises. It's not that much of a problem that you only have half the solutions, because concurrent problems are usually similar.

Downsides to the text include the language, which is highly technical, relying heavily on symbols, terminology, and acronyms. Of course, statistics in general is like this, but this book really forces you to learn this rather distasteful aspect of the field. Anyway, it could certainly be more user-friendly, although as such it might be less concise. For those who are well accustomed to such things (e.g. statisticians, mathematicians, military people) the material might be an easy thing to pick up. For others, it can be frustrating when you have to flip back a few hundred pages to remind yourself what a particular Greek alphabet was supposed to represent. Personally, I feel a table of all the symbols with a brief description of each would have been a very welcome addition to the appendix.

Though the exercises are generally well done and challenging, I do have some issues with them. From time to time, one would refer to a problem or data set from way back in the textbook- with no executive summary. It would have been nice to be able to see what was being asked without flipping back hundreds of pages. Additionally, the answers provided in the appendix are often nearly useless, since a terse numerical answer says little about how you might arrive at it. For this purpose there is a solutions manual available, but you might be disinclined to pay for it.

The most prominent difficulty with the text- and I know it's not exclusively mine- is the simple fact that it is a professional work. If you have no knowledge of statistics beforehand, it can be an extremely difficult read. For a while I tried to browse the text before lectures, but I found that it wasn't worth the effort. It took so long to plow through to the 'moral of the story' that I ended up just using the book for review. Fortunately, I was lucky enough to have an excellent statistics professor (the author himself) and so was able to pick up the concepts by simply going to class. If, in the admittedly improbable event that you're new to statistics and are looking for an extra book to make up for a poor instructor, you might want to look at a different one. If you already know something about statistics and want a useful reference, then this is your resource.

This is the textbook used for the introductory statistics course for engineers at UC Berkeley and while it has some merits there are other textbooks that are better for the purpose:

If probability is the emphasis, Jim Pitman's Probability does the job.

If an overview is desired, then Purves' Statistics book will work.

The homework problems in Devore's book are of moderate difficulty but the explanation isn't any more detailed than that of a high school level statistics course (with the exception of continuous random variables). The book has some computer examples but does not discuss uses of computing packages like Matlab, R/S-plus, SAS, etc.

Unless the textbook is required for the class, for reference purposes try other textbooks for your dollars.

This has to be by far, the worst college math book I have encountered. The authors were so intent on providing real life examples and data they forgot to explain anything. Their descriptions are written for those who speak statistics as a second language not the average student or grad student. The text more often confuses me than helps me.
Also, It is very irritating that I cannot obtain the answers to the even problems even with the study guide. As adults, if college students want to cheat on homework assignments that's their business and they'll fail the tests on their own. But please, for honest students who need more than average assistance in statistics, the even answers would be appreciated.

This book presents many things in rather strange ways. For instance, the central concept of a probability distribution appears in a number of different "versions" which are likely to confuse beginners (distributions on the sample space, distributions of a random variable defined on the sample space, and distributions of functions of random variables (statistics) are discussed as if they were separate concepts). More than once the order in which the material is discussed is truly amazing. For example, the central limit theorem is discussed before the linearity property of the expectation is even mentioned, although the latter is an essential part of formulating the CLT. Many further examples could be listed here.

I do give the book credit for its effort of illustrating the material with real-world data sets. However, too little background (What exactly is measured? What is the context? Which results could be expected here?) is given in most cases, resulting in examples that convey little information beyond a sequence of numbers. In this sense I doubt the data sets are as instructive as the author claims them to be. I do not recommend this book.

As an Operations Research Manager I am constantly employing statistical models in my work. Having sat through numerous undergraduate and graduate classes in statistics and stochastic systems (queueing, reliability analysis, and quality control...to name a few), I find this book the most used statistical reference book in my library!

By using real data, Jay shows us how the topics apply to our everyday life. I remember this approach when I took his class (10 years ago)!

Now if Jay could write more books on stochastic models!

I would not recommend this book for anyone who actually needs to understand the concepts of probability and statistics. The notation is bad and most exercises are tedious and add little insight. Instead, I would recommend Milton and Arnold's book (which is a bit outdated, but wonderfully written) for any serious stats student. I gave this book two stars (I think I may be acting a little overgenerous) because it has a nice list of tables of distribution functions in the back and because of the abundance of exercises, some of which are worth working through.

I got this book for a grad school stats course. While the material covered thus far is relatively easy to understand, the book certainly doesn't make an effort to present it clearly. Generic rules are stated, followed by samples whose solutions require caveats that were never explained. At times problems are ambiguous or spend too much length setting the scene without getting to the meat of the problem (e.g. reading an entire paragraph of a problem before the statistical relevance becomes clear) Attention is not paid to details. Solutions to odd problems are a great help... why not even numbers? In stats, having the answer is a great help when it comes to figuring out where you went wrong in a problem or in a statistical method.

I had to use this book for an undergrad course in Prob & Stats at Univ. of Idaho. I'm in Comp.Sci and I can tell you one thing..I don't think this book is targeted for students who aren't doing a degree in Math/Stats or Physics..

Things aren't explained clearly and lack clarity. The book tries to use professional level examples and problems but forgets to realize that students from different majors may have a tough time dealing with other fields..Like for me, a CS guy, when the book had a problem on dynamics..I was basically unable to apply my Prob. & Statistics knowledge to that problem..cause i don't even know how to begin dealing with it..

I think it'll be good for reference when you've completed the course (using another book I hope)..Cause it really shouldn't be used as a First Semester College text in Stats..

The last thing I want to say is that as a CS guy..Probabilty needs more stress than statistics...i don't know about the other engineerings..

If all you're looking for is "how to plug numbers into formulas," this isn't the book for you. But if you want to understand WHY you're using a particular formula to solve a particular problem, this is probably the best introductory statistics textbook out there. Highly recommended not only for would-be statisticians, but also for students in science, engineering, medicine, finance, etc. who want to know if their results make sense.

This text has many examples, but if you look closely, there are no problem statements anywhere in those examples. As the example goes on the solve the problem, the reader finally "gets" what the problem was half way through the solution. Many say that in industry, there are no problem statements such as those found in exams, but in the "real world" the employee does have an idea of what direction he or she is headed in. The example problems, as well as the tendency to "hide" formulas in text, causes many of my classmate and I to curse the day this text was written!