Customer Reviews

Mathematical Statistics and Data Analysis

5 star:		(4)
4 star:		(8)
3 star:		(2)
2 star:		(5)
1 star:		(13)

 

 

Teaching statistics is a tough business because it is quantitative, rigourous, and often abstract. Most importantly teaching statistics is tough because the majority of students most professors face take statistics because the need to, not because they want to. To make matters worse, they face instructors who not only grasp the theory, but enjoy it, and who are all the while empowered to deliver no more than little snippets of higher level stuff their students can apply.

Rice tries to bridge the gap between theory and application, delivering enough theory that the student understands the logical foundation of the applied aspects they may have already discovered in previous courses. In my mind, this is the central theme of Rice's text - avoiding unnecessary and often pedantic details better left to graduate majors in statistics while filling in the background material that often left students of statistics uncertain about the amount of confidence to place in their analyses. Rice's text is not for those who fear rigour and logic. His introductions to new concepts are compact, impersonal, and often followed by terse propositions, definitions and laws that build logically as the text progresses. He includes numerous examples that are similarly terse; however, he never failed my litmus test for logical works, which is a demonstrable linkage between each example and some proposition, law or definition previously introduced.

The text commences with the most basic review of probability, progressing quickly to random variables, distributions, expected values and important derived distributions like the t, F and Chi-square. Students will discover how the tests they applied in the past are related to theory. This theme culminates in the section on Survey Sampling, in which sampling estimators and their assumptions are derived.

Rice has weaknesses that deserve mention. Some of the problems are tough, and Rice's impersonal approach emphasizes concepts over technique. I spent many hours reading and re-reading sections in the text before a useful approach to a problem came to me. Sections on least squares and ANOVA are the least useful; they are too compact to achieve the goal of bridging theory and application. This material is much better covered elsewhere. The decision theory and Baesian inference section suffers similarly, but given how little exposure most stats students get to this material is nevertheless useful.

If you're interested in learning the rigourous application of statistics but not theory, then Rice isn't for you. No matter what, you mustn't be afraid of challenges; Rice is impersonal and compact and won't make any excuses for you. If you want to understand the assumptions and limitations of the applied statistics you've already been practicing, however, I recommend Rice enthusiastically. He won't explain the assumptions, but he will arm you with the knowledge to do it yourself.

This book got very mixed reviews from 1 star to 5. I am in agreement with Froese's review and give it 4 stars. Rice is trying to write a book for statistics students who are not mathematics or statistics majors without shortchanging them on the advanced topics and the theory. This can be difficult and often alienates both the beginners and those interested in advanced methods. I have tried to stay along that fine line with my texts also. So I appreciate the difficulties. As an author of a book on bootstrap methods, I also appreciate the way Rice has integrated that subject into this text.

This book is so far the best mathematical statistics and data analysis textbook I've ever read for an undergraduate intermediate level statistics course. The topics are well chosen and the book is well written. The previous bad reviews of the book at Amazon.com are from people with absolutely no knowledge of statistics and trying to find some short-cut to "prepare for a exam" or whatever. So if you are a serious reader and with intermediate level statistics understanding, go for the book. It is not only good to be used a textbook, but also excellent for reference purpose.

This book is not for the beginner. It is not for someone who doesn't already have a modicum of elementary probability as part of his 'blood and bones.' It is not for those without a functional knowledge of multivariate integration and the transformations involved therein: just as for any "mathematical statistics" textbook, mathematical competence is critical in deriving utility -- in fact, outside some set theoretic properties, a topic such as this is all about applying methods from linear algebra/multivariable calc. This is not a probability textbook in the vein of Stephen Ross (whose "first course in probability" would be best to consult for certain topics -- this text too requires calculus.) In short, look elsewhere first if you're incompetent.

But then return. Return to this wonderfully complete and rigourous txt that offers challenging end of chapter exercises and insures that if you look something up, you will find it. The contents is vast, and each time you open it, you're likely to walk away with something new or something appreciated fully for the first time.

Misses the 5th star because of its list of errata, and because newer editions haven't been forthcoming. It could use one more revision.

This is probably the best textbook on mathematical statistics for students with a good lower-division background in mathematics (calculus, multivariable calculus, linear algebra). Once the course is over, it makes for an excellent reference. I refer to it often for maximum likelihood, non-parametric tests, least squares theory, etc.

Although the title contains the word "mathematical," the book has an eminently practical orientation. There is even an entire chapter devoted to descriptive statistics and graphical tools. A course making good use of this book will give the student a solid introduction to the art of data analysis.

This book may be too advanced for students without sufficient mathematical preparation. Such students might warm up first with Friedman et al. or Moore and McCabe.

The chief difference between the second and third editions seems to be that the latter gives somewhat more space to the Bayesian approach.

As a researcher in the field of computer communications, I often have to interpret statistical analysis of different phenomena. Honestly, I slept through my statistics classes in the school, since I never could understand how the extravagant mathematics could be used in practice. Now I see how it can be used, but the underlying math is long forgotten. Rice's book is an excellent reminder for a range of topics including various distributions, hypothesis testing, linear regression, etc.

I perfectly understand that many reviewers find this book unsatisfactory. There must be books out there both wider and deeper than this. But i have no time to read them. I just want to understand what the presented Chi-square test results really mean. Practicioners, go for it!

If I can save anyone the pain of using this book I would like to do it. This was used for my undergraduate course in mathematical statistics (the first statistics course for me). Fortunately, I had an excellent professor, and was able to learn the material from him and from other texts. Rice is extraordinarily difficult to understand. There are many examples of poor English with ambiguous sentences, algebraic errors, failure to emphasize or sometimes even to explain or define essential concepts, and obtuse end-of-chapter problems. In order to learn the material, I made use of several other textbooks, including Hogg & Craig and Bain & Englehart. Both of these books, in fact EVERY one of the ten or so books I used, was far better than Rice.

Most of the reviews are rather heavy on the opposite ends of the spectrum.

I used this book for a second semester undergrad course on statistical inference/methods. So I mainly used the later chapters on estimation, testing, anova, regression.. Most of the negative reviews on Amazon gripe about how this book is not an introductory text on statistics, which I can't say much against since I myself didn't use it as an intro text. I read pitman's probability text (great book) first. However, I would still urge you to be wary of those reviews because many people, even statisticians, have trouble with the trickiness of probability - much less people diving into mathematical statistics for the first time.

The chapters in the later half of the book are really just amazing. Rice writes in very clear, albeit sometimes lengthy, terms. There are enough proofs in this book to satisfy those looking to bypass the English, and there are enough explanations and examples to give students a firm understanding of the subject. Really! The explanations are very very clear and cover a variety of situations you may face in the field when working with stats (in private industry or econometric research alike). The graphical depictions in this book are really priceless.

I have frequently referenced this book and pitman's probability. Honestly, with maybe 3-4 hours of quiet study time, I can easily review all the basic probability and statistics topics one needs to do well in any statistics class at the undergrad and the lower grad level.

The only con is that the solutions aren't worked out in the book. If you need solutions, you can go to [...]and purchase monthly subscriptions to access their user worked out answers for this book. They have maybe 2/3 of the problems worked out, with perhaps a 90% accuracy rate.

Great 2nd mathematical intro to stats book!

Rice's book is well written and statistical ideas are communicated nicely to the reader. You need to have a good mathematical background to use this book because many theorems are proved intuitively in this book. However, Rice failed to communicate ideas behind the Neyman Pearson Lemma in chapter 9. Do not be surprised if it is difficult to understand that chapter. It is also important that your basic statistics knowledge is good before attempting to use this book. For sure, your problem solving skills will improve soon as you start solving problems from this book. On average, the book is very good. I have used it to refresh my statistics knowledge when doing my MSc Engineering Mathematics degree.

This book takes a lot of time to read not because the material covered is so difficult to understand but rather because the author seems to explain in a way like telling a story. If you're an applied math student, then this book is probably for you because it emphasizes more on the applied aspects of statistics. But...(warning) if you're a PURE math stud with theoretical tendency, then this book is probably not for you. Yes... i agree in some part, the book seems to be going around and around when trying to explain sth. But no book is perfect. My suggestion: ask yourself what kind of student you are - applied or pure? If the author's style doesnt suit you, find another book. But even if you are a pure math stud., you can still appreciate the author's effort in trying to make statistics looks simple and applied (though sometimes that means some other things has to be sacrificed).