Summaries and Customer Reviews are supplied by Amazon.com

This book is for people who want to learn probability and statistics quickly. It brings together many of the main ideas in modern statistics in one place. The book is suitable for students and researchers in statistics, computer science, data mining and machine learning.

This book covers a much wider range of topics than a typical introductory text on mathematical statistics. It includes modern topics like nonparametric curve estimation, bootstrapping and classification, topics that are usually relegated to follow-up courses. The reader is assumed to know calculus and a little linear algebra. No previous knowledge of probability and statistics is required. The text can be used at the advanced undergraduate and graduate level.

Average Customer Rating:

all of statistics in just this little book?

Customer Rating:

Wasserman wrote a book titled "All of Nonparametrics." You can see my review of that on amazon. That also was a concise treatment of the subject in a book that covered more topics than say Conover's fine book but yet in less pages. The trick was to give the basics,provide references and offer the reader a broad perspective on the topic without going through the nitty gritty details. I was impressed at the way the author achieved his goal and addressed topics like nonparametric regression and wavelets that are not normally covered in a first course in nonparametrics. Covering all of statistics in just slightly more pages seems at first an insane notion. The approach is the same as in the other book but with so much more to cover the treatment is a little less detailed and a little more concise. The reader needs to realize that the title is intentionally misleading. In both cases it is not Wasserman's intention to really cover every aspect of the subject at hand. Rather it is a carefully chosen selection of essential topics written in a concise but still very clear and lucid way. I think a more appropriate title would have been "All You Really Need to Know About Statistics That You Were Afraid to Ask." I think the author might consider such a change of title in a revised edition. I would have the same typr of title change for the Nonparametrics book as well. These books are different from the standard fare for introductory texts. But if you want a overview of the subject where the author points you in the right direction for dotting the i's and crossing the t's, this is the right book for you. For practitioners who are not statisticians this usually what they are looking for. For statisticians it is a useful reference source to go along with other texts on statistical inference.

Excellent at times, but only a summary or introduction: far from thorough

Customer Rating:

This book is essentially a summary of the major theoretical topics in statistics, at an introductory level. The focus is on theory, not on data analysis or modeling, but there are more connections to data analysis and modeling than is typical among books on the same topics. The main flaw in this book is not that it does anything poorly, but rather, that it omits a lot. The book is very balanced in its coverage of different topics, its discussion of the frequentist vs. Bayesian paradigm, etc. It mentions parametric and nonparametric inference, including hypothesis testing, point estimation, Bayesian inference, decision theory, regression, and even two different approaches to causal inference. The book also paints a fairly whole picture of how the different topics relate to each other and fit into a unified theoretical framework. Another huge strength of this book is that it always omits unnecessary technical details, including only streamlined discussions highlighting essential points. The main weakness of this book is that certain topics are only brushed upon and not adequately explained. The first two chapters are deep enough for students to get a more or less complete understanding of the important ideas (assuming they do the exercises). But, for example, the 4th chapter covering inequalities is simply a collection of equations and formulas: the text explains how to use them, but not where they come from or what their intuitive interpretation is. This problem arises throughout the book but it is most evident in chapter 4. I want to remark, however, that this problem is widespread in statistics textbooks, and this book is still less lacking in this respect than is common among typical texts. I'm not sure this book makes the best textbook. In my opinion most students would benefit from a text that offers more explanation of the meaning and driving ideas behind theory. However, I like the way this book gets to the main points quickly and omits confusing and tedious details and irrelevant tangents. This book may be good for students who are briefly studying statistics and will never take a future course. This book is useful as a very basic reference, but I think its best use is for self-study--advanced students will find it one of the quickest and best ways to get an overview of most of the fundamental topics in theoretical statistics. Honestly, I think Wasserman is an outstanding writer, and part of me wishes he would expand this book to the scale of something like Casella and Berger's "Statistical Inference", covering more material and adding more discussion of certain topics, but retaining the style of being to-the-point and omitting tedious details. I think this is one of the best books of its type out there but I refrain from giving 5 stars because I think Statistics is one area where most of the 5 star books have not yet been written.

Difficult and without solution	Customer Rating:
This book make a review about all statistics for undergradute students, but the author haven't a solution book. Many exercises are so difficult, and I feel it so hard.

Great for a quick summary of the basics	Customer Rating:
I have not read every section, but have found that it is a nice place to get a quick summary of the main results in some of the more outlying regions of statistics. I would not use it for a course because of its brevity, but I have recommended it to my class of future statisticians as a nice capsule reference book.

Extremely Good Book	Customer Rating:
The text book is small but the content is very concise as stated in the title. The good point is that it does not cover everything which may make the book hard to read and hard to follow. Instead, it just introduce the important and fundamental concepts necessary for learning statistical inference. It's good for reference purpose.