Rating: 
Summary: A good start..
Review: If you are looking for a good (non-technical i.e. not involving higher math) introduction to statistics, this is the book for you. As a TA at Cal (Berkeley..Go Bears!), I worked for Roger Purves (one of the authors of the book) and I taught out of this book. Needless to say, I got to know the book rather well. I highly recommend it. However, if you are looking for a slightly more advanced introduction to statistics/probability, I would suggest something along the lines of Probability by Jim Pitman or Mathematical Statistics and Data Analysis by John Rice. If you are really serious about probability theory, you might want to try Statistical Inference - by G. Casella and R.L. Berger.
Rating:
Summary: great introductory texts
Review: Many introductory statistics texts suffer from one of two ailments. Either they incorporate too much mathematics for non-statisticians or they provide oversimplified and sometimes incorrect explanations. This text is excellent and is favored by many statisticians who teach the introductory service course for non-statistics majors. The book provides excellent and insightful explanations. It is written by well-known Berkeley statisticians with great theoretical and applied experience, so it is not oversimplified or inaccurate. On the other hand Friedman and his co-authors took pains to minimize the necessary mathematics. It covers all the topics one would want to include in a first course. Real examples are used throughout to illustrate the value of the methods. These include clinical trials and observational studies, telephone surveys and opinion polls and some models in genetics. Discussion of the data snooping issue is important, particularly as we move into an age where data mining is now feasible with current computing power.
Rating:
Summary: Fantastic!
Review: This book is a rare gem. You can find piles of books with Statistics symbols/equations and hard-core problems, but how many of them really teach you the meaning of what you're doing? Statistics is a kind of data-compression - you start off with with a bag of data and you extract certain "features" such as averages, standard deviations etc... ...this allows you to say general things about the entire dataset (avg/SD, etc.) or claim associations between multiple datasets with varying degrees of confidence (correlations) or even predict the value of one variable if you know the other (regressions). The dangerous thing is, if you are not careful about how you "compress" this data or about what you do or don't do with the dataset (like dealing with outliers), your conclusions may be ENTIRELY INVALID! By using specific examples, this book teaches you to look at what are you doing before you do the analysis and then how to look at your results after you do your compression (running statistical studies). I was reluctant to buy this book at the bookstore at first, but after having read the entire text, I believe this investment was money well spent. If you don't believe me, check it out in a bookstore before you buy the book. Good luck!
Rating: 
Summary: A good place to start...
Review: This book teaches statistics using diagrams and the english language, rather than mathematical notation. This approach makes basic statistics seem quite simple. I learned more from this book in a few hours than I did from an entire semester of college statistics (even though I got an "A" in the class). However, I wish the book included the formal notation associated with statistical concepts. If this notation was at least included as an appendix I would give the book 5 stars, notwithstanding some basic concepts it fails to include. In the end, I highly recommend this book for anyone who is having trouble understanding statistics. And if you really want to know statistics, use this book and a standard textbook. Then you should have Statistics 101 down cold.
Rating: 
Summary: A mediocre statistics book, lacking many basic concepts.
Review: This book was adequate, but I found myself reading other books at the same time just to understand everything. It lacks some vital information that the beginning statistics student needs. The absence of review question answers was quite disturbing, and at times frustrating. Without answers, how do you know if you're doing the problem correctly? This is not a test, so give us the answers!
Rating:
Summary: very good introduction
Review: This is an excellent introduction to the subject, especially if your math background is close to nill. If you feel confortable with elementary mathematics, (e.g. a little calculus and a little linear algebra) then the book is still very useful, especially in explaining the intuitive aspects of the field. However, even the handy companion workbook "Mathematical Methods in Statistics", which does use a little bit of mathematics, is quite elementary. Unfortunately, there are no introductory Statistics books aimed at those who know a little bit of mathematics, so this book is pretty much all there is (with the exception of the new Nolan & Speed book). Overall, I think this is a really wonderful book, and if you do have a little bit of math background, it reads like a novel. It is extremely comprehensive for an introduction, and experienced statistics students will often use this book as a reference. There could be a little more attention paid to the mathematically confortable readers (at least in the workbook: the treatment of regression for example could at least mention the linear alebra.) As well, it would be very useful to touch upon some statistical software (e.g. Splus)
Rating:
Summary: very good introduction
Review: This is an excellent introduction to the subject, especially if your math background is close to nill. If you feel confortable with elementary mathematics, (e.g. a little calculus and a little linear algebra) then the book is still very useful, especially in explaining the intuitive aspects of the field. However, even the handy companion workbook "Mathematical Methods in Statistics", which does use a little bit of mathematics, is quite elementary. Unfortunately, there are no introductory Statistics books aimed at those who know a little bit of mathematics, so this book is pretty much all there is (with the exception of the new Nolan & Speed book). Overall, I think this is a really wonderful book, and if you do have a little bit of math background, it reads like a novel. It is extremely comprehensive for an introduction, and experienced statistics students will often use this book as a reference. There could be a little more attention paid to the mathematically confortable readers (at least in the workbook: the treatment of regression for example could at least mention the linear alebra.) As well, it would be very useful to touch upon some statistical software (e.g. Splus)
Rating:
Summary: Nothing Better -Nothing even comes close
Review: This is not merely the best introductory statistics text, it is in a sense the only one. So far as I know all the others (which were inferior before) have gone "computer." Students are taught how to solve problems on the computer which means they never learn statistics at all. I have taught introductory statistics for many years and my contempt for the other texts increases year by year. Apparently publishers demand that texts be "computerized" and the authors have been too spineless to resist. I would like to add that I suspect that most instructors who have used the other texts exclusively would have a tough time with some questions which students of this book would answer with ease.
Rating:
Summary: Statistics by David Freedman
Review: Useful book. Lots of helpful examples. Don't really need any extra stats resources (like a workbook) if you buy this.
Rating:
Summary: Statistics by David Freedman
Review: Useful book. Lots of helpful examples. Don't really need any extra stats resources (like a workbook) if you buy this.
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