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Statistics, 4th Edition 4th Edition
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Renowned for its clear prose and no-nonsense emphasis on core concepts, Statistics covers fundamentals using real examples to illustrate the techniques.
The Fourth Edition has been carefully revised and updated to reflect current data.- ISBN-100393929728
- ISBN-13978-0393929720
- Edition4th
- PublisherW. W. Norton & Company
- Publication dateFebruary 13, 2007
- LanguageEnglish
- Dimensions7.4 x 1.4 x 10.4 inches
- Print length720 pages
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About the Author
Robert Pisani received his B.A. and Ph.D. at the University of California, Berkeley. His research interests include probability models of market-price behavior and the statistical valuation of financial instruments.
Roger Purves received his B.A. at the University of British Columbia and his Ph.D. at the University of California, Berkeley, where he currently teaches. His research interests are in the mathematical foundations of probability theory.
Product details
- Publisher : W. W. Norton & Company; 4th edition (February 13, 2007)
- Language : English
- Hardcover : 720 pages
- ISBN-10 : 0393929728
- ISBN-13 : 978-0393929720
- Item Weight : 2.77 pounds
- Dimensions : 7.4 x 1.4 x 10.4 inches
- Best Sellers Rank: #104,941 in Books (See Top 100 in Books)
- #16 in Statistics (Books)
- #21 in Dermatology (Books)
- #142 in Probability & Statistics (Books)
- Customer Reviews:
About the author

David A. Freedman (1938-2008) was a Professor of Statistics at the University of California, Berkeley. A distinguished mathematical statistician, he revolutionized the teaching of statistics with his undergraduate (new edition, 2007) and graduate (new edition, 2009) textbooks that emphasize clear reasoning over mere technique and that use numerous illustrations and empirical examples that are vivid, real, and up-to-date. Freedman also published widely on the application--and misapplication--of statistics in the social sciences. This major aspect of his work is synthesized in his book "Statistical Models and Causal Inference" (2009). Freedman was a member of the American Academy of Arts and Sciences and in 2003 received the National Academy of Science's John J. Carty Award for his "profound contributions to the theory and practice of statistics."
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Reviewed in the United States on October 29, 2022
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Top reviews from the United States
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First, to address the flaws. Many critical reviewers seem to bemoan the lack of formulas and mathematical notation in this book, but I haven’t found this to be a problem at all. If you want this to be a part of your course, simply type up a formula sheet with the formulas in both English and mathematical symbolism, and — voila! — problem solved. As long as I also do problems on the board and on the test and quiz keys using both English and symbols, my students have not found this to be an issue at all.
A much more serious problem is the non-standard usage of the term “standard error.” In most textbooks, SDs (standard deviations) are for formulas using the parameter σ (or p), and SEs (standard errors) are for formulas using the statistic s (or ˆp ). This is the dominant convention.
However, in the Freedman book, SDs are for lists of numbers, and SEs are for random variables. (He even has an illustration underlining this point on p. 291). Although I do see pedagogical value in this non-standard convention (it makes the students mindful of whether they’re dealing with a list of numbers, or with a chance process) I have to agree with the critics that this could be very confusing to students who go on to more advanced courses. I deal with this issue by making it clear to the students that this usage is non-standard.
The reason that I love this book — in spite of it’s flaws — is two-fold. First the quality of the problem-sets is at a deeper level than is typical in most introductory statistics books. Secondly, I love the wonderful use of box-models to derive all of the statistical techniques. It’s akin to teaching students how to solve quadratic equations by completing the square; when they finally do learn the quadratic formula, they understand immediately where the formula came from. It’s just a formalization of what they’ve already been doing.
In so many statistics books, it’s just one big mess of magical formulas that the students need to learn to use, but why they work and where they come from remains a mystery. Moses might have brought them down from the top of Mt. Sinai, for all they know. (Yes, yes, I know that you can derive them on the board in class, but — let’s face it — it will mostly go in one ear and out the other.) The Freedman book promotes understanding at a deeper level. Indeed, by using box models, a talented student can derive the formulas for herself.
There are not many practice questions (maybe 30 questions for each chapter at most), so if you learn by constant practice this book may not be for you.
This is one of the top half-dozen texts of my entire college career.
Not only do the authors make statistics accessible and even fun, they do so in a consistently smart style that simultaneously simplifies statistical concepts while not pandering in the quality of language overall, or occasions for clever asides. While many professors will end up using modern calculators for the problems, the text bases its lessons on the use of tables (normal, t, and chi-square). I found myself following both the professor and, electively, the text for a more full understanding of "old school" methods. Each chapter has enough embedded problems (with answers at the end of the text) that the reviews and other materials provided by my professor were often redundant. I wish I had access to the answers for each chapter review questions, but that can hardly be a criticism in my "student" copy of the text.
I'll be revisiting this book long after I've ceased being a student. It has helped me have more informed attitudes about statistical products in general, which I suppose was a point of the course and the text.
Top reviews from other countries

1. The author arranged the concepts in a way which helps to build my understanding gradually. Important concepts and fundamentals get highlighted and are easy to refer.
2. The reading is effortless as the writing style is straightforward. You don't even need to have science background to understand.
3. The examples given can be easily replicated, e.g., dice tossing and drawing cards from a box. For any reader who understands better from practical works, this is great.
Perhaps the 3 points above are common in many books, but I particularly appreciate them because my university is using a textbook from the U.S. which has too many jargons and felt like ideas are being forced down my throat. There aren't much explanation on the fundamentals. I took this as a norm since many engineering textbooks are written this way as well. This book is so effortless that I can go back to it multiple times to re-cap some important basics.



