Statistics:

by Roger Purves, David Freedman, Robert Pisani, et al.

Book Overview

Statistics in unusual in its emphasis on the models that underlie statistical inference. The authors make the models comprehensible and show why choosing the wrong model can lead students astray.... This description may be from another edition of this product.

• Format: Hardcover
• Language: English
• ISBN: 0393970833
• ISBN13: 9780393970838
• Release Date: January 1998
• Publisher: W. W. Norton & Company
• Length: 578 Pages
• Weight: 3.00 lbs.
• Dimensions: 1.5" x 7.4" x 10.1"

Customer Reviews

great for a first course to non-statistics majors

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.

Buy this book and you won't regret it!

I had used this book for my Introduction to Statistics class, and I think it was the best $60 I had ever spent. The authors explain all the major statistical concepts clearly and succinctly, drawing on a variety of samples and adding a touch of humor. The math in this book is more than doable; anyone with a basic grasp of algebra and a willingness tackle numbers is the only prerequisite to understanding this book. However, the authors also try to convey to the reader that there is more to statistics than just numbers. How an experiment is constructed, how polls are taken, what biases exist, and how assumptions are made are all integral parts of statistics. This book is applicable to almost every subject--political science, sociology, sciences, engineering, etc. There are enough exercises in the chapters to assist the reader in reviewing the concepts. I can't stress this enough: Buy this book and you won't regret it!

great introductory texts

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.

If mathematics scares you, get this book.

If you buy just one book on statistics, this should be it. If mathematics scares you, get this book. If you are interested in how and why statistics works, get this book. If you want to improve the way you teach statistics, get this book. For people first studying statistics, note that this book is written in ENGLISH! All formulas are written in English, not arcane mathematical symbols. For example, the formula for the arithmetic mean or average is: The average of a list of numbers equals their sum, divided by how many there are. That's it, no summation symbols and no variables with subscripts. The average is also described as: Average of a list = sum of entries divided by the number of entries The standard deviation (SD) is described: SD = square root of (average of (deviations from the average)^2 ) (A deviation from the average is just the number minus the mean for the entire set of numbers. I've used "^2" to represent "squared" or "raising to the second power".) The book is both easy and enjoyable to read. It is interesting reading and not just for statisticians. You get to read about important applications of statisitics in the real world (often including relevant historical details). There are also very well thought-out excercises that are realistic and yet can be easily computed by hand. When I first found this book, I had finished by Ph. D. and had taught statistics for a number of years. Even though this is an introductory text book, I still learned a lot! It actually explains many important concepts that are often buried in the mathematics of other books. (For example, how many students understand the concept of "regression toward the mean"?) It completely changed the way I taught statistics. Especially when you are first starting to study statistics, you don't want the mathematics to obscure the statistical concepts. I've seen far too many students being able to do much of the mathematics but not having a clue about the statistical concept behind the method. They could do the computations but wouldn't know why they were doing them or when the method was appropriate to use. The book consists of 29 chapters and covers design of experiments (comparative experiments), descriptive statistics (histograms, mean, standard deviation, normal distribution), correlation and regression, probability, chance variability (expected value and standard error), sampling (surveys, chance error), chance models (measurement error, genetics), and tests of significance (large sample tests for the mean and proportions, t-tests, and Chi-square tests).