Cohen does a masterful job of taking the guesswork out of statistical power estimation. This text provides procedural guidelines for determining power for many designs, and can be quite helpful in determining proper sample sizes. Not for the casual reader, but a necessary addition to any serious researchers statistical library.

Clearly, a must for every statistical library. This book is considered the authority on power analysis.

With dozens of cookie cutter SPC books out there, I was skeptical that Alwan's book would be much different. I was pleasantly surprised. The author spends a great deal of time impressing upon the reader that control charts are potentially useless when processes don't behave like textbook examples. So, there is a data analysis approach taken with time spent on dealing with issues of nonormality and autocorrelation and time patterns. This is pretty different and not found in other SPC books that I have run across.

The time-series analysis is on the elementary level, e.g., using OLS regression to fit an AR(1). The book doesn't go deep into ARIMA models.

There is an interesting discussion on p charts and c charts. Basically, the author is dealing with the issue of overdispersion though he does not say the term. This is not to my knowledge covered in other SPC books but it is clearly an issue in reality.

The chapter on CUSUMs is particularly done well and is quite comprehensive.

If there is one negative point, the book is a bit too wordy to my liking. I think he could have shorten up his prose a bit. But, for folks that are leary of statistics, the extra text might help get the point across.

Most folks will recognize Montgomery's Intro. to Stat. Quality Control as the main player in the field. Given its longer history, I don't think that will change but Alwan's book is definitely an add-on to any decent SPC collection of reference books.

This book is great! Through numerous real-world examples, it takes high-level ideas and makes them understandable. I highly recommend this book for anyone using SPC.

I have had formal graduate education in SPC from world class leaders. But this book is a must read for anyone who performs statistical analysis for quality management, especially in the health care arena. I actually covet the Hart's advice and would declare that any reader would learn much from this reading. The tidbits of knowledge will assist the quality staff in interpretation far beyond any courses that I am aware of in the U.S. This book will make you a cut above others in your field.

I found this book to be exactly what I wanted--a solid reference on process control for health care. It walked me through many key issues in statistical process control and will serve as a great reference.

This book is not for the advanced statistician by any means--but it is a good tool for use in health care by typical quality management staff.

Very easy to use. Open airy text. Well defined formulas and examples provide clear instruction on how to perform each test.

The chapters are very easy to read and understand. Each chapter includes exercises to do after each new concept is explained. Most answers to the exercises are included in the back of the book. The step-by-step instructions make concepts and exercises user friendly. A great Stats book.

...between simply regurgitating what I needed on the exams, and understanding the subject. I borrowed a friend's copy, and suddenly statistics, the Big Problem course for me, became fun.

Now I work in a field where I crunch a lot of numbers, and I could recite the ideas in this book in my sleep. I still keep a copy on hand for two reasons:

1) I'm often called on to expain my conclusions in "layman's terms" (yikes!). Jaeger's presentation is thorough and clear; I re-read his passages to remind myself how to say things clearly.

2) I also often work with people who are over-educated in some endevours, but badly under-educated in statistics. They're often using numbers to make decisions, and using them clumsily. I lend them Jaeger, with a stern warning that if I don't get it back in a timely fashion, there will be dire consequences (I give them about 2 weeks - usually enough for them to decide they need their own copy).

So, if...

1) you need to crunch numbers but think you hate statistics, or
2) you are a student, and you think you hate your statistics coursework, or
3) you're having trouble explaining things to people who think they hate statistics

get this book (there are probably lots of other reasons I've omitted).

Jaeger's book is the standard bearer to the "Statistical Revolution." That is, Statistics made fun and easy. The drudgery of laboring over ponderous Statistics books is gone!

Jaeger's book is an absolute must for those people who simply want to understand statistics; as they appear in various business and government reports, polls, and professional research papers. Jaeger proves that you don't have to fill your head with a bunch of equations and Greek letters to understand and appreciate the beauty of statistics. In reality, as Jaeger so expertly points out, Statistics is a straight forward subject that has been misused, abused, and poorly taught throughout the ages.

This book does not teach you how to compute statistics. Why should it? Any electronic spreadsheet worth its salt can calculate the most cumbersome of equations with a single key stroke! Therefore, unlike other books on statistics, this book contains absolutely no equations and a minimum of Greek letters.

This book's primary focus is on teaching the "user" of statistical information what statistics are, what they mean, and how to use and interpret them appropriately.

The first six chapters in this book focus on descriptive statistics -- such as the measures of central tendency, graphical and tabular presentation of data, disbursement of data about the mean (standard deviation and range), and other similar topics. Chapters 7 through 14 discuss inferential statistics -- such as analysis of variance and hypothesis testing. Chapter 15 addresses advanced topics, such as regression models and forecasting.

If you want to be a "producer" of statistics, then this is not the book for you -- look elsewhere! However, if you are in a position where you are presented statistical data to help you make an informed decision, then; by all means, buy this book immediately.

This book is ideal for managers in private industry and government agencies who live and die by statistical data -- such as people supervising production, training, quality control, or a method improvement / efficiency cell. This book is also good for military leaders to help them understand and discuss the statistics presented in readiness issues, mobilization topics, and unit readiness.

I strongly recommend this book. As a matter of fact, this book will make great reading to accompany the Standard Deviants tapes on statistics.

Buy this book! You will never look at statistical data quite the same way again. You will become an "abuser" of statistics worst nightmare -- someone knowledgeable enough to understand and challenge his data.

Excellent book. . . .Well worth the price!

I had an Economitrics course and to tell truth I wasn't sure I could pass it! god know's I tried reading the assigned book, consulting my instructer and reading more books on the subject but all I got was more frustration. until two weeks before the final exam I stumbled onto this book and guess what? I got an A!

I have used the 3rd edition and I think is is a very good textbook for highschool.

This is the finest introduction to inferential statistics I've come across. It's highly readable without being simplistic. Perhaps some readers will want more detail, particularly about descriptive statistics. If so, I recommend Joseph Tal's book Reading Between The Numbers: Statistical Thinking in Everyday Life.
A few minor complaints about Hinton's book: he neglected to include a chapter on the use of statistics in educational testing. Such a chapter would have included information about various types of reliability and validity, and test-item discrimination and difficulty. Also missing is a discussion about factor analysis.
But these are relatively minor criticisms. Hinton's book is still an excellent text to use before tackling more thorough (and probably less readable) sources.

This lovely book provides a consistently straightforward and easily accessible explanation of statistics. The book takes the reader through basic concepts in a de-mystifying way, providing the intuition that got lost in the mathematics when I took statistics in college. The book strikes an excellent balance between substance and simplicity in going through the basic ideas and the math behind them, providing a role model for first explanations of substantive topics. It's nice to read a book that sticks to its task of helping the reader understand a subject broadly and thoroughly without the author seeming to be trying to impress the reader with how much he or she knows.

I used this book for my MBA statistics class recently. This book is very well written in a clear way, not meant to be fancy but to provide a comprehensive coverage of most, if not all, relevant topics. I also belive that the coverage depth of each topic is adequate enough to satisfy all common business and economics applications.

This book bring us statistics in a simple way, begining with easy exercises and increasing them to hard tasks. I have tried a lot of diferent books, some of them in Portuguese, and this was the only one that is realy good enough to teach us without a tutor or professor. It brings the most important parts of statistics for Business and Economics using exercises that talk about real cases and real hypothesis testing. It was easer to learn from this book than to do it in my own language.

Very interesting at a theoretical as well as practical point of view. Covers in details all aspects of long-range and short-range dependance, be it all heuristic and more formal methods that permit to detect dependance in time series.

Not recommended for those who don't have a mathematical background (graduate) or don't want to spend too much time on the formal aspects of self-similar and related processes.

Maybe some of the chapters require a deep understanding of a particular application field where long-memory does appear but there are enough exmples so that reading it a second time enables to develop a strong intuition of what memory is when applied to time series.

Very interesting at a theoretical as well as practical point of view. Covers in details all aspects of long-range and short-range dependance, be it all heuristic and more formal methods that permit to detect dependance in time series.

Not recommended for those who don't have a mathematical background (graduate) or don't want to spend too much time on the formal aspects of self-similar and related processes.

Maybe some of the chapters require a deep understanding of a particular application field where long-memory does appear but there are enough exmples so that reading it a second time enables to develop a strong intuition of what memory is when applied to time series.

The book is up-to-date and effectively presents the basic concepts. Deals with current power and sphericity concerns. Example SPSS-keyed analyses and APA-format results sections are especially valuable for research training. Uses post-hoc Tukey HSD test. Separate nonparametric chapters. The authors know their material (not always the case with statistics texts) and they provide a solid (higher-level) introduction. Good selection of topics and fairly thorough. Perhaps a bit wordy at times. One of the best available introductions. From a long-time teacher of statistics with no connection to authors.

Jaccard and Becker's preface says it all:

"Introductory statistics, unlike content areas in the behavioral sciences, does not become dated quickly. Many of the concepts taught ten years ago are still relevant today. So why another text?

....Most introductory statistics texts fail to integrate sufficiently the subject matter of statistics with what students will encounter in the behavioral science journals. A statistics course should not only teach students basic skills for analyzing data but also make them intelligent consumers of scientific information.

....Because of the way chapters and exercises are organized in most texts, students are essentially told which statistical procedure to use on a given set of data. This state of affairs is simply unrealistic. It is just as important to teach students when to use a particular statistic and why it should be used as it is to teach them how to compute and interpret the statistic.

....A common complaint among students is that statistics is irrelevant and boring. This view is fostered, in part, by the tendency of statistic texts to use examples and exercises that are irrelevant and boring. Yet, it is possible to provide interesting applications of statistics (which this text does successfully).

....In the present book, a unifying structure is provided [(1)in contrast with other texts and (2) in order to provide students with conceptual relationships among the various stastical analyses].

....The book emphasizes a conceptual understanding of statistics [rather than the chosen outdated computational emphasis of the vast majority].

....Another unique characteristic of this text is a chapter on research methods.

....Appendixes to several chapters explain in more detail certain advanced concepts referred to in the body of the text [for advanced students].

....[The material covered is systematically thorough, allowing the professor to choose a customized curriculum and providing the student with a tremendous resource for further study and reference long after the course]." (pp. xiii-xvi)

The book triumphs as the most relevant introductory text on the market today.