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on April 2, 2000
Your typical undergraduate student who is not a fan of mathematics education will find this book intimidating. But that's not really saying much.
A student who's not math-phobic will enjoy it. It's not one of those statistics texts that tries to give just the concepts and not the underlying math. This one goes for the math -- which is the foundation of the concepts. (Conceptual explanations in plain English are here too! The book is not pure math. Anyway, statistics is equal parts numbers and reasoning.)
The illustrations and diagrams are generally excellent. Each chapter ends with a large selection of questions and exercises (answers to some of these are provided at the back of the book) and a bibliography for further reading. Yes, really useful further reading -- not just academic texts, but popular science magazine articles, biographies of mathematicians, etc.
Notation and terms are boxed off within the text, to be clearly noticeable upon review of the chapter. Helpful for studying.
Sample computer output is given frequently, which is a nice bonus. Sometimes the output of popular statistics software can seem cryptic to the uninitiated. This initiates people. An appendix covers SAS and SPSS usage for each topic in the textbook.
Of course it's up to the reader (or instructor) to choose how much material to cover; you could easily just ignore the last few chapters if you don't need the advanced material. But it's here, which makes this a nice book. (You might want the advanced material SOMEday...)
There are 17 chapters running from "Sampling and Measurement", "Descriptive Statistics" and "Probability Distributions" through regression and correlation, *multiple* regression and correlation, ANOVA, and on to ANCOVA, "Model Building with Multpile Regression", "Logistic Regression", and then a single chapter at the end which briefly talks about the existence of factor analysis, structural equations, and other "Advanced Topics".
It's a well written and quite in-depth textbook. A good choice for learning about statistics; a good choice for keeping on your bookshelf.
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on September 16, 2003
I was subjected to an earlier edition when I took statistics as an undergraduate, and I've used the 2nd and 3rd editions as a lecturer and professor, and I believe there is no preferable alternative.
Agresti and Finlay are, above all, clear and accurate. Over the last decade, I've looked at several dozen alternatives, hoping to find one that's strong in the areas where this text is weak. I've been enticed by different layouts, writing styles, even overall motifs, but am always reminded of why I (and others) have relied on this text for so long.
Some alternatives are just sloppy - poor editing, excessive typographic errors, incorrect answers in the answer keys. Some others border on incompetent, confusing basic issues and not clarifying the disputes on border issues. And some, while achieving rapport through comics, comedy, or simply light humor, lose some of the subtle finesse that statistics entails.
Now, this one ain't perfect. The subtleties and disputes are side-stepped rather than highlighted. The text and layout are a bit wordy and eye-hard. And the examples are more practical than pedagogical. The data examples could be a bit sexier.
But the meat is all there, and correct, and clear. And that's what you want in a statistics textbook. You don't need something that pretends stats is inherently fun or exciting. The lecturer should convey the power of p, the coolness of coefficients, and the holy grail of "r-squared". The text book should cover the material accurately and in detail, and this one does.
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on June 14, 2008
As a doctoral student with an interest in quantitative research and evaluation methodologies, I am currently using this book for a course in regression analyses. It is a fairly easy read for those who are familiar with statistics and I would recommend it to those wanting to learn more about quantitative analyses. The examples are up-to-date compared to previous editions. There appear to be no substatial content differences between this edition and the previous editions (in my experience having read both).
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on February 25, 2011
This book has solid formulae, but
Some of the exercises weren't checked for validity.
I wasted an hour when a prediction equation contained a negative coefficient,
when it in fact should have been positive. I hate when this happens, because students often
Assume that these things are adequately proofread and mathematically sound.
For the amount students are charged for academic texts such as these, this is simply unacceptable.
Also, some of the terminology in the exercises deviates from that covered in the chapters.
Eg; strength in association suddenly becomes magnitude. You eventually infer these relationships, but
When you're in the middle of trying to complete your homework and your constantly cycling back to the chapter in order to
Verify the correct processes needed to complete the problem, the last thing you need is to have an author suddenly
Deviate from the standard (pardon the pun) terminology. I understand that these books take a lot of time and energy to produce, hence the significant cost, but my time is valuable as well and given this, the overall cost is not worth it for this book.
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on March 28, 2010
Well... the text is pretty straightforward. It markets itself particularly to the social sciences, but in reality it is nothing more than an introductory statistics text. Nonetheless, it is very clearly written and covers most of the basics you need to do any kind of research (multiple regressions, ANOVA, basic t,z, and F-tests, etc.). This is by no means a theoretical textbook: if you want to learn about distribution theory and the proofs behind the tests look elsewhere. If you want a quick introduction and reference to the different methods and tests with all the assumptions and potential pitfalls then this text is perfect.
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on January 30, 2004
I bought this book in graduate school for my statistics class, and it has continued to help me ever since in my professional career. Unlike many of the statistics books that use complex formulas to explain statistical methods, this book breaks each formula down in an easy to follow format. After explaining the concept, the authors use an example to illustrate the point, which makes things much easier to understand. Also, the Appendix which has SPSS and SAS coding for each of the statistical methods described in the book is a lifesaver!
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on May 17, 2002
Agresti and Finlay did an excellent job with this textbook. It includes most of the important topics generally covered in a two-semester introductory statistics sequence. These authors skillfully use graphs, practical examples, and a diversity of exercises relevant to the social sciences students' experience to illustrate the concepts studied. Summaries at the end of each chapter provide a clear overview of the important points to remember. Readers need no more than a firm high-school math background to understand its contents. This book, in my opinion, is an excellent choice for both undergrad and beginning grad students interested in acquiring a solid foundation on statistical methods.
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on December 11, 2014
I was required to get this for a class. Of the 4 intro stats books in my office, I have to say that this is the absolute worst. There are many better textbooks available to use for introductory stats. Do yourself a favor and avoid this incoherent mess at all costs.

Problems with the book:
1) the author took the liberty of using non conventional symbols to label variables. To give the most noticeable example, while we were once taught means using X(bar), this book delightfully confuses readers by using a Y (this is one of many examples)
2) the examples presented in the text are so ingrained in to the text that they make it impossible to sort out key concepts. While other books use examples to explain theories or introduce concepts, this text uses examples as a means of teaching and presenting everything. One cannot look back and quickly review a concept because you can't find the concept because it's buried in examples. I need a book that both explains the concept and provides the example, not both at once.
3) the chapters fail to follow a logical progression. For example, the chapter on tests comparing two samples goes from comparing independent means, to comparing proportions, to dependent means, back to independent pooled variance, and then nonparametric tests. All of this in one I coherently designed chapter.
4)practice problems are terrible.
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on February 8, 2015
I personally didn't find this textbook's explanations or presentation of material entirely intuitive, especially for anybody who hadn't already been exposed to statistics. It serves a purpose just fine, but I think some of the topics could have benefited from clearer conceptual explanations. I was assigned it in a first-year PhD class, and found myself rereading a previous textbook when I couldn't remember certain ideas.
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on September 24, 2014
I have taught for 2 semesters using this book. I was previously teaching econometrics at the PhD level and now teaching undergraduates at a leading institution.

The book is well structured and the authors have clearly made a thorough effort to structure knowledge here.

However, there are a few issues that keep being a problem for any person who actually uses data on a daily basis in research:

1. The book has a large number of formulas that need to be learnt by "heart" by the student. At some point it becomes a learning-of-recipes exercise rather than a logical deduction process. For instance the formula for OLS is given, without the derivation of the formula or even checking that the estimator is "the right one" (unbiased or consistent). I understand why you would avoid derivations at the undergrad level, but here it makes students more puzzled, not less.

2. A number of notations are just too specific to this book. Why E1 and E2 rather than TSS and SSE? the total sum of squares is universally known, so why reinvent the wheel? I don't understand this from a pedagogical perspective. Students will be puzzled to see that every other textbook uses a different notation.

3. Some of the questions/examples just don't match actual data. For instance, it is well known that the distribution of earnings does not follow a bell shaped distribution and so any data exercise will fail to find this -- but they keep on asking questions saying "assuming that the distribution of income follows a bell shaped distribution". And so we have to make sure students don't really inquire/look for the actual data. Is this a desirable feature of an introductory textbook? I don't think so.

I'm looking for alternatives.
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