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Download Categorical Data Analysis, (Wiley Series in Probability and Mathematical Statistics, Applied Probability and Statistics) epub

by Alan Agresti

Categorical Data Analysis describes the most important methods, offering a unified presentation of modeling using generalized linear models and emphasizing loglinear and logit modeling techniques. Contributions of noted statisticians (Pearson, Yule, Fisher, Neyman, Cochran), whose pioneering efforts set the pace for the evolution of modern methods, are examined as well.

Special features of the book include:

Coverage of methods for repeated measurement data, which have become increasingly important in biomedical applications Prescriptions for how ordinal variables should be treated differently than nominal variables Derivations of basic asymptotic and fixed-sample-size inferential methods Discussion of exact small sample procedures More than 40 examples of analyses of "real" data sets, including: aspirin use and heart disease; job satisfaction and income; seat belt use and injuries in auto accidents; and predicting outcomes of baseball games More than 400 exercises to facilitate interpretation and application of methods

Categorical Data Analysis also contains an appendix that describes the use of computer software currently available for performing the analyses presented in the book. A comprehensive bibliography and notes and the end of each chapter round out the work, making it a complete, invaluable reference for statisticians, biostatisticians and professional researchers.

Download Categorical Data Analysis, (Wiley Series in Probability and Mathematical Statistics, Applied Probability and Statistics) epub
ISBN: 0471853011
ISBN13: 978-0471853015
Category: Science
Subcategory: Mathematics
Author: Alan Agresti
Language: English
Publisher: Wiley-Interscience; 1 edition (March 1990)
Pages: 576 pages
ePUB size: 1171 kb
FB2 size: 1770 kb
Rating: 4.8
Votes: 290
Other Formats: txt doc lit docx

This is an excellent book on categorical analysis. My advice to anyone planning to use this book is to study the first 4 chapters very carefully and very thoroughly. These chapters form the heart of the analysis, and describe the tests that follow in the most general setting. After the first 4 are done, go through chapters 5 and 6, and you will realize that logistic regression is a special case of the results you have already learned. The rest of the book is best used as a reference (in my opinion) and one can look into the relevant sections as needed.

The 2 important things reading this book has taught me to look for are (1) the role of cofounders in categorical analysis (see the example in section 2.3.2 for an illustration) and (2) effective groupings, when one of the entries in the contingency table is scarce
Really good clear book! Have to get the supplement from the web. The TA or student that did the stats code is the bomb in helping you code the problems and examples to really see what is going on.
Comprehensive coverage of topics.
A must have for any data analyst, data scientist, applied statistician.
good sales, good book
Agresti's book is what every statistics professor I've had mentions when teaching categorical data analysis, and after having it for a few weeks, I agree that it's great. Very clear chapters with useful exercises. As a second year graduate student, it's perfect for me. However, I would have purchased the book from the school bookstore if not for amazon's promised "buy it by BLANK time today and, if you choose two-day shipping, get it by BLANK" . They didn't even ship the book until the date they advertised I would be receiving it. Disappointing. And, I assume, intentionally misleading. Definitely get this book, but maybe not from amazon if you need it soon.
Please read this in addition to the other reviews! I agree with the other reviewers except on one aspect: I found the style of writing a little bit choppy at times. The author uses short sentences when a few connecting words like e.g. "because", "due to", would have made understanding a little easier. Also, examples are not integrated optimally into the text so that there seems to be a gap between abstract conceptual explanations and the examples.
check plus though i wish that all the examples weren't in sas. i use r primarily and have to write some of my own functions