Logistic Regression (2nd Edition) | 
 enlarge | Authors: David G. Kleinbaum, Mitchel Klein
Publisher: Springer
Category: Book
List Price: $94.95
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Avg. Customer Rating:  6 reviews
Sales Rank: 203992
Media: Hardcover
Edition: 2nd
Number Of Items: 1
Pages: 536
Shipping Weight (lbs): 2.1
Dimensions (in): 9.3 x 8 x 1.2
ISBN: 0387953973
Dewey Decimal Number: 610.727
EAN: 9780387953977
ASIN: 0387953973
Publication Date: October 25, 2005
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Product Description
This is the second edition of this text on logistic regression methods. As in the first edition, each chapter contains a presentation of its topic in "lecture-book" format together with objectives, an outline, key formulae, practice exercises, and a test. The "lecture-book" has a sequence of illustrations and formulae in the left column of each page and a script (i.e., text) in the right column. This format allows you to read the script in conjunction with the illustrations and formulae that highlight the main points, formulae, or examples being presented. This second edition includes five new chapters and an appendix. The new chapters are: Chapter 9. Polytomous Logistic Regression Chapter 10. Ordinal Logistic Regression Chapter 11. Logistic Regression for Correlated Data Chapter 12. GEE Examples Chapter 13. Other Approaches for Analysis of Correlated Data Chapters 9 and 10 extend logistic regression to response variables that have more than two categories. Chapters 11-13 extend logistic regression to generalized estimating equations (GEE) and other methods for analyzing correlated response data. The appendix "Computer Programs for Logistic Regression" provides descriptions and examples of computer programs for carrying out the variety of logistic regression procedures described in the main text. The software packages considered are SAS Version 8.0, SPSS Version 10.0 and STATA Version 7.0.
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| Customer Reviews: Read 1 more reviews...
 An excellent textbook January 9, 2007
1 out of 2 found this review helpful
The textbook "Logistic Regression" is an excellent textbook of this statistical method because it is complete and relatively easy. This method will never be really easy but the Authors present the logistic regression in an comprehensible way introducing progressively new terms. The provide many simple clinical examples.
Logistic Regression March 13, 2006
1 out of 2 found this review helpful
Kleinbaum has done it again. His books are so informative and easy to understand. It is worth the money.
Must have September 20, 2005
2 out of 3 found this review helpful
Simply the best logistic regression book I've seen. Concepts clearly and succinctly explained and illustrated.
A must-have for all biostatisticians.
 depends on what background you are coming from... September 10, 2004
11 out of 11 found this review helpful
I'm a physician learning about clinical research/biostatistics etc. I found that this book was extremely helpful in guiding me through basic rules, steps and theories on how to build a logistic regression model. The examples where straight forward, even for a person without a strong math background. However, I can also see that this would not be enough for a person set out to be a biostatistician, as this book would seem rather elementary. If you are a person with a so-so background in math and statistics, and are interested in learning to adequately perform statistical analyses with logistic regression, this is the book for you.
Good for what it is January 5, 2004
15 out of 15 found this review helpful
This book has a specific goal. It's aim is to give a basic competence in the use of logistic regression, related techniques, and the software that deal with them. This, it does very well. By intent, it leaves many other needs unmet.The format is 13 chapters, possibly representing the 13 or 14 weeks in a typical school term. Each chapter has a specific statement of teaching goals at the front, a summary outline of the course to date in the back, and a few pages of questions or exercises with answers. There appear to be sample data sets available, formatted for popular stats packages, but I did not figure out how they are made available. Within the main text of each chapter, every page reads like a blackboard lecture: equations on the left and narration on the right. The presentation uses a minimum of math, just a little algebra and exponentials in a few specific forms. For the aspiring tool-user, this book may be worth a semester's tuition. I can fault it only for an annoying habit of writing out in words equations that appear on the same page ("e raised to the power of the sum of products ... "). This book is NOT meant for people truly interested in the theory or practice of the exact computations. For example, its use of probability scarely mentions joint or conditional distributions. As a result, some of its formulas (e.g. p.48) come across as rote memorization, instead of natural expressions of the laws of probability. Lacking joint probability, the covariance matrix can not have meaning. It is just something produced, somehow, by an oracular computer program. The repeated phrase, "according to statisticians ..." makes it very clear that statisticians are a breed distinct from intended audience. What they do is quite alien, but somehow, sometimes leaves the student with formulas to grind through. Before you buy this book, be very clear about what you expect from it. Beginning students may get a lot from it. Readers already familiar with probability and some stats are likely to be disappointed.
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