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Logistic Regression: A Self-Learning Text (Anglais) Relié – 1 décembre 2005


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Book by Kleinbaum David G Klein Mitchel

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Couverture | Copyright | Table des matières | Extrait | Index | Quatrième de couverture
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31 internautes sur 32 ont trouvé ce commentaire utile 
An excellent step-by-step text 23 mars 2001
Par Mme Viallefont Anne - Publié sur Amazon.com
Format: Relié
When Kleinbaum entitles his book "a self-learning text", this is TRUE ! I'm sure anyone can learn logistic regression with this book. It is cristal-clear, very progressive, with real-data examples... If the best teachers are those who make you feel you're intelligent, certainly the author must be a good teacher... because his book is ! I do recommend it warmly to anyone who has to teach (like me) or learn logistic regression.
23 internautes sur 23 ont trouvé ce commentaire utile 
depends on what background you are coming from... 10 septembre 2004
Par trilungdoc - Publié sur Amazon.com
Format: Relié
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.
24 internautes sur 26 ont trouvé ce commentaire utile 
Good for what it is 5 janvier 2004
Par wiredweird - Publié sur Amazon.com
Format: Relié
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.
4 internautes sur 4 ont trouvé ce commentaire utile 
Excellent for applied researchers who want self-study 24 juillet 2012
Par Sitting in Seattle - Publié sur Amazon.com
Format: Relié Achat vérifié
If you want to learn about logistic regression (LR), and are an applied statistics user (especially in medical, health, or policy areas), this is the book for you. It is thorough in coverage and focuses deeply on the fundamentals: understanding applications of LR, interpreting the results, developing intuition for the procedures, and avoiding common errors.

You might wonder: what is LR good for? The answer: when you want to assess a dichotomous outcome on the basis of any kind of predictors. For example, to predict disease occurrence (0/1) on the basis of gender, behaviors, income, etc. Or to predict a behavior (0/1) on the basis of psychological scores, demographics, etc.

The book follows a "lecture plus commentary" style, where a primary didactic text is heavily annotated with sidebar comments, summaries, reviews of the material, and quizzes with answers. Overall this is a good thing and makes the book extremely well-suited for self study. However, it also makes it extremely long and moderately tedious to read at times. Unlike many stats books, however, it actually is readable.

The mathematics are held to a high school level (i.e., algebra), so it is suitable for any applied researcher or research consumer, although therefore probably not suitable for a professional statistician. Still, it is mathematically rigorous and requires to reader to work through a large number of (simple) formulas, contingency tables, and the like.

One odd omission is R: the book covers procedures for SAS, SPSS, and Stata, but not R. The authors' website appears not to be updated since the 2nd edition, and also does not cover R. That is a puzzling lacuna given the growing popularity of R in general and especially in bioinformatics. However, it is not a crucial flaw, since LR in R is not difficult and there are many examples online.

In summary: if you're an applied researcher in medicine, public health, psychology, etc., and want to learn about LR, get it.
2 internautes sur 2 ont trouvé ce commentaire utile 
Incredibly user-friendly with great use of examples 19 avril 2010
Par Hirohiko - Publié sur Amazon.com
Format: Relié
A great book makes all the difference and that's exactly what this book did for my studies and research. It provides easy to follow descriptions of the scientific concepts with interpretation using real data and stata/sas output. I've enrolled in courses for advanced logistic, GLM, and correlated data, that require other books and research monographs, but found this one, that I used for my first logistic regression the course, the most useful for those courses as well. Another hidden gem is the appendices that provide code for SAS, Stata, and SPSS--I use stata and sas. The authors survival analysis is also one of the few books I have purchased during my graduate studies.
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