Applied Logistic Regression (Wiley Series in Probability and Statistics) 2nd Edition
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"...the revised text continues to provide a focused introduction to the logistic regression model and its use in methods for modelling..." (Short Book Reviews, Vol. 21, No. 2, August 2001)
"In this revised and updated edition of the popular test, the authors incorporate theoretical and computing advances from the last decade." (Journal of the American Statistical Association, September 2001)
"...an excellent book that balances many objectives well.... All statistical practitioners...can benefit from this book...Applied Logistic Regression is an ideal choice." (Technometrics, February 2002)
"...a focused introduction to the logistic regression model and its use in methods for modeling the relationship between a categorical outcome variable and a set of covariates." (Zentralblatt MATH, Vol. 967, 2001/17)
"...it remains an extremely valuable text for everyone working or teaching in fields like epidemiology..." (Statistics in Medicine, No.21, 2002)
"...The book is a classic, extremely well written, and it includes a variety of software packages and real examples...." (The Statistician, Vol. 51, No.2, 2002)
From the Back Cover
"An interesting, useful, and well-written book on logistic regression models . . . Hosmer and Lemeshow have used very little mathematics, have presented difficult concepts heuristically and through illustrative examples, and have included references."-Choice
"Well written, clearly organized, and comprehensive . . . the authors carefully walk the reader through the estimation of interpretation of coefficients from a wide variety of logistic regression models . . . their careful explication of the quantitative re-expression of coefficients from these various models is excellent."-Contemporary Sociology
"An extremely well-written book that will certainly prove an invaluable acquisition to the practicing statistician who finds other literature on analysis of discrete data hard to follow or heavily theoretical."-The Statistician
In this revised and updated edition of their popular book, David Hosmer and Stanley Lemeshow continue to provide an amazingly accessible introduction to the logistic regression model while incorporating advances of the last decade, including a variety of software packages for the analysis of data sets. Hosmer and Lemeshow extend the discussion from biostatistics and epidemiology to cutting-edge applications in data mining and machine learning, guiding readers step-by-step through the use of modeling techniques for dichotomous data in diverse fields. Ample new topics and expanded discussions of existing material are accompanied by a wealth of real-world examples-with extensive data sets available over the Internet.
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Anyone who is serious about doing logistic regression analysis should have this book.
New topics include the use of exact methods in logistic regression, logistic models for multinomial, ordinal and multiple response data. Also included is the use of logistic regression in the analysis of complex survey sampling data and for the modeling of matched studies.
The book is intended for a graduate course in logistic regression requiring the student to be familiar with linear regression and contingency tables. Similar in spirit and objectives to the first edition, this text also maintains the clarity of thought and presentation that these authors have a history of providing.
This is an important update to the first edition and is worth having on the bookshelf in any biostatistics library. I have my own personal copy and I think many others would also benefit by having it as a reference.
There are some features of logistic regression that are unfamiliar to those who have never used the technique before. This book describes the Wald statistic, pseudo r-square (designed to be something of an analogue to explained variation in multiple regression), goodness of fit measures (such as the eponymous Hosmer-Lemeshow test and chi square), logistic regression with more than two categories of the dependent variable, accuracy of predictions, and so on.
This is one of the best works on the subject, and it has helped me make sense of the results when I use statistical software featuring logistic regression. If interested in the technique, this work will be a nice resource.
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Lecture conseillée pour les praticiens du scoring