Product Description

From the reviews of the First Edition.
"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.

Product Details

• Amazon Sales Rank: #15460 in Books
• Published on: 2000-09-15
• Original language: English
• Number of items: 1
• Binding: Hardcover
• 392 pages

Review
"...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

"...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

"...it remains an extremely valuable text for everyone working or teaching in fields like epidemiology..." -- Statistics in Medicine, No.21, 2002

"...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

"This well written, organized, comprehensive, and useful book will be appreciated by graduate students and researchers." (Journal of Statistical Computation and Simulation, January 2006)

"...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 Publisher
Shows how to model a binary outcome variable from a linear regression analysis point of view. Develops the logistic regression model and describes its use in methods for modeling the relationship between a dichotomous outcome variable and a set of covariates. Following establishment of the model there is discussion of its interpretation. Several data sets are the source of the examples and the exercises, and a number of software packages are used to analyze data sets, including BMDP, EGRET, GLIM, SAS, and SYSTAT.

From the Back Cover
From the reviews of the First Edition…

"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.

Customer Reviews

great applied book on logistic regression
Hosmer and Lemeshow point to the massive growth in applications of logistic regression over a ten year period from the time of publication of the first edition of their text. They found over 1000 articles that used logistic regression during that time frame. There also have been many software advances that make it easier to apply logistic regression. The authors do their computing mostly in STATA. But they also acquaint the reader with many other useful standard packages for applying logistic regression. They also provide a web site from the publisher where data sets can be found.
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.

A valuable tool for the applied statistician.
This book is widely considered the "bible" of logistic regression analysis. It provides an accessible introduction to the theory of logistic modeling, and gives in-depth coverage of the proper use of the method, including interpretation, diagnostics, and practical considerations. Indispensable for anyone who uses logistic regression in their work.

update of very well written and popular text
Hosmer and Lemeshow point to the massive growth in applications of logistic regression over a ten year period from the time of publication of the first edition of their text. They found over 1000 articles that used logistic regression during that time frame. There also have been many software advances that make it easier to apply logistic regression. The authors do their computing mostly in STATA. But they also acquaint the reader with many other useful standard packages for applying logistic regression. They also provide a web site from the publisher where data sets can be found.

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.