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.
Applied logistic regression
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Book Description
Editorial Reviews
Review
"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.
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Most Helpful Customer Reviews
27 of 27 people found the following review helpful:
5.0 out of 5 stars
great applied book on logistic regression,
By
This review is from: Applied logistic regression (Wiley Series in probability and statistics) (Hardcover)
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.
31 of 32 people found the following review helpful:
5.0 out of 5 stars
A valuable tool for the applied statistician.,
By A Customer
This review is from: Applied Logistic Regression (Wiley Series in Probability and Statistics - Applied Probability and Statistics Section) (Hardcover)
This book is widely considered the "bible" of logisticregression 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.
17 of 18 people found the following review helpful:
5.0 out of 5 stars
Should suit the needs of most, especially analysts,
By
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This review is from: Applied logistic regression (Wiley Series in probability and statistics) (Hardcover)
This is an excellent beginning and intermediate text on logistic regression analysis. Avoids the thorny details, but provides a wealth of references for those who are interested.Anyone who is serious about doing logistic regression analysis should have this book.
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Inside This Book
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First Sentence:
In the previous chapter we introduced the logistic regression model in the univariate context. Read the first page Key Phrases - Capitalized Phrases (CAPs): (learn more)
Variable Coeff, Estimated Logistic Probability Figure, Values Name, All Probabilities, Benign Breast Disease Study, Classification Table Based, Observed Classified, Cross-Classification of Low Birth Weight, Drug Use Total, Initial Blood Gases, Last Menstrual Period Figure, Model Constant New!
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Front Cover | Table of Contents | First Pages | Index | Back Cover | Surprise Me!
In the previous chapter we introduced the logistic regression model in the univariate context. Read the first page Key Phrases - Capitalized Phrases (CAPs): (learn more)
Variable Coeff, Estimated Logistic Probability Figure, Values Name, All Probabilities, Benign Breast Disease Study, Classification Table Based, Observed Classified, Cross-Classification of Low Birth Weight, Drug Use Total, Initial Blood Gases, Last Menstrual Period Figure, Model Constant New!
Books on Related Topics Browse Sample Pages:
Front Cover | Table of Contents | First Pages | Index | Back Cover | Surprise Me!
Citations (learn more)
This book cites 100
books:
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100
books
cite this book:
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- Design and Analysis of Clinical Experiments (Wiley Classics Library) by Joseph L. Fleiss in Back Matter (1), and Back Matter (2)
- Statistical Distributions by Merran Evans in Back Matter (1), and Back Matter (2)
- Applied Survival Analysis: Regression Modeling of Time to Event Data by David W. Hosmer Jr. in Back Matter (1), and Back Matter (2)
- Logistic Regression: A Self-Learning Text by David G. Kleinbaum on page 223, and Back Matter
- Survey Measurement and Process Quality by Lars E. Lyberg in Back Matter
See all 100 books this book cites
- An Introduction to Stata for Health Researchers by Svend Juul on 5 pages
- Multivariable Analysis: A Practical Guide for Clinicians by Mitchell H. Katz on 5 pages
- Multivariable Analysis: A Practical Guide for Clinicians by Mitchell H. Katz on 4 pages
- An Introduction to Categorical Data Analysis by Alan Agresti in Back Matter (1), and Back Matter (2)
- Discriminant Analysis and Statistical Pattern Recognition by Geoffrey J. McLachlan in Front Matter, and Back Matter
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