|
||||||||||||||||||
|
||||||||||||||||||
|
||||||||||||||||||
| ||||||||||||
Frequently Bought Together
Product Details
|
Product Description
Review
"...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)
Book Description
"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.
In the previous chapter we introduced the logistic regression model in the univariate context. Read the first page Browse Sample Pages
Front Cover | Copyright | Table of Contents | Excerpt | Index | Back Cover
Tag this product(What's this?)Think of a tag as a keyword or label you consider is strongly related to this product.
Tags will help all customers organize and find favorite items. |
What Other Items Do Customers Buy After Viewing This Item?
Customer Reviews
|
Share your thoughts with other customers:
|
||||||||||||||||||||||
|
Most helpful customer reviews
5.0 out of 5 stars
update of very well written and popular text,
By
This review is from: Applied Logistic Regression (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.
5.0 out of 5 stars
Should suit the needs of most, especially analysts,
By
This review is from: Applied Logistic Regression (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.
Share your thoughts with other customers: Create your own review
Most Helpful Customer Reviews on Amazon.com (beta) Amazon.com:
4.6 out of 5 stars (12 customer reviews) 28 of 28 people found the following review helpful
5.0 out of 5 stars
great applied book on logistic regression,
By Michael R. Chernick "statman31147" - Published on Amazon.com
This review is from: Applied Logistic Regression (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 - Published on Amazon.com
This review is from: Applied Logistic Regression (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 David C. Frye - Published on Amazon.com
Amazon Verified Purchase(What's this?)
This review is from: Applied Logistic Regression (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. |
|
Listmania!
Look for similar items by category
Look for similar items by subject
Feedback
|
