資料來源:
三民書局
Applied logistic regression / David W. Hosmer, Jr., Stanley Lemeshow, Rodney X. Sturdivant.
- 作者： Hosmer, David W.
- 其他作者：
- 其他題名：
- Wiley series in probability and statistics.
- 出版： Hoboken, NJ : Wiley c2013.
- 叢書名： Wiley series in probability and statistics
- 主題： Regression analysis.
- 版本：3rd ed.
- ISBN： 9780470582473 (bound): NT3635 、 0470582472 (bound)
- URL：
電子書
- 一般註：Includes bibliographical references (p. 459-478) and index.
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讀者標籤：
- 系統號： 000251115 | 機讀編目格式
館藏資訊
"A new edition of the definitive guide to logistic regression modeling for health science and other applicationsThis thoroughly expanded Third Edition provides an easily accessible introduction to the
摘要註
"A new edition of the definitive guide to logistic regression modeling for health science and other applicationsThis thoroughly expanded Third Edition provides an easily accessible introduction to the logistic regression (LR) model and highlights the power of this model by examining the relationship between a dichotomous outcome and a set of covariables. Applied Logistic Regression, Third Edition emphasizes applications in the health sciences and handpicks topics that best suit the use of modern statistical software. The book provides readers with state-of-the-art techniques for building, interpreting, and assessing the performance of LR models. New and updated features include: A chapter on the analysis of correlated outcome data. A wealth of additional material for topics ranging from Bayesian methods to assessing model fit Rich data sets from real-world studies that demonstrate each method under discussion. Detailed examples and interpretation of the presented results as well as exercises throughout Applied Logistic Regression, Third Edition is a must-have guide for professionals and researchers who need to model nominal or ordinal scaled outcome variables in public health, medicine, and the social sciences as well as a wide range of other fields and disciplines"--Provided by publisher. "This Third Edition continues to focus on applications and interpretation of results from fitting regression models to categorical response variables"--Provided by publisher.
