Applied Logistic Regression, 3/e (Hardcover)

David W. Hosmer Jr., Stanley Lemeshow, Rodney X. Sturdivant

  • 出版商: Wiley
  • 出版日期: 2013-04-01
  • 售價: $1,560
  • 貴賓價: 9.8$1,529
  • 語言: 英文
  • 頁數: 528
  • 裝訂: Hardcover
  • ISBN: 0470582472
  • ISBN-13: 9780470582473
  • 相關分類: 地理資訊系統 Gis

下單後立即進貨 (約5~7天)

買這商品的人也買了...

相關主題

商品描述

<內容簡介>

A new edition of the definitive guide to logistic regression modeling for health science and other applications

This 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.

<章節目錄>

1 Introduction to the Logistic Regression Model
2 The Multiple Logistic Regression Model
3 Interpretation of the Fitted Logistic Regression Model
4 Model-Building Strategies and Methods for Logistic Regression
5 Assessing the Fit of the Model
6 Application of Logistic Regression with Different Sampling Models
7 Logistic Regression for Matched Case-Control Studies
8 Logistic Regression Models for Multinomial and Ordinal Outcomes
9 Logistic Regression Models for the Analysis of Correlated Data
10 Special Topics