Applied Logistic Regression

Priekinis viršelis
John Wiley & Sons, 2013-04-01 - 528 psl.
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.

 

Pasirinkti puslapiai

Turinys

Logistic Regression Models for the Analysis of Correlated Data
9
The Multiple Logistic Regression Model
35
Interpretation of the Fitted Logistic Regression Model
49
ModelBuilding Strategies and Methods for Logistic Regression
89
Assessing the Fit of the Model
153
5
175
Application of Logistic Regression with Different Sampling
227
Logistic Regression for Matched CaseControl Studies
243
9
351
243
355
Exercises 456
368
Exercises
375
References
395
Index
419
269
431
377
475

Logistic Regression Models for Multinomial and Ordinal
269
6
313
Autorių teisės

Kiti leidimai - Peržiūrėti viską

Applied Logistic Regression
David W. Hosmer, Jr.,Stanley Lemeshow,Rodney X. Sturdivant
Ribota peržiūra - 2013

Pagrindiniai terminai ir frazės

additional adjusted analysis approach approximate assess assume assumption binary calculated Chapter choice cluster coding coefficients computed conditional confidence interval Constant continuous correlation covariate data set defined denote described dichotomous discussed distribution effect equal equation error estimated coefficients estimated probabilities example Figure final fitted model follows four fractional polynomial fracture function given groups important increase independent indicate interaction interpretation less linear log-odds logistic regression model matched matrix mean measures methods model fit multivariable normal observed obtained odds ratio outcome p-value packages parameter perform plot population average possible posterior predicted present prior probability problem RACE random effects reference residuals risk sample scale score selection shown in Table significant similar slope standard STATA statistic Step subjects suggest Table term values variable variance versus Wald weight

Apie autorių (2013)

DAVID W. HOSMER, Jr., PhD, is Professor Emeritus of Biostatistics at the School of Public Health and Health Sciences at the University of Massachusetts Amherst.

STANLEY LEMESHOW, PhD, is Professor of Biostatistics and Founding Dean of the College of Public Health at The Ohio State University, Columbus, Ohio.

RODNEY X. STURDIVANT, PhD, is Associate Professor and Founding Director of the Center for Data Analysis and Statistics at the United States Military Academy at West Point, New York.

Bibliografinė informacija

PavadinimasApplied Logistic Regression
398 tomas iš Wiley Series in Probability and Statistics
AutoriaiDavid W. Hosmer, Jr., Stanley Lemeshow, Rodney X. Sturdivant
Leidimas3, iliustruotas
LeidėjasJohn Wiley & Sons, 2013
ISBN0470582472, 9780470582473
Ilgis528 psl.
  
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