Applied Logistic Regression
Solutions Manual
Wiley Series in Probability and Statistics
2. Auflage Oktober 2001
288 Seiten, Softcover
Wiley & Sons Ltd
Kurzbeschreibung
The logistic regression (LR) model has been in use in statistical analyses for many years; but it was not until the late 1960's that a model was used to provide a multivariate analysis of the Framingham heart study data that its full power and applicability were totally appreciated. Since then the LR model has become the standard method for regression analysis of dichotomous data in many fields, especially in the health sciences. This new and updated edition of the classic bestseller provides a focused introduction to the LR model and its use in methods for modeling the relationship between a dichotomous outcome variable and a set of covariables.
From the reviews of the First Edition.
"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.