Introduction to the logistic regression model -- Multiple logistic regression -- Interpretation of the fitted logistic regression model -- Model-building strategies and methods for logistic regression -- Assessing the fit of the model -- Application of logistic regression with different sampling models -- Logistic regression for matched case-control studies -- Special topics
The focus in this Second Edition is again on logistic regression models for individual level data, but aggregate or grouped data are also considered.
From the reviews of the First Edition.
Emphasizing the parallels between linear and logistic regression, Scott Menard explores logistic regression analysis and demonstrates its usefulness in analyzing dichotomous, polytomous nominal, and polytomous ordinal dependent variables. The book...
The first book to provide a unified framework for both single-level and multilevel modeling of ordinal categorical data, Applied Ordinal Logistic Regression Using Stata helps readers learn how to conduct analyses, interpret the results from ...
Rothman,K.J. and Greenland,S. (1998). Modern Epidemiology.3rd edition. LippincottRaven., Philadelphia, PA. Royston, P(2001). Flexible parametric alternatives to the Coxmodeland more. TheStata Journal, 1(1 ): 1–28. Royston, P.(2004).
'Tis better to use a logit or probit link function than to inappropriately use ordinary least squares regression with binary or categorical dependent variables . . .” —attributed to Warren Shakespeare, William's younger statistician ...
Glenview, IL: Scott, Foresman. Lipsey, M. W. (1998). Design sensitivity: Statistical power for applied experimental research. In L. Bickman and D. J. Rog (eds.), Handbook of Applied Social Research Methods. Thousand Oaks, CA: Sage.
Presenting information on logistic regression models, this work explains difficult concepts through illustrative examples. This is a solutions manual to accompany applied Logistic Regression, 2nd Edition.
Ordinal measures provide a simple and convenient way to distinguish among possible outcomes. The book provides practical guidance on using ordinal outcome models.
The book also serves as a valuable resource for professionals and researchers who utilize statistical methods for decision-making in their everyday work. Praise for the First Edition "The attention to detail is impressive.