Logistic Regression Models presents an overview of the full
range of logistic models, including binary, proportional, ordered,
partially ordered, and unordered categorical response regression
procedures. Other topics discussed include panel, survey, skewed,
penalized, and exact logistic models. The text illustrates how to
apply the various models to health, environmental, physical, and
social science data.
Examples illustrate successful modeling
The text first provides basic terminology and concepts, before
explaining the foremost methods of estimation (maximum likelihood
and IRLS) appropriate for logistic models. It then presents an
in-depth discussion of related terminology and examines logistic
regression model development and interpretation of the results.
After focusing on the construction and interpretation of various
interactions, the author evaluates assumptions and goodness-of-fit
tests that can be used for model assessment. He also covers
binomial logistic regression, varieties of overdispersion, and a
number of extensions to the basic binary and binomial logistic
model. Both real and simulated data are used to explain and test
the concepts involved. The appendices give an overview of marginal
effects and discrete change as well as a 30-page tutorial on using
Stata commands related to the examples used in the text. Stata is
used for most examples while R is provided at the end of the
chapters to replicate examples in the text.
Apply the models to your own data
Data files for examples and questions used in the text as well as
code for user-authored commands are provided on the book's website,
formatted in Stata, R, Excel, SAS, SPSS, and Limdep. See Professor
Hilbe discuss the book.
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