Joseph M. Hilbe-Modeling Count Data-Cambridge University Press (2014).pdf
Modeling Count Data is written for the practicing researcher who has a
reason to analyze and draw sound conclusions from modeling count data.
More specifically, it is written for an analyst who needs to construct a count
response model but is not sure how to proceed.
A count response model is a statistical model for which the dependent, or
response, variable is a count. A count is understood as a nonnegative discrete
integer ranging from zero to some specified greater number. This book aims
to be a clear and understandable guide to the following points:
 How to recognize the characteristics of count data
 Understanding the assumptions on which a count model is based
 Determining whether data violate these assumptions (e.g., overdispersion),
why this is so, and what can be done about it
 Selecting the most appropriate model for the data to be analyzed
 Constructing a well-fitted model
 Interpreting model parameters and associated statistics
 Predicting counts, rate ratios, and probabilities based on a model
 Evaluating the goodness-of-fit for each model discussed
I primarily use two statistical software packages to demonstrate examples of
the count models discussed in the book. First, the Stata 13 statistical package
(http://www.stata.com) is used throughout the text to display example model
output. I show both Stata code and output for most of the modeling examples.
I also provide R code (www.r-project.org) in the text that replicates, as far
as possible, the Stata output. R output is also given when helpful. There are
also times when no current Stata code exists for the modeling of a particular
procedure. In such cases, R is used
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