In this text, author Scott Menard provides coverage of not only
the basic logistic regression model but also advanced topics found
in no other logistic regression text. The book keeps mathematical
notation to a minimum, making it accessible to those with more
limited statistics backgrounds, while including advanced topics of
interest to more statistically sophisticated readers. Not dependent
on any one software package, the book discusses limitations to
existing software packages and ways to overcome them. Key Features
Examines the logistic regression model in detailIllustrates
concepts with applied examples to help readers understand how
concepts are translated into the logistic regression modelHelps
readers make decisions about the criteria for evaluating logistic
regression models through detailed coverage of how to assess
overall models and individual predictors for categorical dependent
variablesOffers unique coverage of path analysis with logistic
regression that shows readers how to examine both direct and
indirect effects using logistic regression analysisApplies logistic
regression analysis to longitudinal panel data, helping students
understand the issues in measuring change with dichotomous,
nominal, and ordinal dependent variablesShows readers how
multilevel change models with logistic regression are different
from multilevel growth curve models for continuous interval or
ratio-scaled dependent variables
Logistic Regression is intended for courses such as Regression
and Correlation, Intermediate/Advanced Statistics, and Quantitative
Methods taught in departments throughout the behavioral, health,
mathematical, and social sciences, including applied
mathematics/statistics, biostatistics, criminology/criminal
justice, education, political science, public health/epidemiology,
psychology, and sociology."
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