Logistic regression is a type of regression used when the dependant variable is binary or ordinal (e.g. when the outcome is either “dead” or “alive”). It is commonly used for predicting the probability of occurrence of an event, based on several predictor variables that may either be numerical or categorical. For example, suppose a researcher 
Introduction Effect estimation is an important task in modern research. An example is the identification of risk factors for disease and the qualification of medical treatments. Usually, researchers are interested in estimating the global, common effect. Since actual effects tend to differ across populations, estimates based on sample of a particular population seldomly generalize well. 
Zero Inflated Models and Generalized Linear Mixed Models with R.
Zuur, Saveliev, Ieno (2012).
