Logistic regression is a statistical technique for predicting the
probability of an event, given a set of predictor variables. The
procedure is more sophisticated than the linear regression procedure.
The binary logistic regression procedure empowers one to select the
predictive model for dichotomous dependent variables. It describes the
relationship between a dichotomous response variable and a set of
explanatory variables. The logistic model, as a non-linear regression
model, is a special case of generalized linear model where the
assumptions of normality and constant variance of residuals are not
satisfied. Binary response models are of major importance in the social
sciences as well as in demography since many social phenomena are
discrete or qualitative rather than quantitative in nature.