We introduce a new class of distributions called the
generalized odd generalized exponential family. Some of its
mathematical properties including explicit expressions for the
ordinary and incomplete moments, quantile and generating
functions, R𝑒́nyi, Shannon and q-entropies, order statistics and
probability weighted moments are derived. We also propose
bivariate generalizations. We constructed a simple type Copula and
intro-duced a useful stochastic property. The maximum likelihood
method is used for estimating the model parameters. The importance
and flexibility of the new family are illustrated by means of two
applications to real data sets. We assess the performance of the
maximum likelihood estimators in terms of biases and mean squared
errors via a simulation study.