This course covers multivariate discrete probability distributions, biavriate normal distribution, maximum likelihood estimation, confidence interval, the Dirichlet distribution, Whishartn expectation identities, Hotelling's T2 and distribution of quadratic forms, quintile transformations and moments, Laws of large number, convergence of moments, characteristics functions of standard distributions, error of the Central Limit Theorem, central order statistics, extremes, markov chains, and random walks.

This course covers R, SAS, SPSS, S-Plus, Mathematics, computational statistics packages and other big data statistical computational packages with emphasis on reading, manipulating, summarizing and modeling data and implementations of simulation through random number generating, Monte Carlo method and bootstrapping.

This course covers basic descriptive statistics, basic probability distributions, simple linear regression, point estimation, comparison of data sets and how to use mathematical and statistical software and packages as well as program to conduct analysis and provide visualized representations.