Data entry, sorting and merging, data summarization, graphical display, reports, and statistical inferences using statistical software.

Laws of probability and sample space; discrete and continuous distributions; joint, marginal and conditional densities; moment generating functions; univariate and bivariate normal distribution.

Markov chains; Poisson processes; introductory renewal theory, Brownian motion and stationary processes used in mathematical modelling.

A comprehensive development of statistical analysis that builds upon a knowledge of probability and basic statistics. Topics include sampling distributions, interval and point estimation, the law of large numbers, limiting distributions, testing hypotheses and order statistics.

More advanced development of solutions to problems involving statistics. Topics include experimental design, analysis of variance, analysis of covariance, multiple linear regression, curvilinear regression, and logistic regression.

Intensive study in a specialized area of statistics. Selected topic is based on student interest and faculty expertise.

Integration of knowledge at an advanced level, a review of recent developments and models in theoretical and applied statistics, along with research and oral presentation.