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Mathematics & Statistics

Graduate Statistics Courses


STA 5166 — STATISTICAL METHODS I (3)

Statistical analysis of data by means of statistics package programs. Regression, ANOVA, discriminant analysis and analysis of categorical data. Emphasis is on interrelation between statistical theory, numerical methods and analysis of real-life data.

Prerequisite(s): STA 4321 and CGS 3414 or CI

Note: This course is taught in the fall semester.


STA 5326 — MATHEMATICAL STATISTICS I (3)

Sample distribution theory, point & interval estimation, optimality theory, statistical decision theory, and hypothesis testing.

Prerequisite(s): STA 5446

Note: This course is taught in the fall semester.


STA 5446 — PROBABILITY THEORY I (3)

Axioms of probability, random variables in Euclidean spaces, moments and moment generating functions, modes of convergence, limit theory for sums of independent random variables.

Prerequisite(s): MAA 5306 or CI

Note: This course is taught in the fall semester.


STA 5526 — NON-PARAMETRIC STATISTICS (3)

Theory and methods of non-parametric statistics, order statistics, tolerance regions, and their applications.

Prerequisite(s): STA 5326 or CI

Note: This course is taught in the spring semester of even years.


STA 6167 — STASTICAL METHODS II (3)

Design of statistics programs, pivoting and other technology used in stepwise regressions, algorithms in non-linear regression, balanced and unbalanced ANOVA. Iteration methods for numerical solutions of likelihood equations.

Prerequisite(s): STA 5166

Note: This course is taught in the spring semester.


STA 6206 — STOCHASTIC PROCESSES (3)

Poisson processes, renewal theorems, Markov chains on a countable state space, continuous-time Markov processes with a countable state space, birth and death processes, branching processes, introduction to Brownian motion.

Prerequisite(s): STA 5446

Note: This course is taught in the fall semester of odd years.


STA 6208 — LINEAR STATISTICAL MODELS (3)

Distribution theory, estimation, and hypothesis testing for the general linear model. Experimental designs, including randomized block and incomplete block designs. Multiple regression, ANOVA and ANCOVA.

Prerequisite(s): STA 6167 or STA 5326 or CI

Note: This course is taught in the spring semester of even years.


STA 6326 — MATHEMATICAL STATISTICS II (3)

Sequential Analysis: Sequential sampling; sequential unbiassed estimation and testing. Decision Theory and Bayesian analysis. Basic concepts: Utility and Loss, Prior information; Bayesian inference; Empirical Bayes analysis; Bayesian robustness; Mini-Max Analysis; Bayesian sequential analysis.

Prerequisite(s): STA 5326

Note: This course is taught in the spring semester.


STA 6447 — PROBABILITY THEORY II (3)

Characteristic functions, central limit theorem, martingale inequalities and convergence theorems, optional stopping, ergodic theorems and applications.

Prerequisite(s): STA 5446 and MAA 5306 or CI

Note: This course is taught in the spring semester.


STA 6746 — MULTIVARIATE ANALYSIS (3)

Multivariate normal distribution; its properties and inference; matrix random variables; multiple and partial correlation; discriminant analysis, principle components and factor analysis; multivariate ANOVA; analysis of covariance; applications using computers.

Prerequisite(s): STA 5326 or CI

Note: This course is taught in the fall semester of even years.


STA 6876 — TIME SERIES ANALYSIS (3)

Theory and applications of discrete time series models illustrated with forecasting problems. Filtering, forecasting, modeling, and spectral analysis of time series. Control problems. Applications using a computer.

Prerequisite(s): STA 5326 or CI

Note: This course is taught in the spring semester of odd years.