Covers modeling of discrete-event dynamic systems and introduces methods for using these models to solve engineering design and analysis problems.

Rigorous introduction to theory of statistical inference. Estimation and testing. Construction and assessment of estimators and tests. Fundamentals of decision theory, minimax, and Bayes Paradigms.

Courses in special topics of timely interest to the profession, conducted by resident or visiting faculty.

Introduction to probability, probability distributions, point estimation, confidence intervals, hypothesis testing, linear regression, and analysis of variance. Crosslisted with MATH 3770 and CEE 3770. Also, credit not awarded for both ISYE 3770 and MATH 3670.

Discrete event simulation methodology emphasizing the statistical basis for simulation modeling and analysis. Overview of computer languages and simulation design applied to various industrial situations.

An introductory course on statistical thinking, modeling, analysis, and decision making. This course covers a variety of topics including descriptive statistics, point and interval estimation, hypothesis testing, regression analysis, analysis of variance and experimental design, etc.

Topics include conditional probability, density and distribution functions from engineering, expectation, conditional expectation, laws of large numbers, central limit theorem, and introduction to Poisson Processes.

Topics include conditional probability, density and distribution functions from engineering, expectation, conditional expectation, laws of large numbers, central limit theorem, and introduction to Poisson Processes.

Doctoral thesis.