Teaching
Graduate Student Instructor
IOE
265. (Stats 265). Probability and Statistics for Engineers
(Winter
2006)
Prerequisite:
Math 116 and ENG 101. I, II (4 credits)
Graphical Representation of Data; Axioms of Probability; Conditioning,
Bayes
Theorem; Discrete Distributions (Geometric, Binomial, Poisson);
Continuous
Distributions (Normal Exponential, Weibull), Point and Interval
Estimation,
Likelihood Functions, Test of Hypotheses for Means, Variances, and
Proportions
for One and Two Populations.
IOE
310. Introduction to Optimization Methods (Fall 2004, Winter
2005,
Winter 2007)
Prerequisite:
Math 214, IOE 202 and ENG 101. I, II (4 credits)
Introduction to deterministic models with emphasis on linear
programming;
simplex and transportation algorithms, engineering applications,
relevant
software. Introduction to integer, network, and dynamic programming,
critical
path methods.
IOE
316. Introduction to Markov Processes (Fall 2005)
Prerequisite:
IOE 265 and Math 214. I, II (2 credits) (7-week course)
Introduction to discrete Markov Chains and continuous Markov processes,
including transient and limiting behavior. The Poisson/Exponential
process.
Applications to reliability, maintenance, inventory, production, simple
queues
and other engineering problems.
IOE
366. Linear Statistical Models (Fall 2005)
Prerequisite:
IOE 265 and Math 214. I, II (2 credits) (7-week course)
Linear statistical models and their application to engineering data
analysis.
Linear regression and correlation; multiple linear regression, analysis
of
variance, introduction to design of experiments.
IOE
474. Simulation (Winter 2004, Fall 2007)
Prerequisite: IOE 316, IOE 366, IOE 373. I, II (4 credits)
Simulation of complex discrete-event systems with applications in
industrial
and service organizations. Course topics include modeling and
programming
simulations in one or more high-level computer packages such as
ProModel or
GPSS/H; input distribution modeling; generating random numbers;
statistical
analysis of simulation output data. The course will contain a team
simulation
project.
IOE 510.
Linear Programming I (Winter 2008)
Prerequisite:
Math 217, Math 417, or Math 419. I, II, IIIa (3 credits)
Formulation of problems from the private and public sectors using the
mathematical model of linear programming. Development of the simplex
algorithm;
duality theory and economic interpretations. Post-optimality
(sensitivity)
analysis application and interpretations. Introduction to
transportation and
assignment problems; special purpose algorithms and advance
computational
techniques. Students have opportunities to formulate and solve models
developed
from more complex case studies and to use various computer programs.
Sample Course
Evaluations
IOE 474 Fall
2007 (midterm evaluation forms)
IOE 310
Winter 2007 (evaluation forms,
sample student notes)
IOE 366 Fall
2005 (evaluation forms,
sample student notes)