Principles and methodologies of statistical inference; methods of data analysis; point and interval estimation; tests of hypotheses; correlation and regression. Introduces analysis of variance methods.

Focuses on the interactions of humans with tasks, equipment, and the environment as part of the workplace system. Students learn about human capabilities and limitations, applications of human factors/ergonomics principles to system design, methodologies for human-system analysis, and experimental design and data analysis applied to human factors problems.

Basics of design, analysis, and selection of manufacturing facilities and material-handling equipment. Approaches to analyzing manufacturing and material-handling systems. Applications of computers in modern facilities design and material handling, emphasizing material-flow control and storage. Economic justification models for manufacturing and material-handling systems.

Continuation of IE 373, including uncertainty, risk, and the probabilistic approach in operations research. Topics include elementary mathematical models of game theory, decision analysis, stochastic dynamic programming, stochastic processes (including Markov chains and Markov decision processes) and queues (waiting lines).

Familiarizes students with the application of statistical quality problem-solving methodologies used to characterize, leverage, and reduce process variability. This course emphasizes the application of sampling methodologies, sample size determination, hypothesis testing, analysis of variance, correlation, regression, measurement systems analysis, design and analysis of saturated experimental designs, design and analysis response surface experimental designs, and statistical process control.

Decision analysis that extends the domain of decision-making problems from those considered in traditional statistical hypothesis testing scenarios: modeling decisions, which emphasize structuring decision problems using techniques such as influence diagrams and decision trees; modeling uncertainty, which covers subjective probability assessment, use of classical probability models, Bayesian analysis, and value of information; and modeling preferences, which introduces concepts of risk preference, expected utility, and multi-attribute value and utility models.

Familiarizes students with the principles and practice of use- and user-centered design of human-computer interfaces. By the end of the course, students are able to identify, describe, and implement a subset of methods appropriate for requirements gathering, user and task modeling, prototype development, and user test plan development, implementation, and analysis. Additionally, students are able to apply principles in user-centered design to interface design.

Overall view of the professional field, particularly emphasizing the role of the human, and human factors, in safe working systems. Provides information relevant to understanding basic concepts associated with systems safety, such as the legislative environment, management culture, and sources of workplace hazards; understanding the need to consider the human when designing safe, complex systems; and analyzing a work system to predict its hazards and select possible interventions.

Studies special areas of interest to students and instructor. Content varies from section to section and semester to semester.

Analyzes robots and robotic systems: kinematics, coordinate transform, numerical control, off-line programming, robot vision, and simulation of robotic systems.

Students collaborate with faculty research mentors on ongoing research of the faculty member or conduct independent research under the guidance of a faculty member.