Patricia Cyr Headshot

Patricia Cyr

Lecturer
Department of Industrial and Systems Engineering
Kate Gleason College of Engineering

585-475-4715
Office Location

Patricia Cyr

Lecturer
Department of Industrial and Systems Engineering
Kate Gleason College of Engineering

Education

BS, University of Pittsburgh; MS, Rochester Institute of Technology

Bio

Patricia Cyr holds a B.S. in Chemical Engineering from the University of Pittsburgh and an M.S. in applied and Mathematical Statistics from RIT. She was the Process Engineer for Corning and Mobil Chemical before beginning her career as a consulting statistician for Kodak and Harris. Patricia was the ASQ Shainnin Medal recipient in 2014 and has been an ASQ Certified Quality Engineer since 1993.

585-475-4715

Currently Teaching

ISEE-325
3 Credits
This course covers statistics for use in engineering as well as the primary concepts of experimental design. The first portion of the course will cover: Point estimation; hypothesis testing and confidence intervals; one- and two-sample inference. The remainder of the class will be spent on concepts of design and analysis of experiments. Lectures and assignments will incorporate real-world science and engineering examples, including studies found in the literature.
ISEE-660
3 Credits
An applied approach to statistical quality control utilizing theoretical tools acquired in other math and statistics courses. Heavy emphasis on understanding and applying statistical analysis methods in real-world quality control situations in engineering. Topics include process capability analysis, acceptance sampling, hypothesis testing and control charts. Contemporary topics such as six-sigma are included within the context of the course.
ISEE-560
3 Credits
An applied approach to statistical quality control utilizing theoretical tools acquired in other math and statistics courses. Heavy emphasis on understanding and applying statistical analysis methods in real-world quality control situations in engineering. Topics include process capability analysis, acceptance sampling, hypothesis testing and control charts. Contemporary topics such as six-sigma are included within the context of the course.
ISEE-582
3 Credits
This course presents the philosophy and methods that enable participants to develop quality strategies and drive process improvements. The fundamental elements of Lean Six Sigma are covered along with many problem solving and statistical tools that are valuable in driving process improvements in a broad range of business environments and industries. Successful completion of this course is accompanied by “yellow belt” certification (for A’s and B’s only), and provides a solid foundation for those who also wish to pursue a “green belt.” (Green belt certification requires completion of an approved project which is beyond the scope of this course).
ISEE-682
3 Credits
This course presents the philosophy and methods that enable participants to develop quality strategies and drive process improvements. The fundamental elements of Lean Six Sigma are covered along with many problem solving and statistical tools that are valuable in driving process improvements in a broad range of business environments and industries. Successful completion of this course is accompanied by “yellow belt” certification and provides a solid foundation for those who also wish to pursue a “green belt.” (Green belt certification requires completion of an approved project and exam, both of which are beyond the scope of this course).
ISEE-760
3 Credits
This course presents an in-depth study of the primary concepts of experimental design. Its applied approach uses theoretical tools acquired in other mathematics and statistics courses. Emphasis is placed on the role of replication and randomization in experimentation. Numerous designs and design strategies are reviewed and implications on data analysis are discussed. Topics include: consideration of type 1 and type 2 errors in experimentation, sample size determination, completely randomized designs, randomized complete block designs, blocking and confounding in experiments, Latin square and Graeco Latin square designs, general factorial designs, the 2k factorial design system, the 3k factorial design system, fractional factorial designs, Taguchi experimentation.

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Invited Keynote/Presentation
Cyr, Patricia A. "Using Multivariate Analysis Methods to Manage Change." 2018 ASQRC Quality COnference. American Society for Quality, Rochester Section. Rochester, NY. 26 Sep. 2018. Conference Presentation.