ESD.86
Models, Data and Inference for Socio-Technical Systems
Spring 2007
In this class, students use data and systems knowledge to build models of complex socio-technical systems for improved system design and decision-making. Students will enhance their model-building skills, through review and extension of functions of random variables, Poisson processes, and Markov processes; move from applied probability to statistics via Chi-squared t and f tests, derived as functions of random variables; and review classical statistics, hypothesis tests, regression, correlation and causation, simple data mining techniques, and Bayesian vs. classical statistics. A class project is required.
Readings
- Wu, C. F. Jeff, and Michael Hamada. Experiments: Planning, Analysis, and Parameter Design Optimization . New York, NY: J. Wiley & Sons, 2000. ISBN: 9780471255116.
- Box, G. E. P., and P. Y. T. Liu. “Statistics as a Catalyst to Learning by Scientific Method Part I - An Example.” Journal of Quality Technology 31, no. 1 (1999): 1-15.
- Fisher, Sir Ronald A. “Scientific Thought and the Refinement of Human Reasoning.” Journal of the Operations Research Society of Japan 3 (1960): 1-10.
- Gigerenzer, Gerd. “Mindless Statistics.” Journal of Socio-Economics 33 (2004): 587-606.
- Weibull, Waloddi. “A Statistical Distribution Function of Wide Applicability.” Journal of Applied Mechanics 18, no. 3 (1951): 293-297.
- Zager, L. A., and G. C. Verghese. “Caps and Robbers: What can you Expect?” College Mathematics Journal 38, no. 3: 185-191.
- Belenky, Alexander S., and Richard C. Larson. “Faulty System for Democracy.” Boston Herald , February 10, 2007.
Sources
- Course catalogue ↗Historical OpenCourseWare
- MIT OCW readings ↗
- MIT OCW syllabus ↗
- Historical MIT OpenCourseWare offering. Named reading entries and schedule topics are automatically extracted from the linked source pages.