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In this class, students use data and systems knowledge to build models of complex socio-technical systems for improved system design and decision-making.

0

1

English

English [CC]

FREE

Description

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.

Course content

  • Introduction and overview Unlimited
  • 3-door problem Unlimited
  • Analyzing a probability problem Unlimited
  • Broken stick problem Unlimited
  • Pedestrian crossing problem Unlimited
  • Random incidence: A major source of selection bias Unlimited
  • Random incidence and more Unlimited
  • Spatial models Unlimited
  • Markov processes and their application to queueing, part 1 Unlimited
  • Markov processes and their application to queueing, part 2 Unlimited
  • Queueing and transitions: Sampling from distributions, Gauss Unlimited
  • Derived distributions to statistics Unlimited
  • The Queue Inference Engine and the psychology of queueing Unlimited
  • Beyond the physics of queueing Unlimited
  • The Weibull distribution and parameter estimation Unlimited
  • Hypothesis testing Unlimited
  • Descriptive statistics and statistical graphics Unlimited
  • Regression Unlimited
  • Analysis of variance, with discussion of Bayesian and frequentist statistics Unlimited
  • Multiple regression Unlimited
  • Design of experiments, part 1 Unlimited
  • Design of experiments, part 2 Unlimited
  • Design of computer experiments Unlimited

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Instructor

Massachusetts Institute of Technology
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