Data Analysis and Probability Models

Course #OS3080

Est.imated Completion Time: 3 months


Coming soon...

Included in degrees & certificates

  • 363
  • 379

Learning Outcomes

  • Learn hypothesis testing for contingency tables, ANOVA, and nonparametric tests.
  • Discuss and design experiments for two-factor, three factor and larger. Methods to screen experiments when number of factors are large.
  • Effectively use simple and multiple regression to create models for data.
  • Learn how to effectively work with time series, including use of lagging variables, autoregression techniques and smoothing models.
  • Review basic probability concepts and Bayes’ theorem. Learn about conditioning to compute expectation and probability.
  • Introduce reliability for systems in series and/or parallel. Define failure rate and hazard rate. Fit parametric models to failure data including censored data.
  • Review Poisson and exponential distributions. Define Poisson Processes.
  • Introduce stochastic models. Learn terminology for Markov models, one-step of n-step transition matrices, steady state probabilities and mean first passage time.
Offerings database access
Asset Publisher

Academic Calendar

  •  06 Jun 2023

    Spring quarter pre-graduation awards ceremony

  •  09 Jun 2023

    Spring quarter last day of classes

  •  13 Jun 2023

    Spring quarter final examinations begin